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Where does biodiversity go from here? A grim business-as-usual forecast and a hopeful portfolio of partial solutions
Ehrlich, Paul R.; Pringle, Robert M.
The threats to the future of biodiversity are many and well known. They include habitat conversion, environmental toxification, climate change, and direct exploitation of wildlife, among others. Moreover, the projected addition of 2.6 billion people by mid-century will almost certainly have a greater environmental impact than that of the last 2.6 billion. Collectively, these trends portend a grim future for biodiversity under a business-as-usual scenario. These threats and their interactions are formidable, but we review seven strategies that, if implemented soundly and scaled up dramatically, would preserve a substantial portion of global biodiversity. These are actions to stabilize the human population and reduce its material consumption, the deployment of endowment funds and other strategies to ensure the efficacy and permanence of conservation areas, steps to make human-dominated landscapes hospitable to biodiversity, measures to account for the economic costs of habitat degradation, the ecological reclamation of degraded lands and repatriation of extirpated species, the education and empowerment of people in the rural tropics, and the fundamental transformation of human attitudes about nature. Like the carbon "stabilization wedges" outlined by Pacala and Socolow [Pacala S, Socolow R (2004) Stabilization wedges: Solving the climate problem for the next 50 years with current technologies. Science 305:968–972] (1), the science and technologies needed to effect this vision already exist. The remaining challenges are largely social, political, and economic. Although academic conservation biology still has an important role to play in developing technical tools and knowledge, success at this juncture hinges more on a massive mobilization of effort to do things that have traditionally been outside the scope of the discipline. PMID:18695214
Colloquium paper: where does biodiversity go from here? A grim business-as-usual forecast and a hopeful portfolio of partial solutions.
Ehrlich, Paul R; Pringle, Robert M
The threats to the future of biodiversity are many and well known. They include habitat conversion, environmental toxification, climate change, and direct exploitation of wildlife, among others. Moreover, the projected addition of 2.6 billion people by mid-century will almost certainly have a greater environmental impact than that of the last 2.6 billion. Collectively, these trends portend a grim future for biodiversity under a business-as-usual scenario. These threats and their interactions are formidable, but we review seven strategies that, if implemented soundly and scaled up dramatically, would preserve a substantial portion of global biodiversity. These are actions to stabilize the human population and reduce its material consumption, the deployment of endowment funds and other strategies to ensure the efficacy and permanence of conservation areas, steps to make human-dominated landscapes hospitable to biodiversity, measures to account for the economic costs of habitat degradation, the ecological reclamation of degraded lands and repatriation of extirpated species, the education and empowerment of people in the rural tropics, and the fundamental transformation of human attitudes about nature. Like the carbon "stabilization wedges" outlined by Pacala and Socolow [Pacala S, Socolow R (2004) Stabilization wedges: Solving the climate problem for the next 50 years with current technologies. Science 305:968-972] (1), the science and technologies needed to effect this vision already exist. The remaining challenges are largely social, political, and economic. Although academic conservation biology still has an important role to play in developing technical tools and knowledge, success at this juncture hinges more on a massive mobilization of effort to do things that have traditionally been outside the scope of the discipline. PMID:18695214
The "Business-As-Usual" growth of global primary energy use and carbon dioxide emissions - historical trends and near-term forecasts
NASA Astrophysics Data System (ADS)
Jarvis, A.; Hewitt, C. N.
We analyse the global primary energy use and total CO2 emissions time series since 1850 and show that their relative growth rates appear to exhibit periodicity with a fundamental timescale of ~60 years and with significant harmonic behaviour. Quantifying the inertia inherent in these dynamics allows forecasting of future "business as usual" energy needs and their associated CO2 emissions. Our best estimates for 2020 are 800 EJ yr-1 for global energy use and 14 Gt yr-1 for global CO2 emissions, with both being above almost all other published forecasts. This suggests the energy and total CO2 emissions landscape in 2020 may be significantly more challenging than currently envisaged.
Geoengineering, Climate Harm, and Business as Usual
NASA Astrophysics Data System (ADS)
Jankunis, F. J.; Peacock, K.
We define geoengineering (GE) as the intentional use of technology to change the planet's climate. Many people believe GE is different in kind rather than degree from any other organized activity in human history. In fact, humans caused changes in the planet's climate long before the industrial age, and all organisms engineer their environments directly or indirectly. The relevant difference between this cumulative and generally inadvertent activity and GE is the presence of intention. Now that science has revealed the extent to which humans can change the climate, however, even the continuance of Business as Usual (BAU) is, in effect, a form of intentional GE, albeit one that will cause significant climate harm, defined as effects such as sea level rise that will impact human well-being. But as with all forms of engineering, the devil is in the details: what forms of GE should be tried first? Some methods, such as large-scale afforestation, are low risk but have long-term payoffs; others, such as aerosol injection into the stratosphere, could help buy time in a warming crisis but have unknown side-effects and little long-term future. Climate change is a world-wide, inter-generational tragedy of the commons. Rational choice theory, the spatial and temporal extension of the problem, poorly fitted moral frameworks, and political maneuvering are all factors that inhibit solutions to the climate tragedy of the commons. The longer that such factors are allowed to dominate decision-making (or the lack thereof) the more likely it is that humanity will be forced to resort to riskier and more drastic forms of GE. We argue that this fact brings an additional measure of urgency to the search for ways to engineer the climate differently so as to avoid climate harm in the most lasting and least risky way.
Future reef decalcification under a business-as-usual CO2 emission scenario
Dove, Sophie G.; Kline, David I.; Pantos, Olga; Angly, Florent E.; Tyson, Gene W.; Hoegh-Guldberg, Ove
Increasing atmospheric partial pressure of CO2 (pCO2) is a major threat to coral reefs, but some argue that the threat is mitigated by factors such as the variability in the response of coral calcification to acidification, differences in bleaching susceptibility, and the potential for rapid adaptation to anthropogenic warming. However the evidence for these mitigating factors tends to involve experimental studies on corals, as opposed to coral reefs, and rarely includes the influence of multiple variables (e.g., temperature and acidification) within regimes that include diurnal and seasonal variability. Here, we demonstrate that the inclusion of all these factors results in the decalcification of patch-reefs under business-as-usual scenarios and reduced, although positive, calcification under reduced-emission scenarios. Primary productivity was found to remain constant across all scenarios, despite significant bleaching and coral mortality under both future scenarios. Daylight calcification decreased and nocturnal decalcification increased sharply from the preindustrial and control conditions to the future scenarios of low (reduced emissions) and high (business-as-usual) increases in pCO2. These changes coincided with deeply negative carbonate budgets, a shift toward smaller carbonate sediments, and an increase in the abundance of sediment microbes under the business-as-usual emission scenario. Experimental coral reefs demonstrated highest net calcification rates and lowest rates of coral mortality under preindustrial conditions, suggesting that reef processes may not have been able to keep pace with the relatively minor environmental changes that have occurred during the last century. Taken together, our results have serious implications for the future of coral reefs under business-as-usual environmental changes projected for the coming decades and century. PMID:24003127
Future reef decalcification under a business-as-usual CO2 emission scenario.
Dove, Sophie G; Kline, David I; Pantos, Olga; Angly, Florent E; Tyson, Gene W; Hoegh-Guldberg, Ove
Increasing atmospheric partial pressure of CO2 (pCO2) is a major threat to coral reefs, but some argue that the threat is mitigated by factors such as the variability in the response of coral calcification to acidification, differences in bleaching susceptibility, and the potential for rapid adaptation to anthropogenic warming. However the evidence for these mitigating factors tends to involve experimental studies on corals, as opposed to coral reefs, and rarely includes the influence of multiple variables (e.g., temperature and acidification) within regimes that include diurnal and seasonal variability. Here, we demonstrate that the inclusion of all these factors results in the decalcification of patch-reefs under business-as-usual scenarios and reduced, although positive, calcification under reduced-emission scenarios. Primary productivity was found to remain constant across all scenarios, despite significant bleaching and coral mortality under both future scenarios. Daylight calcification decreased and nocturnal decalcification increased sharply from the preindustrial and control conditions to the future scenarios of low (reduced emissions) and high (business-as-usual) increases in pCO2. These changes coincided with deeply negative carbonate budgets, a shift toward smaller carbonate sediments, and an increase in the abundance of sediment microbes under the business-as-usual emission scenario. Experimental coral reefs demonstrated highest net calcification rates and lowest rates of coral mortality under preindustrial conditions, suggesting that reef processes may not have been able to keep pace with the relatively minor environmental changes that have occurred during the last century. Taken together, our results have serious implications for the future of coral reefs under business-as-usual environmental changes projected for the coming decades and century. PMID:24003127
Effects of business-as-usual anthropogenic emissions on air quality
NASA Astrophysics Data System (ADS)
Pozzer, A.; Zimmermann, P.; Doering, U. M.; van Aardenne, J.; Tost, H.; Dentener, F.; Janssens-Maenhout, G.; Lelieveld, J.
The atmospheric chemistry general circulation model EMAC has been used to estimate the impact of anthropogenic emission changes on global and regional air quality in recent and future years (2005, 2010, 2025 and 2050). The emission scenario assumes that population and economic growth largely determine energy and food consumption and consequent pollution sources with the current technologies ("business as usual"). This scenario is chosen to show the effects of not implementing legislation to prevent additional climate change and growing air pollution, other than what is in place for the base year 2005, representing a pessimistic (but feasible) future. By comparing with recent observations, it is shown that the model reproduces the main features of regional air pollution distributions though with some imprecisions inherent to the coarse horizontal resolution (~100 km) and simplified bottom-up emission input. To identify possible future hot spots of poor air quality, a multi pollutant index (MPI), suited for global model output, has been applied. It appears that East and South Asia and the Middle East represent such hotspots due to very high pollutant concentrations, although a general increase of MPIs is observed in all populated regions in the Northern Hemisphere. In East Asia a range of pollutant gases and fine particulate matter (PM2.5) is projected to reach very high levels from 2005 onward, while in South Asia air pollution, including ozone, will grow rapidly towards the middle of the century. Around the Arabian Gulf, where natural PM2.5 concentrations are already high (desert dust), ozone levels are expected to increase strongly. The per capita MPI (PCMPI), which combines demographic and pollutants concentrations projections, shows that a rapidly increasing number of people worldwide will experience reduced air quality during the first half of the 21st century. Following the business as usual scenario, it is projected that air quality for the global average
Effects of business-as-usual anthropogenic emissions on air quality
NASA Astrophysics Data System (ADS)
Pozzer, A.; Zimmermann, P.; Doering, U. M.; van Aardenne, J.; Tost, H.; Dentener, F.; Janssens-Maenhout, G.; Lelieveld, J.
The atmospheric chemistry general circulation model EMAC has been used to estimate the impact of anthropogenic emission changes on global and regional air quality in recent and future years (2005, 2010, 2025 and 2050). The emission scenario assumes that population and economic growth largely determine energy and food consumption and consequent pollution sources with the current technologies ("business as usual"). This scenario is chosen to show the effects of not implementing legislation to prevent additional climate change and growing air pollution, other than what is in place for the base year 2005, representing a pessimistic (but plausible) future. By comparing with recent observations, it is shown that the model reproduces the main features of regional air pollution distributions though with some imprecisions inherent to the coarse horizontal resolution (~100 km) and simplified bottom-up emission input. To identify possible future hot spots of poor air quality, a multi pollutant index (MPI), suited for global model output, has been applied. It appears that East and South Asia and the Middle East represent such hotspots due to very high pollutant concentrations, while a general increase of MPIs is observed in all populated regions in the Northern Hemisphere. In East Asia a range of pollutant gases and fine particulate matter (PM2.5) is projected to reach very high levels from 2005 onward, while in South Asia air pollution, including ozone, will grow rapidly towards the middle of the century. Around the Persian Gulf, where natural PM2.5 concentrations are already high (desert dust), ozone levels are expected to increase strongly. The population weighted MPI (PW-MPI), which combines demographic and pollutant concentration projections, shows that a rapidly increasing number of people worldwide will experience reduced air quality during the first half of the 21st century. Following this business as usual scenario, it is projected that air quality for the global
Vietnam's Forest Transition in Retrospect: Demonstrating Weaknesses in Business-as-Usual Scenarios for REDD+
NASA Astrophysics Data System (ADS)
Ankersen, Jeppe; Grogan, Kenneth; Mertz, Ole; Fensholt, Rasmus; Castella, Jean-Christophe; Lestrelin, Guillaume; Nguyen, Dinh Tien; Danielsen, Finn; Brofeldt, Søren; Rasmussen, Kjeld
One of the prerequisites of the REDD+ mechanism is to effectively predict business-as-usual (BAU) scenarios for change in forest cover. This would enable estimation of how much carbon emission a project could potentially prevent and thus how much carbon credit should be rewarded. However, different factors like forest degradation and the lack of linearity in forest cover transitions challenge the accuracy of such scenarios. Here we predict and validate such BAU scenarios retrospectively based on forest cover changes at village and district level in North Central Vietnam. With the government's efforts to increase the forest cover, land use policies led to gradual abandonment of shifting cultivation since the 1990s. We analyzed Landsat images from 1973, 1989, 1998, 2000, and 2011 and found that the policies in the areas studied did lead to increased forest cover after a long period of decline, but that this increase could mainly be attributed to an increase in open forest and shrub areas. We compared Landsat classifications with participatory maps of land cover/use in 1998 and 2012 that indicated more forest degradation than was captured by the Landsat analysis. The BAU scenarios were heavily dependent on which years were chosen for the reference period. This suggests that hypothetical REDD+ activities in the past, when based on the remote sensing data available at that time, would have been unable to correctly estimate changes in carbon stocks and thus produce relevant BAU scenarios.
Vietnam's forest transition in retrospect: demonstrating weaknesses in business-as-usual scenarios for REDD.
Ankersen, Jeppe; Grogan, Kenneth; Mertz, Ole; Fensholt, Rasmus; Castella, Jean-Christophe; Lestrelin, Guillaume; Nguyen, Dinh Tien; Danielsen, Finn; Brofeldt, Søren; Rasmussen, Kjeld
One of the prerequisites of the REDD+ mechanism is to effectively predict business-as-usual (BAU) scenarios for change in forest cover. This would enable estimation of how much carbon emission a project could potentially prevent and thus how much carbon credit should be rewarded. However, different factors like forest degradation and the lack of linearity in forest cover transitions challenge the accuracy of such scenarios. Here we predict and validate such BAU scenarios retrospectively based on forest cover changes at village and district level in North Central Vietnam. With the government's efforts to increase the forest cover, land use policies led to gradual abandonment of shifting cultivation since the 1990s. We analyzed Landsat images from 1973, 1989, 1998, 2000, and 2011 and found that the policies in the areas studied did lead to increased forest cover after a long period of decline, but that this increase could mainly be attributed to an increase in open forest and shrub areas. We compared Landsat classifications with participatory maps of land cover/use in 1998 and 2012 that indicated more forest degradation than was captured by the Landsat analysis. The BAU scenarios were heavily dependent on which years were chosen for the reference period. This suggests that hypothetical REDD+ activities in the past, when based on the remote sensing data available at that time, would have been unable to correctly estimate changes in carbon stocks and thus produce relevant BAU scenarios. PMID:25588807
ERIC Educational Resources Information Center
Sch Libr J, 1970
Despite the conference theme, the youth division seldom rose above the trite and the obvious" in discussing their resources for human understanding. A report on the Children's Service Division (CSD), Young Adults Service Division (YASD) and other meetings. (Author)
ERIC Educational Resources Information Center
Cohen, Jeremy
Even in an industry where rapid change is the status quo, it takes a special kind of company to handle the training challenges posed by a major corporate acquisition and massive product rollout. No one has ever accused Verizon of thinking small-scale when it comes to training initiatives, but over the last year, the telecommunications giant…
Changes in snowmelt runoff timing in western North America under a 'business as usual' climate change scenario
Stewart, I.T.; Cayan, D.R.; Dettinger, M.D.
Spring snowmelt is the most important contribution of many rivers in western North America. If climate changes, this contribution may change. A shift in the timing of springtime snowmelt towards earlier in the year already is observed during 1948-2000 in many western rivers. Streamflow timing changes for the 1995-2099 period are projected using regression relations between observed streamflow-timing responses in each river, measured by the temporal centroid of streamflow (CT) each year, and local temperature (TI) and precipitation (PI) indices. Under 21st century warming trends predicted by the Parallel Climate Model (PCM) under business-as-usual greenhouse-gas emissions, streamflow timing trends across much of western North America suggest even earlier springtime snowmelt than observed to date. Projected CT changes are consistent with observed rates and directions of change during the past five decades, and are strongest in the Pacific Northwest, Sierra Nevada, and Rocky Mountains, where many rivers eventually run 30-40 days earlier. The modest PI changes projected by PCM yield minimal CT changes. The responses of CT to the simultaneous effects of projected TI and PI trends are dominated by the TI changes. Regression-based CT projections agree with those from physically-based simulations of rivers in the Pacific Northwest and Sierra Nevada.
Regional Assessment of Urban Impacts on Landcover and Open Space Finds a Smart Urban Growth Policy Performs Little Better than Business as Usual
Thorne, James H.; Santos, Maria J.; Bjorkman, Jacquelyn H.
Assessment of landscape change is critical for attainment of regional sustainability goals. Urban growth assessments are needed because over half the global population now lives in cities, which impact biodiversity, ecosystem structure and ecological processes. Open space protection is needed to preserve these attributes, and provide the resources humans need. The San Francisco Bay Area, California, is challenged to accommodate a population increase of 3.07 million while maintaining the region's ecosystems and biodiversity. Our analysis of 9275 km2 in the Bay Area links historic trends for three measures: urban growth, protected open space, and landcover types over the last 70 years to future 2050 projections of urban growth and open space. Protected open space totaled 348 km2 (3.7% of the area) in 1940, and expanded to 2221 km2 (20.2%) currently. An additional 1038 km2 of protected open space is targeted (35.1%). Urban area historically increased from 396.5 km2 to 2239 km2 (24.1% of the area). Urban growth during this time mostly occurred at the expense of agricultural landscapes (62.9%) rather than natural vegetation. Smart Growth development has been advanced as a preferred alternative in many planning circles, but we found that it conserved only marginally more open space than Business-as-usual when using an urban growth model to portray policies for future urban growth. Scenarios to 2050 suggest urban development on non-urban lands of 1091, 956, or 179 km2, under Business-as-usual, Smart Growth and Infill policy growth scenarios, respectively. The Smart Growth policy converts 88% of natural lands and agriculture used by Business-as-usual, while Infill used only 40% of those lands. Given the historic rate of urban growth, 0.25%/year, and limited space available, the Infill scenario is recommended. While the data may differ, the use of an historic and future framework to track these three variables can be easily applied to other metropolitan areas. PMID
Regional assessment of urban impacts on landcover and open space finds a smart urban growth policy performs little better than business as usual.
Thorne, James H; Santos, Maria J; Bjorkman, Jacquelyn H
Assessment of landscape change is critical for attainment of regional sustainability goals. Urban growth assessments are needed because over half the global population now lives in cities, which impact biodiversity, ecosystem structure and ecological processes. Open space protection is needed to preserve these attributes, and provide the resources humans need. The San Francisco Bay Area, California, is challenged to accommodate a population increase of 3.07 million while maintaining the region's ecosystems and biodiversity. Our analysis of 9275 km² in the Bay Area links historic trends for three measures: urban growth, protected open space, and landcover types over the last 70 years to future 2050 projections of urban growth and open space. Protected open space totaled 348 km² (3.7% of the area) in 1940, and expanded to 2221 km² (20.2%) currently. An additional 1038 km² of protected open space is targeted (35.1%). Urban area historically increased from 396.5 km² to 2239 km² (24.1% of the area). Urban growth during this time mostly occurred at the expense of agricultural landscapes (62.9%) rather than natural vegetation. Smart Growth development has been advanced as a preferred alternative in many planning circles, but we found that it conserved only marginally more open space than Business-as-usual when using an urban growth model to portray policies for future urban growth. Scenarios to 2050 suggest urban development on non-urban lands of 1091, 956, or 179 km², under Business-as-usual, Smart Growth and Infill policy growth scenarios, respectively. The Smart Growth policy converts 88% of natural lands and agriculture used by Business-as-usual, while Infill used only 40% of those lands. Given the historic rate of urban growth, 0.25%/year, and limited space available, the Infill scenario is recommended. While the data may differ, the use of an historic and future framework to track these three variables can be easily applied to other metropolitan areas. PMID
Effects of "Reduced" and "Business-As-Usual" CO2 Emission Scenarios on the Algal Territories of the Damselfish Pomacentrus wardi (Pomacentridae).
Bender, Dorothea; Champ, Connor Michael; Kline, David; Diaz-Pulido, Guillermo; Dove, Sophie
Turf algae are a very important component of coral reefs, featuring high growth and turnover rates, whilst covering large areas of substrate. As food for many organisms, turf algae have an important role in the ecosystem. Farming damselfish can modify the species composition and productivity of such algal assemblages, while defending them against intruders. Like all organisms however, turf algae and damselfishes have the potential to be affected by future changes in seawater (SW) temperature and pCO2. In this study, algal assemblages, in the presence and absence of farming Pomacentrus wardi were exposed to two combinations of SW temperature and pCO2 levels projected for the austral spring of 2100 (the B1 "reduced" and the A1FI "business-as-usual" CO2 emission scenarios) at Heron Island (GBR, Australia). These assemblages were dominated by the presence of red algae and non-epiphytic cyanobacteria, i.e. cyanobacteria that grow attached to the substrate rather than on filamentous algae. The endpoint algal composition was mostly controlled by the presence/absence of farming damselfish, despite a large variability found between the algal assemblages of individual fish. Different scenarios appeared to be responsible for a mild, species specific change in community composition, observable in some brown and green algae, but only in the absence of farming fish. Farming fish appeared unaffected by the conditions to which they were exposed. Algal biomass reductions were found under "reduced" CO2 emission, but not "business-as-usual" scenarios. This suggests that action taken to limit CO2 emissions may, if the majority of algae behave similarly across all seasons, reduce the potential for phase shifts that lead to algal dominated communities. At the same time the availability of food resources to damselfish and other herbivores would be smaller under "reduced" emission scenarios. PMID:26121163
Future changes in climate, ocean circulation, ecosystems, and biogeochemical cycling simulated for a business-as-usual CO2 emission scenario until year 4000 AD
NASA Astrophysics Data System (ADS)
Schmittner, Andreas; Oschlies, Andreas; Matthews, H. Damon; Galbraith, Eric D.
A new model of global climate, ocean circulation, ecosystems, and biogeochemical cycling, including a fully coupled carbon cycle, is presented and evaluated. The model is consistent with multiple observational data sets from the past 50 years as well as with the observed warming of global surface air and sea temperatures during the last 150 years. It is applied to a simulation of the coming two millennia following a business-as-usual scenario of anthropogenic CO2 emissions (SRES A2 until year 2100 and subsequent linear decrease to zero until year 2300, corresponding to a total release of 5100 GtC). Atmospheric CO2 increases to a peak of more than 2000 ppmv near year 2300 (that is an airborne fraction of 72% of the emissions) followed by a gradual decline to ˜1700 ppmv at year 4000 (airborne fraction of 56%). Forty-four percent of the additional atmospheric CO2 at year 4000 is due to positive carbon cycle-climate feedbacks. Global surface air warms by ˜10°C, sea ice melts back to 10% of its current area, and the circulation of the abyssal ocean collapses. Subsurface oxygen concentrations decrease, tripling the volume of suboxic water and quadrupling the global water column denitrification. We estimate 60 ppb increase in atmospheric N2O concentrations owing to doubling of its oceanic production, leading to a weak positive feedback and contributing about 0.24°C warming at year 4000. Global ocean primary production almost doubles by year 4000. Planktonic biomass increases at high latitudes and in the subtropics whereas it decreases at midlatitudes and in the tropics. In our model, which does not account for possible direct impacts of acidification on ocean biology, production of calcium carbonate in the surface ocean doubles, further increasing surface ocean and atmospheric pCO2. This represents a new positive feedback mechanism and leads to a strengthening of the positive interaction between climate change and the carbon cycle on a multicentennial to millennial
Teaching across Borders: Business as Usual?
ERIC Educational Resources Information Center
Allen, Bobbe McGhie
The quest to comprehend how cultural differences can impact learning is one of those intriguing challenges that continue to beguile some scholars and educational leaders even at a time that is characterized as globalized. This dissertation is a qualitative case study about teaching to culturally diverse populations and is primarily based on the…
The Escalation of Business as Usual
ERIC Educational Resources Information Center
Tuchman, Gaye
An academic plan is a business plan disguised in the regalia donned for significant public ceremonies--black cap and gown, colorful hood, and, of course, gold tassel. Several years ago, the University of Connecticut started to plan for the economic disaster that was at the time so obviously in the future of higher education institutions. A formal…
Antibiotic research and development: business as usual?
Harbarth, S; Theuretzbacher, U; Hackett, J
The global burden of antibiotic resistance is tremendous and, without new anti-infective strategies, will continue to increase in the coming decades. Despite the growing need for new antibiotics, few pharmaceutical companies today retain active antibacterial drug discovery programmes. One reason is that it is scientifically challenging to discover new antibiotics that are active against the antibiotic-resistant bacteria of current clinical concern. However, the main hurdle is diminishing economic incentives. Increased global calls to minimize the overuse of antibiotics, the cost of meeting regulatory requirements and the low prices of currently marketed antibiotics are strong deterrents to antibacterial drug development programmes. New economic models that create incentives for the discovery of new antibiotics and yet reconcile these incentives with responsible antibiotic use are long overdue. DRIVE-AB is a €9.4 million public-private consortium, funded by the EU Innovative Medicines Initiative, that aims to define a standard for the responsible use of antibiotics and to develop, test and recommend new economic models to incentivize investment in producing new anti-infective agents. PMID:25673635
The Holocaust and Business as Usual: Congressional Source Materials.
ERIC Educational Resources Information Center
Rosenberg, Daniel
Presents resources for Holocaust reference from proceedings and reports of the Subcommittee on War Mobilization of the Committee on Military Affairs and the Special Committee Investigating the National Defense Program that investigated American and other Western corporate ties with Germany are located in leading libraries. Careful analysis by…
Indicators for European Union Policies. Business as Usual?
ERIC Educational Resources Information Center
Saltelli, Andrea; D'Hombres, Beatrice; Jesinghaus, Jochen; Manca, Anna Rita; Mascherini, Massimiliano; Nardo, Michela; Saisana, Michaela
This paper looks at the role of "statistics-based knowledge" in the making of EU policy. We highlight "shortcomings" in the use of statistical indicators made in the course of the Lisbon strategy, ended in 2010. In our opinion the shortcomings are: (i) The paradox of the "coexistence" within the same European Commission of "two holistic…
Business as Usual: Sex Stereotyping in Business Education.
ERIC Educational Resources Information Center
Project on Sex Stereotyping in Education, Red Bank, NJ.
The module described in this document is part of a series of instructional modules on sex-role stereotyping in education. This document (including all but the cassette tape) is the module that explores the myths and stereotypes that have limited women in the world of work. The material provides suggestions for helping students expand occupational…
Business as Usual: Amazon.com and the Academic Library
ERIC Educational Resources Information Center
Van Ullen, Mary K.; Germain, Carol Anne
In 1999, Steve Coffman proposed that libraries form a single interlibrary loan based entity patterned after Amazon.com. This study examined the suitability of Amazon.com's Web interface and record enhancements for academic libraries. Amazon.com could not deliver circulating monographs in the University at Albany Libraries' collection quickly…
Business as Usual? Not for These Middle-Grades Students
ERIC Educational Resources Information Center
Crawford, Heather; Wiest, Lynda
A perpetual dilemma of schooling is how to help students develop skills needed for everyday life, including the work world. Quantitative literacy, also called numeracy, involves an ability to apply essential mathematics skills to authentic or near-authentic tasks. Carefully planned classroom activities can help students develop these important…
The Learning Outcomes Project: Not Business as Usual
ERIC Educational Resources Information Center
Heiland, Linda; Switzer-Kemper, Cathy
Central Arizona College successfully defined student learning outcomes and is building a culture of evidence to support the Learning Paradigm. Recent data indicate great strides in the improvement of student learning. Qualitative research produced meaningful comparisons of leadership and faculty perceptions of the process of developing student…
Working With Suicidal Clients: "Not" Business as Usual
ERIC Educational Resources Information Center
Ellis, Thomas E.; Goldston, David B.
In this introduction to a special series of articles on working with suicidal clients, we note that much of the recent growth in theory and research pertaining to suicidal individuals has been contributed by cognitive-behavioral theorists and researchers. This work has established that suicidal people manifest important cognitive vulnerabilities…
Establishing Wraparound Fidelity: Not Business as Usual. Symposium.
ERIC Educational Resources Information Center
Malysiak, Rosalyn; Duchnowski, Albert J.; Dollard, Norin; Slewczkowski, Robert; Black, Marcia; Greeson, Michael
Three summaries of papers presented in a symposium examine issues in the wraparound model of providing case management and mental health services to children and adolescents with emotional/behavioral disorders. The papers describe 30 months of participatory program evaluation and simultaneous program development between the University of South…
Business as usual: heroin distribution in the United States.
McBride, R B
This article criticizes the predominant analysis of heroin use as a social aberration and argues instead that the normal structure and functioning of U.S. capitalism generate both the market for the drug and the industry which supplies it. The structure of the distribution industry is much like those for comparable legal goods, but with distinctive features which provide reduced risk for dealers and long-term stability for the industry as a whole. The expansionary dynamic of the industry and the key role of syndicates in it are analyzed. The heroin industry is deeply integrated into the economy, and far-reaching social and economic change will be necessary if heroin use is to be significantly reduced. PMID:6500783
Business as Usual? It's Just Not an Option
ERIC Educational Resources Information Center
Blewitt, John
There is no doubt that in order to address the serious challenges arising from anthropogenic--or human-produced--climate change, Britain, along with the rest of the world, needs to adopt policies and develop skills that will create a low-carbon economy with a highly effective use of renewable and natural resources. People need to create the…
Resume Preferences: Is It Really "Business as Usual"?
ERIC Educational Resources Information Center
Ross, Craig M.; Young, Sarah J.
This study examines the resume preferences of 523 recreation and leisure service professionals who interview and hire entry-level recreation professionals. Findings from this study support the fact that different occupations and disciplines require different approaches on how resume content information is presented. In the recreation and leisure…
Insurance benefits under the ADA: Discrimination or business as usual?
McFadden, M.E.
In December 1987, John McGann discovered he had AIDS. In July 1988, his employer altered his health insurance policy by reducing lifetime coverage for AIDS to $5,000, while maintaining the million-dollar limit for all other health conditions. The United States Court of Appeals for the Fifth Circuit upheld the employer`s right to make that change. The Supreme Court denied certiori. Public outcry was immediate and voluminous. The Solicitor General argued that the new Americans with Disabilities Act would save future John McGanns from the same treatment, but the validity of this optimistic prediction is yet to be determined. The Americans with Disabilities Act of 1990 (ADA) is landmark legislation that bars discrimination against the disabled in all aspects of employment, public services, and accommodations. The Act broadly defines disability to include illnesses such as AIDS and cancer, as well as limitations on mobility, vision, and hearing. The ADA indisputably creates a private cause of action for discrimination on the basis of disability. However, depending on the standard of review chosen by the federal courts, this cause of action may or may not provide much protection to those claiming discrimination on the basis of disability in employee benefits and insurance. This article discusses the ADA`s coverage of insurance and benefits in light of the possible standards courts might use to evaluate actions of parties in suits alleging discrimination in these areas and applies those standards of review to the facts of the McGann case. 146 refs.
Global/Regional Integrated Model System (GRIMs): Double Fourier Series (DFS) Dynamical Core
NASA Astrophysics Data System (ADS)
Koo, M.; Hong, S.
A multi-scale atmospheric/oceanic model system with unified physics, the Global/Regional Integrated Model system (GRIMs) has been created for use in numerical weather prediction, seasonal simulations, and climate research projects, from global to regional scales. It includes not only the model code, but also the test cases and scripts. The model system is developed and practiced by taking advantage of both operational and research applications. We outlines the history of GRIMs, its current applications, and plans for future development, providing a summary useful to present and future users. In addition to the traditional spherical harmonics (SPH) dynamical core, a new spectral method with a double Fourier series (DFS) is available in the GRIMs (Table 1). The new DFS dynamical core with full physics is evaluated against the SPH dynamical core in terms of short-range forecast capability for a heavy rainfall event and seasonal simulation framework. Comparison of the two dynamical cores demonstrates that the new DFS dynamical core exhibits performance comparable to the SPH in terms of simulated climatology accuracy and the forecast of a heavy rainfall event. Most importantly, the DFS algorithm guarantees improved computational efficiency in the cluster computer as the model resolution increases, which is consistent with theoretical values computed from the dry primitive equation model framework of Cheong (Fig. 1). The current study shows that, at higher resolutions, the DFS approach can be a competitive dynamical core because the DFS algorithm provides the advantages of both the spectral method for high numerical accuracy and the grid-point method for high performance computing in the aspect of computational cost. GRIMs dynamical cores
Grim19 Attenuates DSS Induced Colitis in an Animal Model
Kim, Jae-kyung; Lee, Seung Hoon; Lee, Seon-Young; Kim, Eun-Kyung; Kwon, Jeong-Eun; Seo, Hyeon-Beom; Lee, Han Hee; Lee, Bo-In; Park, Sung-Hwan; Cho, Mi-La
DSS induced colitis is a chronic inflammatory disease characterized by inflammation in the gastrointestinal tract, which destabilizes the gut and induces an uncontrolled immune response. Although DSS induced colitis is generally thought to develop as a result of an abnormally active intestinal immune system, its pathogenesis remains unclear. Gene associated with retinoid interferon induced mortality (Grim) 19 is an endogenous specific inhibitor of STAT3, which regulates the expression of proinflammatory cytokines. In this study, we investigated the influence of GRIM19 in a DSS induced colitis mouse model. We hypothesized that Grim19 would ameliorate DSS induced colitis by altering STAT3 activity and intestinal inflammation. Grim19 ameliorated DSS induced colitis severity and protected intestinal tissue. The expression of STAT3 and proinflammatory cytokines such as IL-1β and TNF-α in colon and lymph nodes was decreased significantly by Grim19. Moreover, DSS induced colitis progression in a Grim19 transgenic mouse line was inhibited in association with a reduction in STAT3 and IL-17 expression. These results suggest that Grim19 attenuates DSS induced colitis by suppressing the excessive inflammatory response mediated by STAT3 activation. PMID:27258062
Cytokine-induced tumor suppressors: a GRIM story
Kalvakolanu, Dhan V; Nallar, Shreeram C; Kalakonda, Sudhakar
Cytokines belonging to the IFN family are potent growth suppressors. In a number of clinical and preclinical studies, vitamin A and its derivatives like retinoic acid (RA) have been shown to exert synergistic growth-suppressive effects on several tumor cells. We have employed a genome-wide expression-knockout approach to identify the genes critical for IFN/RA-induced growth suppression. A number of novel Genes associated with Retinoid-Interferon-induced Mortality (GRIM) were isolated. In this review, we will describe the molecular mechanisms of actions of one GRIM-19 which participates in multiple pathways for exerting growth control and/or cell death. This protein is emerging as a new tumor suppressor. In addition, GRIM-19 appears to participate in innate immune responses as its activity is modulated by several viruses and bacteria. Thus, GRIMs seem to couple with multiple biological responses by acting at critical nodes. PMID:20382543
Advances in fracture algorithm development in GRIM
NASA Astrophysics Data System (ADS)
Cullis, I.; Church, P.; Greenwood, P.; Huntington-Thresher, W.; Reynolds, M.
The numerical treatment of fracture processes has long been a major challenge in any hydrocode, but has been particularly acute in Eulerian Hydrocodes. This is due to the difficulties in establishing a consistent process for treating failure and the post failure treatment, which is complicated by advection, mixed cell and interface issues, particularly post failure. This alone increase the complexity of incorporating and validating a failure model compared to a Lagrange hydrocode, where the numerical treatment is much simpler. This paper outlines recent significant progress in the incorporation of fracture models in GRIM and the advection of damage across cell boundaries within the mesh. This has allowed a much more robust treatment of fracture in an Eulerian frame of reference and has greatly expanded the scope of tractable dynamic fracture scenarios. The progress has been possible due to a careful integration of the fracture algorithm within the numerical integration scheme to maintain a consistent representation of the physics. The paper describes various applications, which demonstrate the robustness and efficiency of the scheme and highlight some of the future challenges.
Updates in the Global/Regional Integrated Model system (GRIMs)-Double Fourier Series (DFS) Dynamical Core
NASA Astrophysics Data System (ADS)
Koo, M. S.; Park, H.; Park, S. H.; Hong, S. Y.
The Global/Regional Integrated Model system (GRIMs)-double Fourier series (DFS) spectral dynamical core has been developed to overcome the limitation of traditional spectral model using spherical harmonics in terms of computational cost at very high resolution. Recently, the GRIMs-DFS dynamical core was updated in two respects: (1) better scalability on high-performance computing platform; and (2) reduction of numerical time-stepping error. To improve the parallel efficiency, the archived wave domain was designed not to be sliced in the meridional direction, but to be decomposed in the horizontal and vertical directions. Although the computational cost slightly increased due to the requirement of temporary work array, the revised DFS dynamical core yielded higher scalability in terms of the wall-clock-time than the original one. In addition, its efficiency gain became greater with the increase of horizontal resolution when the number of processors is increased. The Robert-Asselin-Williams (RAW) time filter has been proposed as a simple improvement to the widely used Robert-Asselin filter, in order to reduce time-stepping errors in semi-implicit leapfrog integration. This new approach was implemented into the GRIMs-DFS dynamical core and its impact was quantitatively evaluated on medium-range forecast and seasonal ensemble prediction frameworks. Preliminary results showed that the RAW time-filter properly reduced spurious light rainfalls that might be produced from unphysical computational mode generated by leap-frog time stepping. Further details will be presented in the conference.
Overexpression of GRIM-19, a mitochondrial respiratory chain complex I protein, suppresses hepatocellular carcinoma growth
Kong, Dexia; Zhao, Lijing; Du, Yanwei; He, Ping; Zou, Yabin; Yang, Luoluo; Sun, Liankun; Wang, Hebin; Xu, Deqi; Meng, Xiangwei; Sun, Xun
GRIM-19 has been demonstrated as an important regulator for the normal tissue development. Recently, more evidences regarded GRIM-19 as the new tumor suppressor. However, the possible mechanisms underlying GRIM-19 suppressing cancer growth are unclear. In the present study, Paired hepatocellular carcinoma (HCC) and adjacent non-tumor liver tissues were obtained from 54 patients who underwent primary surgical HCC tissue resection. GRIM-19 protein expression in HCC tissues was performed by immunohistochemistry. Cells were transfected by lentiviruses plasmid expressing GRIM-19. RT-PCR and Western blot analyses were performed to confirm the expression of GRIM-19 mRNA or protein. Cell proliferation was assessed by MTT and FCM analyses. Mitochondrial membrane potential and apoptosis were respectively determined by using fluorescence microscopy and FCM analyses. AKT1, pAKT1, cyclinD1, CDK4, PCNA, Bax, Bcl-2, cleaved caspase-9, cleaved caspase-3, and cytochrome C were detected by Western blot and immunofluorescence. GRIM-19 protein expression was markedly lower in HCC than in paired adjacent non-tumor liver tissues. GRIM-19 overexpression in HCC cells significantly induced cell cycle arrest and enhanced apoptosis. We also found that AKT1 expression and phosphorylation were regulated by the expression of GRIM-19. Collectively, our study demonstrated that GRIM-19 overexpression suppressed HCC growth and downregulated AKT1 expression, suggesting that GRIM-19 might play a crucial role in hepatocarcinogenesis through negatively regulating the PI3K/AKT signaling pathway. PMID:25550785
It's Not Business as Usual: New and Emerging Career in Marketing, Finance, and Management
ERIC Educational Resources Information Center
Miller, April J.
There have been many changes in the field of business as a result of technological advancements, government regulations, and shifts in focus. These new career opportunities have arisen as a result: social media marketers, financial examiners, and project managers. In this article, the author discusses these new and emerging career opportunities in…
The UN Decade of Education for Sustainable Development: Business as Usual in the End
ERIC Educational Resources Information Center
Huckle, John; Wals, Arjen E. J.
An analysis of the literature supporting the UN Decade of Education for Sustainable Development and a sample of its key products suggests that it failed to acknowledge or challenge neoliberalism as a hegemonic force blocking transitions towards genuine sustainability. The authors argue that the rationale for the Decade was idealistic and that…
Business as Usual: Exploring Private Sector Participation in American Public Schools.
ERIC Educational Resources Information Center
Shakeshaft, Charol; Trachtman, Roberta
Although there is widespread publicity about the involvement of businesses with schools, and as President Reagan as well as authors of reform reports continue to call upon the private sector to help education, it is unclear to what extent such relationships exist and what they are accomplishing. A 10-page, 55-question survey was mailed to the…
Business as Usual: The Use of English in the Professional World in Hong Kong
ERIC Educational Resources Information Center
Evans, Stephen
This article examines the role of written and spoken English vis-a-vis written Chinese, Cantonese and Putonghua in the four key service industries that have driven Hong Kong's economy in the past decade. The study forms part of a long-standing and continuing investigation into the impact of Hong Kong's transition from British colony to Chinese…
"No-Business-As-Usual German": A Critical Pedagogy of Business German
ERIC Educational Resources Information Center
Robinson, Benjamin
In this article, the author describes his "Business German" course. His course sought to narrate a dialectic of agency and institution. He started by asserting a distinction between the intending subject--with its plural desires, interests, and identifications--and the world it acts in, through and upon. This distinction between acting subject and…
Business as Usual? A Review of Continuing Professional Education and Adult Learning
ERIC Educational Resources Information Center
Wittnebel, Leo
The commodification of education in all forms has created a lucrative trade, particularly within the realm of continuing professional education. Mandated across a wide spectrum of industries, and particularly salient in healthcare due to rapid advances in medicine and technology, professional education is said to be the vehicle that keeps…
Business as Usual: Exploring Private Sector Participation in American Public Schools.
ERIC Educational Resources Information Center
Shakeshaft, Charol; Trachtman, Roberta
Chief executives of corporations were surveyed to see why they participated in school-business collaborations. The survey focused on: (1) what type of corporation was likely to participate; (2) types of partnership; (4) benefits for education; (5) beliefs about corporate involvement; (6) education and corporate philanthropy; and (7) the impact of…
Student Cheating: As Serious an Academic Integrity Problem as Faculty-Administration Business as Usual?
ERIC Educational Resources Information Center
Puka, Bill
Most faculty and administrators rate academic dishonesty a high crime, fatal to education. What cheating shows that merits strong opposition is a student's pride in deceptively "getting over" on professors and "the system," even where both are recognized as fair. This affection for injustice and casual disregard for honest dealings must be trained…
GRIM-19 Restores Cervical Cancer Cell Senescence by Repressing hTERT Transcription.
Zhou, Ying; Xu, Fei; Tao, Feng; Feng, Dingqing; Ling, Bin; Qian, Lili; Yang, Xia; Wang, Qingyuan; Wang, Huiyan; Zhao, Weidong; Cheng, Yong; Shan, Ge; Kalvakolanu, Dhan V; Xiao, Weihua
High telomerase activity promotes tumor growth by stabilizing damaged chromosomes and their mitotic replication. Overactivation of telomerase activity has been reported in cervical cancer, a malignancy caused by high-risk human papillomaviruses (HR-HPVs). The HR-HPV E6 can activate hTERT promoter by interacting with E6AP or other binding proteins and by stabilizing the interaction between hTERT and E6AP. GRIM-19 is a novel tumor suppressor that affects multiple targets in a cell to regulate growth. We have previously reported the interaction of GRIM-19 with 18E6 and E6AP to disrupt the E6/E6AP complex and increase the autoubiquitination of E6AP. In this study, we characterized the interaction of GRIM-19 with 16E6 (an oncoprotein produced by HPV16) and identified the binding sites that mediate this interaction. We also found that GRIM-19 expression in cervical cancer cells could inhibit telomerase activity by inhibiting the transactivation of the hTERT promoter by E6, thereby promoting cervical cancer cell senescence. Moreover, we identified a negative correlation between GRIM-19 and hTERT expression in cervical cancer tissues. Suppression of GRIM-19 and induction of hTERT levels were associated with lymph node metastasis, advanced clinical stage, and poor prognosis. This study identified another important novel antitumor molecular link associated with GRIM-19 in the tumorigenesis. PMID:27142689
Monoallelic loss of tumor suppressor GRIM-19 promotes tumorigenesis in mice
Kalakonda, Sudhakar; Nallar, Shreeram C.; Jaber, Sausan; Keay, Susan K.; Rorke, Ellen; Munivenkatappa, Raghava; Lindner, Daniel J.; Fiskum, Gary M.; Kalvakolanu, Dhananjaya V.
Gene-associated with retinoid-interferon induced mortality-19 (GRIM-19), a STAT3-inhibitory protein, was isolated as a growth-suppressive gene product using a genome-wide expression knockdown screen. We and others have shown a loss of expression and occurrence of mutations in the GRIM-19 gene in a variety of primary human cancers, indicating its potential role as tumor suppressor. To help investigate its role in tumor development in vivo, we generated a genetically modified mouse in which Grim-19 can be conditionally inactivated. Deletion of Grim-19 in the skin significantly increased the susceptibility of mice to chemical carcinogenesis, resulting in development of squamous cell carcinomas. These tumors had high Stat3 activity and an increased expression of Stat3-responsive genes. Loss of Grim-19 also caused mitochondrial electron transport dysfunction resulting from failure to assemble electron transport chain complexes and altered the expression of several cellular genes involved in glycolysis. Surprisingly, the deletion of a single copy of the Grim-19 gene was sufficient to promote carcinogenesis and formation of invasive squamous cell carcinomas. These observations highlight the critical role of GRIM-19 as a tumor suppressor. PMID:24145455
GRIM-19 mutations fail to inhibit v-Src-induced oncogenesis
Kalakonda, Sudhakar; Nallar, Shreeram C.; Lindner, Daniel J.; Sun, Peng; Lorenz, Robert R.; Lamarre, Eric; Reddy, Sekhar P.; Kalvakolanu, Dhananjaya V.
The non-receptor tyrosine kinase Src is a major player in multiple physiological responses including growth, survival and differentiation. Overexpression and/or oncogenic mutation in the Src gene have been documented in human tumors. The v-Src protein is an oncogenic mutant of Src, which promotes cell survival, migration, invasion and division. GRIM-19 is an anti-oncogene isolated using a genome-wide knockdown screen. GRIM-19 binds to transcription factor STAT3 and ablates its pro-oncogenic effects while v-Src activates STAT3 to promote its oncogenic effects. However, we found that GRIM-19 inhibits the pro-oncogenic effects of v-Src independently of STAT3. Here, we report the identification of functionally inactivating GRIM-19 mutations in a set of Head and Neck cancer patients. While wild-type GRIM-19 strongly ablated v-Src-induced cell migration, cytoskeletal remodeling and tumor metastasis, the tumor-derived mutants (L71P, L91P and A95T) did not. These mutants were also incapable of inhibiting the drug resistance of v-Src-transformed cells. v-Src down regulated the expression of Pag1, a lipid raft-associated inhibitor of Src, which was restored by wild-type GRIM-19. The tumor-derived mutant GRIM-19 proteins failed to upregulate Pag1. These studies show a novel mechanism that deregulates Src activity in cancer cells. PMID:23851499
Upregulation of GRIM-19 inhibits the growth and invasion of human breast cancer cells.
Zhang, Wei; Du, Ye; Jiang, Tong; Geng, Wei; Yuan, Jiuli; Zhang, Duo
Gene associated with retinoid-interferon (IFN)-induced mortality 19 (GRIM-19), a novel IFN-β/retinoic acid-inducible gene product, has been identified as a potential tumor suppressor, which is associated with the inhibition of tumor growth. GRIM-19 has been demonstrated to be downregulated in the ovarian tissue of patients with breast cancer, however, its role in breast cancer remains to be fully elucidated. In the present study, a recombinant eukaryotic expression plasmid carrying GRIM-19 was constructed and then transfected into the MCF7 human breast cancer cell line to examine its effects on breast cancer cell growth, migration and invasion using several in vitro approaches. The results demonstrated that upregulation GRIM-19 in the MCF7 cells significantly inhibited cell proliferation, colony formation, migration and invasion, and induced cell apoptosis. Additionally, upregulation of GRIM-19 also suppressed the secretion of urokinase-type plasminogen activator (u-PA), matrix metalloproteinase (MMP)-2, MMP-9 and vascular endothelial growth factor (VEGF). It was also demonstrated that the activation of signal transducer and activator of transcription 3 (STAT3) was downregulated by the expression of GRIM-19. These results revealed that overexpression of the GRIM-19 gene may be an effective approach to control the growth and invasion of human breast cancer cells. PMID:25955394
Apoptotic proteins Reaper and Grim induce stable inactivation in voltage-gated K+ channels
Avdonin, V.; Kasuya, J.; Ciorba, M. A.; Kaplan, B.; Hoshi, T.; Iverson, L.
Drosophila genes reaper, grim, and head-involution-defective (hid) induce apoptosis in several cellular contexts. N-terminal sequences of these proteins are highly conserved and are similar to N-terminal inactivation domains of voltage-gated potassium (K+) channels. Synthetic Reaper and Grim N terminus peptides induced fast inactivation of Shaker-type K+ channels when applied to the cytoplasmic side of the channel that was qualitatively similar to the inactivation produced by other K+ channel inactivation particles. Mutations that reduce the apoptotic activity of Reaper also reduced the synthetic peptide's ability to induce channel inactivation, indicating that K+ channel inactivation correlated with apoptotic activity. Coexpression of Reaper RNA or direct injection of full length Reaper protein caused near irreversible block of the K+ channels. These results suggest that Reaper and Grim may participate in initiating apoptosis by stably blocking K+ channels. PMID:9751729
GRIM-19 opposes reprogramming of glioblastoma cell metabolism via HIF1α destabilization.
Liu, Qian; Wang, Lulu; Wang, Zhaojuan; Yang, Yang; Tian, Jingxia; Liu, Guoliang; Guan, Dongshi; Cao, Xinmin; Zhang, Yanmin; Hao, Aijun
The metabolism that sustains cancer cells is adapted preferentially to glycolysis, even under aerobic conditions (Warburg effect). This effect was one of the first alterations in cancer cells recognized as conferring a survival advantage. In this study, we show that gene associated with retinoid-interferon-induced mortality-19 (GRIM-19), which was previously identified as a tumor suppressor protein associated with growth inhibition and cell apoptosis, contributes to the switch between oxidative and glycolytic pathways. In parallel to this, vascular endothelial growth factor, which promotes neovascularization, is also regulated. We have identified hypoxia-inducible factor 1α (HIF1α) as the downstream factor of GRIM-19 in human glioblastoma cell lines. Downregulation of GRIM-19 promotes HIF1α synthesis in a STAT3-dependent manner, which acts as a potential competitive inhibitor for von Hippel-Lindau (pVHL)-HIF1α interaction, and thereby prevents HIF1α from pVHL-mediated ubiquitination and proteasomal degradation. Taken together, it is concluded that GRIM-19, a potential tumor suppressor gene, performs its function in part via regulating glioblastoma metabolic reprogramming through STAT3-HIF1α signaling axis, and this has added new perspective to its role in tumorigenesis, thus providing potential strategies for tumor metabolic therapy. PMID:23580587
Oxidation States of GRIM Glasses in EET79001 Based on Vanadium Valence
NASA Astrophysics Data System (ADS)
Sutton, S. R.; Rao, M. N.; Nyquist, L. E.
Mean vanadium valences determined by microXANES for gas-rich impact-melt (GRIM) glasses in EET79001 ranged from 3.0 to 3.6. Mean fO2 ranged from IW-1.2 to IW+1.4. Variable oxidation state is consistent with impact reduction of regolith precursors.
Inhibitor of Apoptosis Proteins Physically Interact with and Block Apoptosis Induced by Drosophila Proteins HID and GRIM
Vucic, Domagoj; Kaiser, William J.; Miller, Lois K.
Reaper (RPR), HID, and GRIM activate apoptosis in cells programmed to die during Drosophila development. We have previously shown that transient overexpression of RPR in the lepidopteran SF-21 cell line induces apoptosis and that members of the inhibitor of apoptosis (IAP) family of antiapoptotic proteins can inhibit RPR-induced apoptosis and physically interact with RPR through their BIR motifs (D. Vucic, W. J. Kaiser, A. J. Harvey, and L. K. Miller, Proc. Natl. Acad. Sci. USA 94:10183–10188, 1997). In this study, we found that transient overexpression of HID and GRIM also induced apoptosis in the SF-21 cell line. Baculovirus and Drosophila IAPs blocked HID- and GRIM-induced apoptosis and also physically interacted with them through the BIR motifs of the IAPs. The region of sequence similarity shared by RPR, HID, and GRIM, the N-terminal 14 amino acids of each protein, was required for the induction of apoptosis by HID and its binding to IAPs. When stably overexpressed by fusion to an unrelated, nonapoptotic polypeptide, the N-terminal 37 amino acids of HID and GRIM were sufficient to induce apoptosis and confer IAP binding activity. However, GRIM was more complex than HID since the C-terminal 124 amino acids of GRIM retained apoptosis-inducing and IAP binding activity, suggesting the presence of two independent apoptotic motifs within GRIM. Coexpression of IAPs with HID stabilized HID levels and resulted in the accumulation of HID in punctate perinuclear locations which coincided with IAP localization. The physical interaction of IAPs with RPR, HID, and GRIM provides a common molecular mechanism for IAP inhibition of these Drosophila proapoptotic proteins. PMID:9584170
Becoming a Truly Helpful Teacher: Considerably More Challenging, and Potentially More Fun, than Merely Doing Business as Usual
ERIC Educational Resources Information Center
Jason, Hilliard
Few medical faculty members are adequately prepared for their instructional responsibilities. Our educational traditions were established before we had research-based understandings of the teaching-learning process and before brain research began informing our understandings of how humans achieve lasting learning. Yet, there are several advantages…
Earthwatch and the HSBC Climate Partnership: Taking Stock of Forest Carbon Worldwide While Changing Business As Usual
NASA Astrophysics Data System (ADS)
Borgatti, R.; Bebber, D. P.; Riutta, T.; Murthy, I.; Ren, H.; Parker, G.; Capretz, R.; Phillips, R.
For the last 40 years, Earthwatch Institute has engaged people and communities in citizen science projects around the world, focusing on topics ranging from climate change in South American rainforests to wildlife conservation on the Mongolian Steppe. In collaboration with the financial institution HSBC and five research partners, Earthwatch has just completed a global, five-year (2007-2012) climate change research project in temperate and tropical forests representative of managed forests worldwide (www.earthwatch.org/hcp). This work was completed in part by more than 2200 HSBC staff members who worked side by side with scientists to collect forest data at five centers, one each in India, China, England, Brazil and the United States. This talk will present findings from this unprecedented climate change research program and highlight research findings specific to each of the countries. In addition to the results from the quantitative research collection, some of the key management outreach and business outcomes resulting from this research will be presented.
Morgenröthe or business as usual: a personal account of the 2nd Annual EULAR Congress, Prague
Wollheim, Frank A
The 2nd Annual European League Against Rheumatism (EULAR) Congress, held in Prague, 13–16 June 2001, was an impressive event with a record turnout of 8300 delegates. It offered a large variety of first-class state of the art lectures by some 180 invited worldwide speakers. Several new and ongoing therapeutic developments were discussed. The aim to attract the young scientific community was only partly achieved, and the dependence on industry posed some problems. The organization, however, was a big improvement compared with the previous congress in this series. The number of submitted abstracts was relatively low (1200) compared with the number of delegates. Accommodation of satellite symposia and organization of poster sessions remain problem areas of this meeting. The Annual EULAR Congress emerges as one of the two most important annual congresses of rheumatology, the other being the American College of Rheumatology meeting.
Application of Goldthorpe PDF model to dynamic fracture. Applications using the GRIM Eulerian hydrocode
NASA Astrophysics Data System (ADS)
Cullis, I. G.; Church, P. D.; Townsley, R.; Greenwood, P.; Proud, W. G.
The Goldthorpe Path Dependent Failure (PDF) model has been incorporated into the GRIM Eulerian hydrocode and has been applied to a number of dynamic fracture scenarios. The model has allowed a step change improvement in the simulation of fracture processes in an Euler scheme. The applications include shear plugging of a plate due to ballistic impact, prediction of the so-called V{50} for a small mass projectile and the impact of a generic EFP against an aluminium plate target. It has been noted that the prediction of temperature in hydrocodes, still requires more effort, particularly when materials approach the melt point. In addition there is also significant batch to batch variation in the fracture properties in materials, which needs to be taken into account when simulating a given application scenario. The paper discusses these points and recognises that GRIM is now an integral part of the design process for new ballistic designs.
Acid-Sulfate-Weathering Activity in Shergottite Sites on Mars Recorded in Grim Glasses
NASA Technical Reports Server (NTRS)
Rao, M. N.; Nyquist, L. E.; Ross, K.; Sutton, S. R.; Schwandt, C. S.
Based on mass spectrometric studies of sulfur species in Shergotty and EET79001, [1] and [2] showed that sulfates and sulfides occur in different proportions in shergottites. Sulfur speciation studies in gas-rich impact-melt (GRIM) glasses in EET79001 by the XANES method [3] showed that S K-XANES spectra in GRIM glasses from Lith A indicate that S is associated with Ca and Al presumably as sulfides/sulfates whereas the XANES spectra of amorphous sulfide globules in GRIM glasses from Lith B indicate that S is associated with Fe as FeS. In these amorphous iron sulfide globules, [4] found no Ni using FE-SEM and suggested that the globules resulting from immiscible sulfide melt may not be related to the igneous iron sulfides having approximately 1-3% Ni. Furthermore, in the amorphous iron sulfides from 507 GRIM glass, [5] determined delta(sup 34)S values ranging from +3.5%o to -3.1%o using Nano-SIMS. These values plot between the delta(sup 34)S value of +5.25%o determined in the sulfate fraction in Shergotty [6] at one extreme and the value of -1.7%o obtained for igneous sulfides in EET79001 and Shergotty [7] at the other. These results suggest that the amorphous Fe-S globules likely originated by shock reduction of secondary iron sulfate phases occurring in the regolith precursor materials during impact [7]. Sulfates in the regolith materials near the basaltic shergottite sites on Mars owe their origin to surficial acid-sulfate interactions. We examine the nature of these reactions by studying the composition of the end products in altered regolith materials. For the parent material composition, we use that of the host shergottite material in which the impact glasses are situated.
Simulations of atmospheric methane for Cape Grim, Tasmania, to constrain southeastern Australian methane emissions
NASA Astrophysics Data System (ADS)
Loh, Z. M.; Law, R. M.; Haynes, K. D.; Krummel, P. B.; Steele, L. P.; Fraser, P. J.; Chambers, S. D.; Williams, A. G.
This study uses two climate models and six scenarios of prescribed methane emissions to compare modelled and observed atmospheric methane between 1994 and 2007, for Cape Grim, Australia (40.7° S, 144.7° E). The model simulations follow the TransCom-CH4 protocol and use the Australian Community Climate and Earth System Simulator (ACCESS) and the CSIRO Conformal-Cubic Atmospheric Model (CCAM). Radon is also simulated and used to reduce the impact of transport differences between the models and observations. Comparisons are made for air samples that have traversed the Australian continent. All six emission scenarios give modelled concentrations that are broadly consistent with those observed. There are three notable mismatches, however. Firstly, scenarios that incorporate interannually varying biomass burning emissions produce anomalously high methane concentrations at Cape Grim at times of large fire events in southeastern Australia, most likely due to the fire methane emissions being unrealistically input into the lowest model level. Secondly, scenarios with wetland methane emissions in the austral winter overestimate methane concentrations at Cape Grim during wintertime while scenarios without winter wetland emissions perform better. Finally, all scenarios fail to represent a~methane source in austral spring implied by the observations. It is possible that the timing of wetland emissions in the scenarios is incorrect with recent satellite measurements suggesting an austral spring (September-October-November), rather than winter, maximum for wetland emissions.
Oxidation States of Grim Glasses in EET79001 Based on Vanadium Valence
NASA Technical Reports Server (NTRS)
Sutton, S. R.; Rao, M. N.; Nyquist, L. E.
Gas-rich impact-melt (GRIM) glasses in SNC meteorites are very rich in Martian atmospheric noble gases and sulfur suggesting a possible occurrence of regolith-derived secondary mineral assemblages in these samples. Previously, we have studied two GRIM glasses, 506 and 507, from EET79001 Lith A and Lith B, respectively, for elemental abundances and spatial distribution of sulfur using EMPA (WDS) and FE-SEM (EDS) techniques and for sulfur-speciation using K-edge XANES techniques. These elemental and FE-SEM micro-graph data at several locations in the GRIM glasses from Shergotty (DBS), Zagami 994 and EET79001, Lith B showed that FeO and SO3 are positively correlated (SO3 represents a mixture of sulfide and sulfate). FE-SEM (EDS) study revealed that the sulfur-rich pockets in these glasses contain numerous micron-sized iron-sulfide (Fe-S) globules sequestered throughout the volume. However, in some areas (though less frequently), we detected significant Fe-S-O signals suggesting the occurrence of iron sulfate. These GRIM glasses were studied by K-edge microXANES techniques for sulfur speciation in association with iron in sulfur-rich areas. In both samples, we found the sulfur speciation dominated by sulfide with minor oxidized sulfur mixed in with various proportions. The abundance of oxidized sulfur was greater in 506 than in 507. Based on these results, we hypothesize that sulfur initially existed as sulfate in the glass precursor materials and, on shock-impact melting of the precursor materials producing these glasses, the oxidized sulfur was reduced to predominately sulfide. In order to further test this hypothesis, we have used microXANES to measure the valence states of vanadium in GRIM glasses from Lith A and Lith B to complement and compare with previous analogous measurements on Lith C (note: 506 and 507 contain the largest amounts of martian atmospheric gases but the gas-contents in Lith C measured by are unknown). Vanadium is ideal for addressing this re
NASA Technical Reports Server (NTRS)
Kalnay, Eugenia; Dalcher, Amnon
It is shown that it is possible to predict the skill of numerical weather forecasts - a quantity which is variable from day to day and region to region. This has been accomplished using as predictor the dispersion (measured by the average correlation) between members of an ensemble of forecasts started from five different analyses. The analyses had been previously derived for satellite-data-impact studies and included, in the Northern Hemisphere, moderate perturbations associated with the use of different observing systems. When the Northern Hemisphere was used as a verification region, the prediction of skill was rather poor. This is due to the fact that such a large area usually contains regions with excellent forecasts as well as regions with poor forecasts, and does not allow for discrimination between them. However, when regional verifications were used, the ensemble forecast dispersion provided a very good prediction of the quality of the individual forecasts.
Observation of sea-salt fraction in sub-100 nm diameter particles at Cape Grim
NASA Astrophysics Data System (ADS)
Cravigan, Luke T.; Ristovski, Zoran; Modini, Robin L.; Keywood, Melita D.; Gras, John L.
Volatility-hygroscopicity tandem differential mobility analyzer measurements were used to infer the composition of sub-100 nm diameter Southern Ocean marine aerosols at Cape Grim in November and December 2007. This study focuses on a short-lived high sea spray aerosol (SSA) event on 7-8 December with two externally mixed modes in the Hygroscopic Growth Factor (HGF) distributions (90% relative humidity (RH)), one at HGF > 2 and another at HGF~1.5. The particles with HGF > 2 displayed a deliquescent transition at 73-75% RH and were nonvolatile up to 280°C, which identified them as SSA particles with a large inorganic sea-salt fraction. SSA HGFs were 3-13% below those for pure sea-salt particles, indicating an organic volume fraction (OVF) of up to 11-46%. Observed high inorganic fractions in sub-100 nm SSA is contrary to similar, earlier studies. HGFs increased with decreasing particle diameter over the range 16-97 nm, suggesting a decreased OVF, again contrary to earlier studies. SSA comprised up to 69% of the sub-100 nm particle number, corresponding to concentrations of 110-290 cm-3. Air mass back trajectories indicate that SSA particles were produced 1500 km, 20-40 h upwind of Cape Grim. Transmission electron microscopy (TEM) and X-ray spectrometry measurements of sub-100 nm aerosols collected from the same location, and at the same time, displayed a distinct lack of sea salt. Results herein highlight the potential for biases in TEM analysis of the chemical composition of marine aerosols.
Surface ozone at rural sites in the latrobe valley and Cape Grim, Australia
NASA Astrophysics Data System (ADS)
Galbally, I. E.; Miller, A. J.; Hoy, R. D.; Ahmet, S.; Joynt, R. C.; Attwood, D.
Ozone and other air quality data from five rural sites in the industrialized Latrobe Valley, Victoria, have been subject to statistical analyses including linear regression modelling. The behaviour of O 3 in the Latrobe Valley is explained largely in terms of natural background atmospheric processes as observed at Cape Grim, Tasmania. The maximum 1-h average concentration of naturally occurring O 3 (obtained from a 6-year record at Cape Grim) is less than 40 ppb (v/v). In contrast the industrialized Latrobe Valley sites show O 3 values exceeding 40 ppb between 1% and 3% of the time. These higher concentrations occur in conditions consistent with local photochemical production of O 3 via 'smog' type processes and appear preferentially at low NO x concentrations (3-4 ppb) during the afternoon (13-18 h) and at high temperatures (above 25°C). A comparison of observations from an elevated station (750 m) with those from the valley floor shows systematic differences in seasonal and diurnal O 3 variations and the time of day of occurrence of elevated O 3 concentrations which can be explained in terms of the diurnal cycle of convective mixing and mountain/valley winds. A linear regression model incorporating this understanding has accounted for between 43% and 64% of the variance of O 3 concentration at the elevated and rural stations. The statistical model incorporates temperature, time of day, month of year, wind speed, O 3 concentration 24-h earlier and NO x concentration as variables in the regression equation, with temperature being the dominant variable. The standard deviation of the residual O 3 values (observed minus fitted) is around 5 ppb. Auto and cross correlations are used to show that perhaps half of the unexplained variance is coherent from site to site and hence potentially could be modelled.
Navy Mobility Fuels Forecasting System report: Navy fuel production in the year 2000
Hadder, G.R.; Davis, R.M.
The Refinery Yield Model of the Navy Mobility Fuels Forecasting System has been used to study the feasibility and quality of Navy JP-5 jet fuel and F-76 marine diesel fuel for two scenarios in the year 2000. Both scenarios account for environmental regulations for fuels produced in the US and assume that Eastern Europe, the USSR, and the People`s Republic of China have free market economies. One scenario is based on business-as-usual market conditions for the year 2000. The second scenario is similar to first except that USSR crude oil production is 24 percent lower. During lower oil production in the USSR., there are no adverse effects on Navy fuel availability, but JP-5 is generally a poorer quality fuel relative to business-as-usual in the year 2000. In comparison with 1990, there are two potential problems areas for future Navy fuel quality. The first problem is increased aromaticity of domestically produced Navy fuels. Higher percentages of aromatics could have adverse effects on storage, handling, and combustion characteristics of both JP-5 and F-76. The second, and related, problem is that highly aromatic light cycle oils are blended into F-76 at percentages which promote fuel instability. It is recommended that the Navy continue to monitor the projected trend toward increased aromaticity in JP-5 and F-76 and high percentages of light cycle oils in F-76. These potential problems should be important considerations in research and development for future Navy engines.
Navy Mobility Fuels Forecasting System report: Navy fuel production in the year 2000
Hadder, G.R.; Davis, R.M.
The Refinery Yield Model of the Navy Mobility Fuels Forecasting System has been used to study the feasibility and quality of Navy JP-5 jet fuel and F-76 marine diesel fuel for two scenarios in the year 2000. Both scenarios account for environmental regulations for fuels produced in the US and assume that Eastern Europe, the USSR, and the People's Republic of China have free market economies. One scenario is based on business-as-usual market conditions for the year 2000. The second scenario is similar to first except that USSR crude oil production is 24 percent lower. During lower oil production in the USSR., there are no adverse effects on Navy fuel availability, but JP-5 is generally a poorer quality fuel relative to business-as-usual in the year 2000. In comparison with 1990, there are two potential problems areas for future Navy fuel quality. The first problem is increased aromaticity of domestically produced Navy fuels. Higher percentages of aromatics could have adverse effects on storage, handling, and combustion characteristics of both JP-5 and F-76. The second, and related, problem is that highly aromatic light cycle oils are blended into F-76 at percentages which promote fuel instability. It is recommended that the Navy continue to monitor the projected trend toward increased aromaticity in JP-5 and F-76 and high percentages of light cycle oils in F-76. These potential problems should be important considerations in research and development for future Navy engines.
High Glucose Induces Down-Regulated GRIM-19 Expression to Activate STAT3 Signaling and Promote Cell Proliferation in Cell Culture
Li, Yong-Guang; Han, Bei-Bei; Li, Feng; Yu, Jian-Wu; Dong, Zhi-Feng; Niu, Geng-Ming; Qing, Yan-Wei; Li, Jing-Bo; Wei, Meng; Zhu, Wei
Recent studies indicated that Gene Associated with Retinoid-IFN-Induced Mortality 19 (GRIM-19), a newly discovered mitochondria-related protein, can regulate mitochondrial function and modulate cell viability possibly via interacting with STAT3 signal. In the present study we sought to test: 1) whether GRIM-19 is involved in high glucose (HG) induced altered cell metabolism in both cancer and cardiac cells, 2) whether GRIM-19/STAT3 signaling pathway plays a role in HG induced biological effects, especially whether AMPK activity could be involved. Our data showed that HG enhanced cell proliferation of both HeLa and H9C2 cells, which was closely associated with down-regulated GRIM-19 expression and increased phosphorylated STAT3 level. We showed that GRIM-19 knock-down alone in normal glucose cultured cells can also result in an increase in phosphorylated STAT3 level and enhanced proliferation capability, whereas GRIM-19 over-expression can abolished HG induced STAT3 activation and enhanced cell proliferation. Importantly, both down-regulated or over-expression of GRIM-19 increased lactate production in both HeLa and H9C2 cells. The activated STAT3 was responsible for increased cell proliferation as either AG-490, an inhibitor of JAK2, or siRNA targeting STAT3 can attenuate cell proliferation increased by HG. In addition, HG increased lactate acid levels in HeLa cells, which was also observed when GRIM-19 was genetically manipulated. However, HG did not affect the lactate levels in H9C2 cells. Of note, over-expression of GRIM-19 and silencing of STAT3 both increased lactate production in H9C2 cells. As expected, HG resulted in significant decreases in phosphorylated AMPKα levels in H9C2 cells, but not in HeLa cells. Interestingy, activation of AMPKα by metformin was associated with a reversal of the suppressed GRIM-19 expression in H9C2 cells, the fold of changes in GRIM-19 expression by metformin were much less in HeLa cells. Metformin did not affect the
Characterisation of J(O1D) at Cape Grim 2000-2005
NASA Astrophysics Data System (ADS)
Wilson, S. R.
Estimates of the rate of production of excited oxygen atoms due to the photolysis of ozone (J(O1D)) have been derived from radiation measurements carried out at Cape Grim, Tasmania (40.6° S, 144.7° E). The individual measurements have a total uncertainty of 16 % (1σ). These estimates agree well with model estimates of clear-sky photolysis rates. Observations spanning 2000-2005 have been used to quantify the impact of season, clouds and ozone column amount. The annual cycle of J(O1D) has been investigated via monthly means. These means show an interannual variation (monthly standard deviation) of 9 %, but in midsummer and midwinter this reduces to 3-5 %. Variations in solar zenith angle and total column ozone explain 86 % of the observed variability in the measured photolysis rates. The impact of total column ozone, expressed as a radiation amplification factor (RAF), is found to be ~ 1.53, in agreement with model estimates. This ozone dependence explains 20 % of the variation observed at medium solar zenith angles (30-50°). The impact of clouds results in a median reduction of 30 % in J(O1D) for the same solar zenith angle range. Including estimates of cloudiness derived from long-wave radiation measurements resulted in a statistically significant fit to observations, but the quality of the fit did not increase significantly as measured by the adjusted R2.
Aerosol Optical Depth at Cape Grim 1986 - 2014: What does it tell us?
NASA Astrophysics Data System (ADS)
Wilson, Stephen
The Cape Grim Baseline Air Pollution Station is located near the northwest tip of Tasmania (Australia), a site chosen to permit measurement of the atmospheric environment over the southern oceans. Atmospheric measurements began in the late 1970s, and observations of Aerosol Optical Depth (AOD) using automated sunphotometers began in 1986. Since then, measurements have continued with a range of different instruments operating at a varying number of wavelengths. The site is challenging for these measurements as it is exposed to a sea-salt laden atmosphere, which presents both instrumental issues (corrosion) and measurement complications (salt fouling of the windows) in addition to the high frequency of cloud. The dataset has been processed to produce a record of half-hourly AOD for the period 1986 - 2014 and investigated for internal consistency. In general the AOD is small (around 0.05 at 500nm). The impact of the Mount Pinatubo eruption in 1991 can be clearly observed, along with a persistent annual cycle. This has been further analyzed fitting to all wavelengths measured to derive an averaged optical depth (at 500 nm) and some preliminary aerosol size distribution information.
Navy mobility fuels forecasting system report: World petroleum trade forecasts for the year 2000
Das, S.
The Middle East will continue to play the dominant role of a petroleum supplier in the world oil market in the year 2000, according to business-as-usual forecasts published by the US Department of Energy. However, interesting trade patterns will emerge as a result of the democratization in the Soviet Union and Eastern Europe. US petroleum imports will increase from 46% in 1989 to 49% in 2000. A significantly higher level of US petroleum imports (principally products) will be coming from Japan, the Soviet Union, and Eastern Europe. Several regions, the Far East, Japan, Latin American, and Africa will import more petroleum. Much uncertainty remains about of the level future Soviet crude oil production. USSR net petroleum exports will decrease; however, the United States and Canada will receive some of their imports from the Soviet Union due to changes in the world trade patterns. The Soviet Union can avoid becoming a net petroleum importer as long as it (1) maintains enough crude oil production to meet its own consumption and (2) maintains its existing refining capacities. Eastern Europe will import approximately 50% of its crude oil from the Middle East.
The Transition from Business as Usual to Funding for Results: State Efforts To Integrate Performance Measures in the Higher Education Budgetary Process.
ERIC Educational Resources Information Center
Albright, Brenda Norman
This report describes a 1997 survey which examined performance funding in higher education and offers guidelines for states' and institutions' explorations of performance-based funding. Among highlights of the survey are: 32 states are planning or using performance measures in the state budget process; legislatively mandated initiatives are…
The Role of Chapter Meetings: Business as Usual or a Course in Leadership Development? and Why FBLA-PBL? The Importance of Student Organizations.
ERIC Educational Resources Information Center
Fracaroli, Mary Lynn; Fitzhugh-Pemberton, Gladys
Fracaroli describes ways advisors can use vocational student organizations as leadership laboratories. Fitzhugh-Pemberton explains the value to students of joining Future Business Leaders of America-Phi Beta Lambda. (SK)
Social change or business as usual at city hall? Examining an urban municipal government's response to neighbourhood-level health inequities.
Cahuas, Madelaine C; Wakefield, Sarah; Peng, Yun
There is a renewed interest in the potential of municipal governments working collaboratively with local communities to address health inequities. A growing body of literature has also highlighted the benefits and limitations of participatory approaches in neighbourhood interventions initiated by municipal governments. However, few studies have investigated how neighbourhood interventions tackling health inequities work in real-time and in context, from the perspectives of Community Developers (CDs) who promote community participation. This study uses a process evaluation approach and semi-structured interviews with CDs to explore the challenges they face in implementing a community development, participatory process in the City of Hamilton's strategy to reduce health inequities - Neighbourhood Action. Findings demonstrate that municipal government can facilitate and suppress community participation in complex ways. CDs serve as significant but conflicted intermediaries as they negotiate and navigate power differentials between city and community actors, while also facing structural challenges. We conclude that community participation is important to bottom-up, resident-led social change, and that CDs are central to this work. PMID:25245453
Paradigm Shift or Business as Usual: The Reception and Implementation of the BYU-Idaho Learning Model by Faculty Members--A Mixed Methods Study
ERIC Educational Resources Information Center
Thurgood, Larry L.
A mixed methods study examined how a newly developed campus-wide framework for learning and teaching, called the Learning Model, was accepted and embraced by faculty members at Brigham Young University-Idaho from September 2007 to January 2009. Data from two administrations of the Approaches to Teaching Inventory showed that (a) faculty members…
Biomass burning emissions of trace gases and particles in marine air at Cape Grim, Tasmania
NASA Astrophysics Data System (ADS)
Lawson, S. J.; Keywood, M. D.; Galbally, I. E.; Gras, J. L.; Cainey, J. M.; Cope, M. E.; Krummel, P. B.; Fraser, P. J.; Steele, L. P.; Bentley, S. T.; Meyer, C. P.; Ristovski, Z.; Goldstein, A. H.
Biomass burning (BB) plumes were measured at the Cape Grim Baseline Air Pollution Station during the 2006 Precursors to Particles campaign, when emissions from a fire on nearby Robbins Island impacted the station. Measurements made included non-methane organic compounds (NMOCs) (PTR-MS), particle number size distribution, condensation nuclei (CN) > 3 nm, black carbon (BC) concentration, cloud condensation nuclei (CCN) number, ozone (O3), methane (CH4), carbon monoxide (CO), hydrogen (H2), carbon dioxide (CO2), nitrous oxide (N2O), halocarbons and meteorology. During the first plume strike event (BB1), a 4 h enhancement of CO (max ~ 2100 ppb), BC (~ 1400 ng m-3) and particles > 3 nm (~ 13 000 cm-3) with dominant particle mode of 120 nm were observed overnight. A wind direction change lead to a dramatic reduction in BB tracers and a drop in the dominant particle mode to 50 nm. The dominant mode increased in size to 80 nm over 5 h in calm sunny conditions, accompanied by an increase in ozone. Due to an enhancement in BC but not CO during particle growth, the presence of BB emissions during this period could not be confirmed. The ability of particles > 80 nm (CN80) to act as CCN at 0.5 % supersaturation was investigated. The ΔCCN / ΔCN80 ratio was lowest during the fresh BB plume (56 ± 8 %), higher during the particle growth period (77 ± 4 %) and higher still (104 ± 3 %) in background marine air. Particle size distributions indicate that changes to particle chemical composition, rather than particle size, are driving these changes. Hourly average CCN during both BB events were between 2000 and 5000 CCN cm-3, which were enhanced above typical background levels by a factor of 6-34, highlighting the dramatic impact BB plumes can have on CCN number in clean marine regions. During the 29 h of the second plume strike event (BB2) CO, BC and a range of NMOCs including acetonitrile and hydrogen cyanide (HCN) were clearly enhanced and some enhancements in O3 were observed
Mutation in NDUFA13/GRIM19 leads to early onset hypotonia, dyskinesia and sensorial deficiencies, and mitochondrial complex I instability.
Angebault, Claire; Charif, Majida; Guegen, Naig; Piro-Megy, Camille; Mousson de Camaret, Benedicte; Procaccio, Vincent; Guichet, Pierre-Olivier; Hebrard, Maxime; Manes, Gael; Leboucq, Nicolas; Rivier, François; Hamel, Christian P; Lenaers, Guy; Roubertie, Agathe
Mitochondrial complex I (CI) deficiencies are causing debilitating neurological diseases, among which, the Leber Hereditary Optic Neuropathy and Leigh Syndrome are the most frequent. Here, we describe the first germinal pathogenic mutation in the NDUFA13/GRIM19 gene encoding a CI subunit, in two sisters with early onset hypotonia, dyskinesia and sensorial deficiencies, including a severe optic neuropathy. Biochemical analysis revealed a drastic decrease in CI enzymatic activity in patient muscle biopsies, and reduction of CI-driven respiration in fibroblasts, while the activities of complex II, III and IV were hardly affected. Western blots disclosed that the abundances of NDUFA13 protein, CI holoenzyme and super complexes were drastically reduced in mitochondrial fractions, a situation that was reproduced by silencing NDUFA13 in control cells. Thus, we established here a correlation between the first mutation yet identified in the NDUFA13 gene, which induces CI instability and a severe but slowly evolving clinical presentation affecting the central nervous system. PMID:25901006
Non-sea-salt sulfate, methanesulfonate, and nitrate aerosol concentrations and size distributions at Cape Grim, Tasmania
NASA Astrophysics Data System (ADS)
Andreae, Meinrat O.; Elbert, Wolfgang; Cai, Yong; Andreae, Tracey W.; Gras, John
We collected weekly aerosol samples using high-volume impactors over a period of 20 months (1988-1990) at the Cape Grim baseline station on the northwestern coast of Tasmania, Australia. The samples were analyzed for soluble ionic constituents, including sulfate, methanesulfonate (MS-), ammonium, nitrate, and the major sea-salt ions. The sea-salt component showed only a slight seasonal variation, whereas the non-sea-salt (nss) ions all had pronounced summer maxima. Significant interannual variability was seen between the nss ion concentrations measured during the two summers investigated. Nss sulfate and MS- were present both in the fine and coarse aerosol fractions, in the latter presumably associated with sea-salt particles. During the winter period, there was more nss sulfate in the coarse fraction than in the fine fraction. These observations are consistent with an important role of liquid-phase oxidation in haze and cloud droplets for the production of nss sulfate aerosol. The seasonal behavior of the sulfur and nitrogen species at Cape Grim and their mutual correlations suggest that DMS oxidation is the dominant sulfur source during summer, while nonbiogenic sulfur sources make significant contributions to nss sulfate outside of this season. Correlations of CN and CCN concentrations with nss sulfate, MS-, and wind speed suggest that DMS oxidation and, to a lesser extent, seaspray formation contributes to CN and CCN populations. The contrast between the weak seasonality of the sea-salt component and the pronounced seasonal behavior in both sulfur species and CCN supports the central role of biogenic DMS emissions as precursors of CCN in this region, at least in the biologically productive season.
NASA Technical Reports Server (NTRS)
ROFFS stands for Roffer's Ocean Fishing Forecasting Service, Inc. Roffer combines satellite and computer technology with oceanographic information from several sources to produce frequently updated charts sometimes as often as 30 times a day showing clues to the location of marlin, sailfish, tuna, swordfish and a variety of other types. Also provides customized forecasts for racing boats and the shipping industry along with seasonal forecasts that allow the marine industry to formulate fishing strategies based on foreknowledge of the arrival and departure times of different fish. Roffs service exemplifies the potential for benefits to marine industries from satellite observations. Most notable results are reduced search time and substantial fuel savings.
Forecasting Future Social Needs
ERIC Educational Resources Information Center
Abt, Clark C.
Describes briefly why social forecasting is easier than technological forecasting, offers four approaches to social forecasting (judgment, extrapolation, speculation, analysis), and suggests a procedure recommended for social forecasting. (CJ)
ERIC Educational Resources Information Center
Taylor, Kelley R.
This article presents a sample legal battle that illustrates school officials' "reasonable forecasts" of substantial disruption in the school environment. In 2006, two students from a Texas high school came to school carrying purses decorated with images of the Confederate flag. The school district has a zero-tolerance policy for clothing or…
NASA Technical Reports Server (NTRS)
Mauldin, L. E.
Business travel planning within an organization is often a time-consuming task. Travel Forecaster is a menu-driven, easy-to-use program which plans, forecasts cost, and tracks actual vs. planned cost for business-related travel of a division or branch of an organization and compiles this information into a database to aid the travel planner. The program's ability to handle multiple trip entries makes it a valuable time-saving device. Travel Forecaster takes full advantage of relational data base properties so that information that remains constant, such as per diem rates and airline fares (which are unique for each city), needs entering only once. A typical entry would include selection with the mouse of the traveler's name and destination city from pop-up lists, and typed entries for number of travel days and purpose of the trip. Multiple persons can be selected from the pop-up lists and multiple trips are accommodated by entering the number of days by each appropriate month on the entry form. An estimated travel cost is not required of the user as it is calculated by a Fourth Dimension formula. With this information, the program can produce output of trips by month with subtotal and total cost for either organization or sub-entity of an organization; or produce outputs of trips by month with subtotal and total cost for international-only travel. It will also provide monthly and cumulative formats of planned vs. actual outputs in data or graph form. Travel Forecaster users can do custom queries to search and sort information in the database, and it can create custom reports with the user-friendly report generator. Travel Forecaster 1.1 is a database program for use with Fourth Dimension Runtime 2.1.1. It requires a Macintosh Plus running System 6.0.3 or later, 2Mb of RAM and a hard disk. The standard distribution medium for this package is one 3.5 inch 800K Macintosh format diskette. Travel Forecaster was developed in 1991. Macintosh is a registered trademark of
Forecaster's dilemma: Extreme events and forecast evaluation
NASA Astrophysics Data System (ADS)
Lerch, Sebastian; Thorarinsdottir, Thordis; Ravazzolo, Francesco; Gneiting, Tilmann
In discussions of the quality of forecasts in the media and public, attention often focuses on the predictive performance in the case of extreme events. Intuitively, accurate predictions on the subset of extreme events seem to suggest better predictive ability. However, it can be demonstrated that restricting conventional forecast verification methods to subsets of observations might have unexpected and undesired effects and may discredit even the most skillful forecasters. Hand-picking extreme events is incompatible with the theoretical assumptions of established forecast verification methods, thus confronting forecasters with what we refer to as the forecaster's dilemma. For probabilistic forecasts, weighted proper scoring rules provide suitable alternatives for forecast evaluation with an emphasis on extreme events. Using theoretical arguments, simulation experiments and a case study on probabilistic forecasts of wind speed over Germany, we illustrate the forecaster's dilemma and the use of weighted proper scoring rules.
Turnipseed, D. Phil
Forecast Mekong is part of the U.S. Department of State's Lower Mekong Initiative, which was launched in 2009 by Secretary Hillary Clinton and the Foreign Ministers of Cambodia, Laos, Thailand, and Vietnam to enhance partnerships between the U.S. and the Lower Mekong River countries in the areas of environment, health, education, and infrastructure. The U.S. Geological Survey (USGS) is working in close cooperation with the U.S. Department of State to use research and data from the Lower Mekong Basin to provide hands-on results that will help decision makers in Lower Mekong River countries in the planning and design for restoration, conservation, and management efforts in the basin.
ERIC Educational Resources Information Center
Fogg, Neeta P.; Harrington, Paul E.
June 2009 is seen by many as the end of the Great Recession. Strong growth in GDP following massive monetary and fiscal responses to the collapse in housing and financial markets meant that the economy was on the mend. Yet a year later, 1.1 million "fewer" people are working, and the unemployment rate is stuck at 9.5%. Worse still, more than one…
Biomass burning emissions of trace gases and particles in marine air at Cape Grim, Tasmania, 41° S
NASA Astrophysics Data System (ADS)
Lawson, S. J.; Keywood, M. D.; Galbally, I. E.; Gras, J. L.; Cainey, J. M.; Cope, M. E.; Krummel, P. B.; Fraser, P. J.; Steele, L. P.; Bentley, S. T.; Meyer, C. P.; Ristovski, Z.; Goldstein, A. H.
Biomass burning (BB) plumes were measured at the Cape Grim Baseline Air Pollution Station during the 2006 Precursors to Particles campaign, when emissions from a fire on nearby Robbins Island impacted the station. Measurements made included non methane organic compounds (NMOCs) (PTR-MS), particle number size distribution, condensation nuclei (CN) > 3 nm, black carbon (BC) concentration, cloud condensation nuclei (CCN) number, ozone (O3), methane (CH4), carbon monixide (CO), hydrogen (H2), carbon dioxide (CO2), nitrous oxide (N2O), halocarbons and meteorology. During the first plume strike event (BB1), a four hour enhancement of CO (max ~ 2100 ppb), BC (~ 1400 ng m-3) and particles > 3 nm (~ 13 000 cm-3) with dominant particle mode of 120 nm were observed overnight. Dilution of the plume resulted in a drop in the dominant particle mode to 50 nm, and then growth to 80 nm over 5 h. This was accompanied by an increase in O3, suggesting that photochemical processing of air and condensation of low volatility oxidation products may be driving particle growth. The ability of particles > 80 nm (CN80) to act as CCN at 0.5 % supersaturation was investigated. The ΔCCN / ΔCN80 ratio was lowest during the fresh BB plume (56 %), higher during the particle growth event (77 %) and higher still (104 %) in background marine air. Particle size distributions indicate that changes to particle chemical composition, rather than particle size, are driving these changes. Hourly average CCN during both BB events were between 2000-5000 CCN cm-3, which were enhanced above typical background levels by a factor of 6-34, highlighting the dramatic impact BB plumes can have on CCN number in clean marine regions. During the 29 h of the second plume strike event (BB2) CO, BC and a range of NMOCs including acetonitrile and hydrogen cyanide (HCN) were clearly enhanced and some enhancements in O3 were observed (ΔO3 / ΔCO 0.001-0.074). A shortlived increase in NMOCs by a factor of 10 corresponded
NASA Technical Reports Server (NTRS)
Lambert, Winifred C.
This report describes the outcome of Phase 1 of the AMU's Improved Anvil Forecasting task. Forecasters in the 45th Weather Squadron and the Spaceflight Meteorology Group have found that anvil forecasting is a difficult task when predicting LCC and FR violations. The purpose of this task is to determine the technical feasibility of creating an anvil-forecasting tool. Work on this study was separated into three steps: literature search, forecaster discussions, and determination of technical feasibility. The literature search revealed no existing anvil-forecasting techniques. However, there appears to be growing interest in anvils in recent years. If this interest continues to grow, more information will be available to aid in developing a reliable anvil-forecasting tool. The forecaster discussion step revealed an array of methods on how better forecasting techniques could be developed. The forecasters have ideas based on sound meteorological principles and personal experience in forecasting and analyzing anvils. Based on the information gathered in the discussions with the forecasters, the conclusion of this report is that it is technically feasible at this time to develop an anvil forecasting technique that will significantly contribute to the confidence in anvil forecasts.
Verifications of the medium-range forecasts of KIAPS integrated model
NASA Astrophysics Data System (ADS)
Lee, Eun-Hee; Lee, Juwon; Choi, In-Jin
The Korea Institute of Atmospheric Prediction System, KIAPS, was established to carry out a national project in developing a new global forecast system from 2011 to 2019. The initial version of KIAPS Integrated Model, KIM, consisted of a spectral element dynamical core on a cubed sphere and a standard physics package from existing models such as the GRIMs, WRF, and GFS. Then KIM2.0 was released with the advanced or newly developed physics, dynamics, and data assimilation. Last July, its semi-real time forecast for 5 days has been operated every 00 and 12 UTC with the fully coupled 3D Var data assimilation system. Performance of KIM forecasts is evaluated both for the period of the selected testbed cases and for the semi-real time operational period, to examine the model improvement along with the upgrade and to figure out the model bias. Standardized statistical verification is also conducted including verification against analyses and observations (e.g., sonde and precipitation data). These will be summarized in this presentation. Additionally, surface verification using SYNOP observations and spatial verification for precipitation applied to meet the need for more informative forecast evaluations will be discussed.
Computers and Technological Forecasting
ERIC Educational Resources Information Center
Martino, Joseph P.
Forecasting is becoming increasingly automated, thanks in large measure to the computer. It is now possible for a forecaster to submit his data to a computation center and call for the appropriate program. (No knowledge of statistics is required.) (Author)
Forecasting Artificial Intelligence Demand
NASA Astrophysics Data System (ADS)
Wheeler, David R.; Shelley, Charles
Forecasts are major components of the decision analysis process. When accurate, estimates of future economic activity associated with specific courses of action can correctly set corporate strategy in an uncertain environment. When inaccurate, they can lead to bankruptcy. The basic trouble with most forecasts is that they are not made by forecasters.
Forecaster priorities for improving probabilistic flood forecasts
NASA Astrophysics Data System (ADS)
Wetterhall, Fredrik; Pappenberger, Florian; Alfieri, Lorenzo; Cloke, Hannah; Thielen, Jutta
Hydrological ensemble prediction systems (HEPS) have in recent years been increasingly used for the operational forecasting of floods by European hydrometeorological agencies. The most obvious advantage of HEPS is that more of the uncertainty in the modelling system can be assessed. In addition, ensemble prediction systems generally have better skill than deterministic systems both in the terms of the mean forecast performance and the potential forecasting of extreme events. Research efforts have so far mostly been devoted to the improvement of the physical and technical aspects of the model systems, such as increased resolution in time and space and better description of physical processes. Developments like these are certainly needed; however, in this paper we argue that there are other areas of HEPS that need urgent attention. This was also the result from a group exercise and a survey conducted to operational forecasters within the European Flood Awareness System (EFAS) to identify the top priorities of improvement regarding their own system. They turned out to span a range of areas, the most popular being to include verification of an assessment of past forecast performance, a multi-model approach for hydrological modelling, to increase the forecast skill on the medium range (>3 days) and more focus on education and training on the interpretation of forecasts. In light of limited resources, we suggest a simple model to classify the identified priorities in terms of their cost and complexity to decide in which order to tackle them. This model is then used to create an action plan of short-, medium- and long-term research priorities with the ultimate goal of an optimal improvement of EFAS in particular and to spur the development of operational HEPS in general.
Weather forecasting expert system study
NASA Technical Reports Server (NTRS)
Weather forecasting is critical to both the Space Transportation System (STS) ground operations and the launch/landing activities at NASA Kennedy Space Center (KSC). The current launch frequency places significant demands on the USAF weather forecasters at the Cape Canaveral Forecasting Facility (CCFF), who currently provide the weather forecasting for all STS operations. As launch frequency increases, KSC's weather forecasting problems will be great magnified. The single most important problem is the shortage of highly skilled forecasting personnel. The development of forecasting expertise is difficult and requires several years of experience. Frequent personnel changes within the forecasting staff jeopardize the accumulation and retention of experience-based weather forecasting expertise. The primary purpose of this project was to assess the feasibility of using Artificial Intelligence (AI) techniques to ameliorate this shortage of experts by capturing aria incorporating the forecasting knowledge of current expert forecasters into a Weather Forecasting Expert System (WFES) which would then be made available to less experienced duty forecasters.
Weather assessment and forecasting
NASA Technical Reports Server (NTRS)
Data management program activities centered around the analyses of selected far-term Office of Applications (OA) objectives, with the intent of determining if significant data-related problems would be encountered and if so what alternative solutions would be possible. Three far-term (1985 and beyond) OA objectives selected for analyses as having potential significant data problems were large-scale weather forecasting, local weather and severe storms forecasting, and global marine weather forecasting. An overview of general weather forecasting activities and their implications upon the ground based data system is provided. Selected topics were specifically oriented to the use of satellites.
No evidence for change of the atmospheric helium isotope composition since 1978 from re-analysis of the Cape Grim Air Archive
NASA Astrophysics Data System (ADS)
Mabry, Jennifer C.; Lan, Tefang; Boucher, Christine; Burnard, Peter G.; Brennwald, Matthias S.; Langenfelds, Ray; Marty, Bernard
The helium isotope composition of air might have changed since the industrial revolution due to the release of 4He-rich crustal helium during exploitation of fossil fuels. Thereby, variation of the atmospheric helium isotope ratio (3He/4He) has been proposed as a possible new atmospheric tracer of industrial activity. However, the magnitude of such change is debated, with possible values ranging from 0 to about 2 ‰ /yr (Sano et al., 1989; Hoffman and Nier, 1993; Pierson-Wickmann et al., 2001; Brennwald et al., 2013; Lupton and Evans, 2013). A new analytical facility for high precision (2‰, 2σ) analysis of the 3He/4He ratio of air has been developed at CRPG Nancy (France) capable of investigating permil level variations. Previously, Brennwald et al. (2013) analyzed a selection of air samples archived since 1978 at Cape Grim, Tasmania, by the Commonwealth Scientific and Industrial Research Organisation (CSIRO). They reported a mean temporal decrease of the 3He/4He ratio of 0.23-0.30‰/yr. Re-analysis of aliquots of the same samples using the new high-precision instrument showed no significant temporal decrease of the 3He/4He ratio (0.0095 ± 0.033‰ /yr, 2σ) in the time interval 1978-2011. These new data constrain the mean He content of globally produced natural gas to about 0.034% or less, which is about 3× lower than commonly quoted.
Forecasting Future Trends in Education
ERIC Educational Resources Information Center
Collazo, Andres; And Others
Describes a forecasting model sensitive to the major factors influencing educational outcomes, presents several forecasts based on alternative sets of assumptions, and discusses the implications of these forecasts, including ways to subvert them. (Author/JG)
NASA Technical Reports Server (NTRS)
Mcmahon, J.
Opinions or plans of qualified experts in the field are used for forecasting future requirements for air navigational facilities and services of international civil aviation. ICAO periodically collects information from Stators and operates on anticipated future operations, consolidates this information, and forecasts the future level of activity at different airports.
Congressional Election Forecasting.
ERIC Educational Resources Information Center
Lewis-Beck, Michael S.; Rice, Tom W.
Reviews the growing literature on the forecasting of elections, providing an example in the form of 1988 congressional election predictions. Briefly discusses the history of election outcome prediction and outlines two scientific forecasting models which, the authors state, are appropriate for use in the classroom. (GEA)
A global non-hydrostatic weather forecast model in KIAPS using the spectral element on a cubed sphere
NASA Astrophysics Data System (ADS)
Choi, Suk-Jin; Lee, Eun-Hee; Hong, Song-You
This presentation covers an introduction to the current state of a non-hydrostatic global atmospheric model to be named the KIAPS integrated model (KIM). Efforts to resolve an excessive dissipation in small scales in KIM will be discussed. Also, simulated results for several idealized benchmark tests and full-physics forecasts will be shown. The dynamical core of the model is using the Euler equation set in a flux form based on the terrain following mass-based vertical coordinate, which is discretized by horizontal spectral element method (SEM) and the vertical finite difference method (FDM) for the spatial discretization and a time-split third-order Runge-Kutta (RK3) for the time discretization. Owing to the virtue of SEM and the explicit time integrator, KIM can achieve easily a high level of scalability. The physics package coupled with the dynamical core is a standard physics package from existing models such as the GRIMs, WRF, and GFS.
Statistical evaluation of forecasts
NASA Astrophysics Data System (ADS)
Mader, Malenka; Mader, Wolfgang; Gluckman, Bruce J.; Timmer, Jens; Schelter, Björn
Reliable forecasts of extreme but rare events, such as earthquakes, financial crashes, and epileptic seizures, would render interventions and precautions possible. Therefore, forecasting methods have been developed which intend to raise an alarm if an extreme event is about to occur. In order to statistically validate the performance of a prediction system, it must be compared to the performance of a random predictor, which raises alarms independent of the events. Such a random predictor can be obtained by bootstrapping or analytically. We propose an analytic statistical framework which, in contrast to conventional methods, allows for validating independently the sensitivity and specificity of a forecasting method. Moreover, our method accounts for the periods during which an event has to remain absent or occur after a respective forecast.
NASA Astrophysics Data System (ADS)
Hsieh, S. W.; Zhang, Y.; Schaefer, R. K.; Romeo, G.; Paxton, L.
A new capability has been developed at JHU/APL for forecasting the global aurora quantities based on the DMSP SSUSI data and the TIMED/GUVI Global Aurora Model. The SSUSI Aurora Forecast Model predicts the electron energy flux, mean energy, and equatorward boundary in the auroral oval for up to 1 day or 15 DMSP orbits in advance. In our presentation, we will demonstrate this newly implemented capability and its results. The future improvement plan will be discussed too.
Precipitation and temperature ensemble forecasts from single-value forecasts
NASA Astrophysics Data System (ADS)
Schaake, J.; Demargne, J.; Hartman, R.; Mullusky, M.; Welles, E.; Wu, L.; Herr, H.; Fan, X.; Seo, D. J.
A procedure is presented to construct ensemble forecasts from single-value forecasts of precipitation and temperature. This involves dividing the spatial forecast domain and total forecast period into a number of parts that are treated as separate forecast events. The spatial domain is divided into hydrologic sub-basins. The total forecast period is divided into time periods, one for each model time step. For each event archived values of forecasts and corresponding observations are used to model the joint distribution of forecasts and observations. The conditional distribution of observations for a given single-value forecast is used to represent the corresponding probability distribution of events that may occur for that forecast. This conditional forecast distribution subsequently is used to create ensemble members that vary in space and time using the "Schaake Shuffle" (Clark et al, 2004). The resulting ensemble members have the same space-time patterns as historical observations so that space-time joint relationships between events that have a significant effect on hydrological response tend to be preserved. Forecast uncertainty is space and time-scale dependent. For a given lead time to the beginning of the valid period of an event, forecast uncertainty depends on the length of the forecast valid time period and the spatial area to which the forecast applies. Although the "Schaake Shuffle" procedure, when applied to construct ensemble members from a time-series of single value forecasts, may preserve some of this scale dependency, it may not be sufficient without additional constraint. To account more fully for the time-dependent structure of forecast uncertainty, events for additional "aggregate" forecast periods are defined as accumulations of different "base" forecast periods. The generated ensemble members can be ingested by an Ensemble Streamflow Prediction system to produce ensemble forecasts of streamflow and other hydrological variables that reflect
An overview of health forecasting.
Soyiri, Ireneous N; Reidpath, Daniel D
Health forecasting is a novel area of forecasting, and a valuable tool for predicting future health events or situations such as demands for health services and healthcare needs. It facilitates preventive medicine and health care intervention strategies, by pre-informing health service providers to take appropriate mitigating actions to minimize risks and manage demand. Health forecasting requires reliable data, information and appropriate analytical tools for the prediction of specific health conditions or situations. There is no single approach to health forecasting, and so various methods have often been adopted to forecast aggregate or specific health conditions. Meanwhile, there are no defined health forecasting horizons (time frames) to match the choices of health forecasting methods/approaches that are often applied. The key principles of health forecasting have not also been adequately described to guide the process. This paper provides a brief introduction and theoretical analysis of health forecasting. It describes the key issues that are important for health forecasting, including: definitions, principles of health forecasting, and the properties of health data, which influence the choices of health forecasting methods. Other matters related to the value of health forecasting, and the general challenges associated with developing and using health forecasting services are discussed. This overview is a stimulus for further discussions on standardizing health forecasting approaches and methods that will facilitate health care and health services delivery. PMID:22949173
Forecasting digital microcircuit obsolescence
NASA Astrophysics Data System (ADS)
Balwally, Nandakumar M.
This report documents a procedure for forecasting digital microcircuit obsolescence at the Defense Electronics Supply Center, Dayton, OH. Obsolescence is caused by rapid advancement in digital technology and decrease in commercial demand while military demand still continues. In logistics parlance, parts obsolescence is known as a diminishing manufacturing source (DMS) problem. Continued supply of an obsolete DMS item is assured via substitution, alternate sourcing or a one time buy equal to the lifetime requirements of the item. Emulation is a recent alternative which explores the possibility of replacing obsolete digital microcircuits with state of the art devices which can be manufactured and supplied on demand. The report recommends use of a statistical model which forecasts DMS items from a population of presently non-DMS items belonging to obsolete digital microcircuit technologies. The items forecast by the model should be evaluated for their emulation potential.
How Grim is Pancreatic Cancer?
Weledji, Elroy Patrick; Enoworock, George; Mokake, Martin; Sinju, Motaze
Pancreatic ductal carcinoma continues to be the most lethal malignancy with rising incidence. It is the fourth most common cause of cancer death in the western world due to its low treatment success rate. In addition, because of its rapid growth and silent course, diagnosis is often only established in the advanced stages. As one of the most aggressive malignancies, the treatment of this disease is a great challenge to clinicians. This paper reviewed the natural history of pancreatic cancer, the current clinical practice and the future in pancreatic cancer management. PMID:27471581
While Asia was the last region to be exposed to the global spread of HIV and AIDS, the incidence of HIV infection there is increasing fastest. The Asian Development Bank predicts mortality from AIDS will cause some town and village populations to begin declining by the year 2000. With an estimated 1 million people infected in India, and 400,000 in Thailand, these 2 countries are particularly exposed to the risk of epidemic HIV spread. In 5 years, more people may be affected by AIDS in India than anywhere else in the world. Concern over a growing presence of HIV is also merited for the Philippines, Indonesia, China, and the drug trade's Golden Triangle. The Second International Conference on AIDS in Asia and the Pacific in November 1992 stressed that AIDS no longer affects only homosexual and IV drug using populations. 50% of new infections worldwide in the first half of 1992 were among women, 65% of Thailand's AIDS cases are among heterosexuals, and 3-5% of Thailand's long-haul truck drivers have tested positive for HIV infection. HIV and AIDS robs economies and societies of their best workers. The immediate costs of caring for AIDS patients will pale next to the far greater losses to be realized in private sector economic productivity. Asia's more developed economies will probably be able to survive the epidemic, but small, poor countries like Laos will wilt. Prompt action must be taken to overcome public and religious ignorance and objections to promoting and using condoms throughout the region. For the first time, Beijing has organized an AIDS awareness conference for male homosexuals. Further, Singapore has implemented compulsory testing for lower-income foreign workers. Pakistan has even solicited educational assistance and support from Islamic religious leaders; similar action is being considered in Bangladesh. PMID:12285939
NASA Astrophysics Data System (ADS)
Maximova, Lyudmila
For the first time Soviet scientists have set up a bioseismological proving ground which will stage a systematic extensive experiment of using birds, ants, mountain rodents including marmots, which can dig holes in the Earth's interior to a depth of 50 meters, for the purpose of earthquake forecasting. Biologists have accumulated extensive experimental data on the impact of various electromagnetic fields, including fields of weak intensity, on living organisms. As far as mammals are concerned, electromagnetic waves with frequencies close to the brain's biorhythms have the strongest effect. How these observations can be used to forecast earthquakes is discussed.
NASA Astrophysics Data System (ADS)
Zuurendonk, I. W.; Wokke, M. J. J.
Road surface temperatures can differ several degrees on a very short distance due to local effects. In order to get more insight in the local temperature differences and to develop safer gritting routes, Meteogroup has developed a system for route based temperature forecasting. The standard version of the road model is addressed to forecast road surface temperature and condition for a specific location. This model consists of two parts. First a physical part, based on the energy balance equations. The second part of the model performs a statistical correction on the calculated physical road surface temperature. The road model is able to create a forecast for one specific location. From infrared measurements, we know that large local differences in road surface temperature exist on a route. Differences can be up to 5 degrees Celsius over a distance of several hundreds of meters. Based on those measurements, the idea came up to develop a system that forecasts road surface temperature and condition for an entire route: route based forecasting. The route is split up in sections with equal properties. For each section a temperature and condition will be calculated. The main factors that influence the road surface temperature are modelled in this forecasting system: •The local weather conditions: temperature, dew point temperature, wind, precipitation, weather type, cloudiness. •The sky view: A very sheltered place will receive less radiation during daytime and emit less radiation during nighttime. For a very open spot, the effects are reversed. •The solar view: A road section with trees on the southern side, will receive less solar radiation during daytime than a section with tress on the southern side. The route based forecast shows by means of a clear Google Maps presentation which sections will be slippery at what time of the coming night. The final goal of this type of forecast, is to make dynamical gritting possible: a variable salt amount and a different
Developing air quality forecasts
NASA Astrophysics Data System (ADS)
Lee, Pius; Saylor, Rick; Meagher, James
Third International Workshop on Air Quality Forecasting Research; Potomac, Maryland, 29 November to 1 December 2011 Elevated concentrations of both near-surface ozone (O3) and fine particulate matter smaller than 2.5 micrometers in diameter have been implicated in increased mortality and other human health impacts. In light of these known influences on human health, many governments around the world have instituted air quality forecasting systems to provide their citizens with advance warning of impending poor air quality so that they can take actions to limit exposure. In an effort to improve the performance of air quality forecasting systems and provide a forum for the exchange of the latest research in air quality modeling, the International Workshop on Air Quality Forecasting Research (IWAQFR) was established in 2009 and is cosponsored by the U.S. National Oceanic and Atmospheric Administration (NOAA), Environment Canada (EC), and the World Meteorological Organization (WMO). The steering committee for IWAQFR's establishment was composed of Véronique Bouchet, Mike Howe, and Craig Stoud (EC); Greg Carmichael (University of Iowa); Paula Davidson and Jim Meagher (NOAA); and Liisa Jalkanen (WMO). The most recent workshop took place in Maryland.
ERIC Educational Resources Information Center
Paradiso, James; Stair, Kenneth
Intended to provide insight into the dynamics of demand analysis, this paper presents an eight-step method for forecasting sales. Focusing on sales levels that must be achieved to enjoy targeted profits in favor of the usual approach of emphasizing how much will be sold within a given period, a sample situation is provided to illustrate this…
Forecasting Scientific - Technical Information.
ERIC Educational Resources Information Center
Vvedenskiy, T. A.; And Others
This document contains three selections from the Russian-language journal "Nauchno-Teknicheskaya Informatsiya," Moscow. The first article is "Documentation for Technical Forecasts" by T. A. Vvedenskiy (Series 1, Number 11, 1969, submitted for publication 9 July 1968, p3-5). This article deals with the transformation of the method of scientific…
Improving operational plume forecasts
NASA Astrophysics Data System (ADS)
Balcerak, Ernie
Forecasting how plumes of particles, such as radioactive particles from a nuclear disaster, will be transported and dispersed in the atmosphere is an important but computationally challenging task. During the Fukushima nuclear disaster in Japan, operational plume forecasts were produced each day, but as the emissions continued, previous emissions were not included in the simulations used for forecasts because it became impractical to rerun the simulations each day from the beginning of the accident. Draxler and Rolph examine whether it is possible to improve plume simulation speed and flexibility as conditions and input data change. The authors use a method known as a transfer coefficient matrix approach that allows them to simulate many radionuclides using only a few generic species for the computation. Their simulations work faster by dividing the computation into separate independent segments in such a way that the most computationally time consuming pieces of the calculation need to be done only once. This makes it possible to provide real-time operational plume forecasts by continuously updating the previous simulations as new data become available. They tested their method using data from the Fukushima incident to show that it performed well. (Journal of Geophysical Research-Atmospheres, doi:10.1029/2011JD017205, 2012)
Forecasting Mass Communication.
ERIC Educational Resources Information Center
Dailey, Joseph M.
In sorting through predictions about future communications, it should be kept in mind that if one can think of a communication technology in the future, then that communication technology will stand a very good chance of becoming a reality. In other words, the forecasting of invention is not separate from invention itself. Secondly, the inventions…
External Environmental Forecast.
ERIC Educational Resources Information Center
Lapin, Joel D.
Representing current viewpoints of academics, futures experts, and social observers, this external environmental forecast presents projections and information of particular relevance to the future of Catonsville Community College. The following topics are examined: (1) population changes and implications for higher education; (2) state and local…
Federal Forecasters Directory, 1995.
ERIC Educational Resources Information Center
National Center for Education Statistics (ED), Washington, DC.
This directory lists employees of the federal government who are involved in forecasting for policy formation and trend prediction purposes. Job title, agency, business address, phone or e-mail number, and specialty areas are listed for each employee. Employees are listed for the following agencies: (1) Bureau of the Census; (2) Bureau of Economic…
ERIC Educational Resources Information Center
Bivin, David; Rooney, Patrick Michael
This study used Tobit analysis to estimate retention probabilities and credit hours at two universities. Tobit was judged as appropriate for this problem because it recognizes the lower bound of zero on credit hours and incorporates this bound into parameter estimates and forecasts. Models are estimated for credit hours in a single year and…
Corporate Forecasting: Promise and Reality
ERIC Educational Resources Information Center
Wheelwright, Steven C.; Clarke, Darral G.
Discusses a survey of forecast preparers and users in 127 major companies in an attempt to assess underlying problems and identify areas for improvement. Concludes that forecasting responsibilities and tasks must be better defined and that forecast preparers and users must become better informed about one another's roles. (Author/JG)
Forecasting Infectious Disease Outbreaks
NASA Astrophysics Data System (ADS)
Shaman, J. L.
Dynamic models of infectious disease systems abound and are used to study the epidemiological characteristics of disease outbreaks, the ecological mechanisms affecting transmission, and the suitability of various control and intervention strategies. The dynamics of disease transmission are non-linear and consequently difficult to forecast. Here, we describe combined model-inference frameworks developed for the prediction of infectious diseases. We show that accurate and reliable predictions of seasonal influenza outbreaks can be made using a mathematical model representing population-level influenza transmission dynamics that has been recursively optimized using ensemble data assimilation techniques and real-time estimates of influenza incidence. Operational real-time forecasts of influenza and other infectious diseases have been and are currently being generated.
Forecasting carbon dioxide emissions.
Zhao, Xiaobing; Du, Ding
This study extends the literature on forecasting carbon dioxide (CO2) emissions by applying the reduced-form econometrics approach of Schmalensee et al. (1998) to a more recent sample period, the post-1997 period. Using the post-1997 period is motivated by the observation that the strengthening pace of global climate policy may have been accelerated since 1997. Based on our parameter estimates, we project 25% reduction in CO2 emissions by 2050 according to an economic and population growth scenario that is more consistent with recent global trends. Our forecasts are conservative due to that we do not have sufficient data to fully take into account recent developments in the global economy. PMID:26081307
NASA Astrophysics Data System (ADS)
Turco, M.; Milelli, M.
To the authors' knowledge there are relatively few studies that try to answer this topic: "Are humans able to add value to computer-generated forecasts and warnings ?". Moreover, the answers are not always positive. In particular some postprocessing method is competitive or superior to human forecast (see for instance Baars et al., 2005, Charba et al., 2002, Doswell C., 2003, Roebber et al., 1996, Sanders F., 1986). Within the alert system of ARPA Piemonte it is possible to study in an objective manner if the human forecaster is able to add value with respect to computer-generated forecasts. Every day the meteorology group of the Centro Funzionale of Regione Piemonte produces the HQPF (Human QPF) in terms of an areal average for each of the 13 regional warning areas, which have been created according to meteo-hydrological criteria. This allows the decision makers to produce an evaluation of the expected effects by comparing these HQPFs with predefined rainfall thresholds. Another important ingredient in this study is the very dense non-GTS network of rain gauges available that makes possible a high resolution verification. In this context the most useful verification approach is the measure of the QPF and HQPF skills by first converting precipitation expressed as continuous amounts into ''exceedance'' categories (yes-no statements indicating whether precipitation equals or exceeds selected thresholds) and then computing the performances for each threshold. In particular in this work we compare the performances of the latest three years of QPF derived from two meteorological models COSMO-I7 (the Italian version of the COSMO Model, a mesoscale model developed in the framework of the COSMO Consortium) and IFS (the ECMWF global model) with the HQPF. In this analysis it is possible to introduce the hypothesis test developed by Hamill (1999), in which a confidence interval is calculated with the bootstrap method in order to establish the real difference between the
Satellite freeze forecast system
NASA Technical Reports Server (NTRS)
Martsolf, J. D. (Principal Investigator)
Provisions for back-up operations for the satellite freeze forecast system are discussed including software and hardware maintenance and DS/1000-1V linkage; troubleshooting; and digitized radar usage. The documentation developed; dissemination of data products via television and the IFAS computer network; data base management; predictive models; the installation of and progress towards the operational status of key stations; and digital data acquisition are also considered. The d addition of dew point temperature into the P-model is outlined.
Uranium price forecasting methods
Fuller, D.M.
This article reviews a number of forecasting methods that have been applied to uranium prices and compares their relative strengths and weaknesses. The methods reviewed are: (1) judgemental methods, (2) technical analysis, (3) time-series methods, (4) fundamental analysis, and (5) econometric methods. Historically, none of these methods has performed very well, but a well-thought-out model is still useful as a basis from which to adjust to new circumstances and try again.
Frost Forecasting for Fruitgrowers
NASA Technical Reports Server (NTRS)
Martsolf, J. D.; Chen, E.
Progress in forecasting from satellite data reviewed. University study found data from satellites displayed in color and used to predict frost are valuable aid to agriculture. Study evaluated scheme to use Earth-temperature data from Geostationary Operational Environmental Satellite in computer model that determines when and where freezing temperatures endanger developing fruit crops, such as apples, peaches and cherries in spring and citrus crops in winter.
Probabilistic forecast for climate change over Northern Eurasia
NASA Astrophysics Data System (ADS)
Sokolov, Andrei; Monier, Erwan; Kicklighter, David; Scott, Jeffrey; Gao, Xiang; Schlosser, Adam
In this study, we investigate possible climate change over Northern Eurasia and its impact on hydrological and carbon cycles. Northern Eurasia is a major player in the global carbon budget because of boreal forests and wetlands. Permafrost degradation associated with climate change could result in wetlands releasing large amounts of carbon dioxide and methane. Changes in the frequency and magnitude of extreme events, such as extreme precipitation, are likely to have substantial impacts on Northern Eurasia ecosystems. For this reason, it is very important to quantify the possible climate change over Northern Eurasia under different emissions scenarios, while accounting for the uncertainty in the climate response. For several decades, the Massachusetts Institute of Technology (MIT) Joint Program on the Science and Policy of Global Change has been investigating uncertainty in climate change using the MIT Integrated Global System Model (IGSM) framework, an integrated assessment model that couples an earth system model of intermediate complexity (with a 2D zonal-mean atmosphere) to a human activity model. Since the IGSM includes a human activity model, it is possible to analyze uncertainties in emissions resulting, for example, from different future climate policies. Another major feature is the flexibility to vary key climate parameters controlling the climate response: climate sensitivity, net aerosol forcing and ocean heat uptake rate. The IGSM has long been used to perform probabilistic forecasts based on estimates of probability density functions of climate parameters. The MIT IGSM-CAM framework links the IGSM to the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM), with new modules developed and implemented in CAM to allow climate parameters to be changed to match those of the IGSM. The simulations discussed in this paper were carried out for two emission scenarios and three sets of climate parameters. The "business as usual" and a
NASA Technical Reports Server (NTRS)
Case, Jonathan L.; Wheeler, Mark M.; Merceret, Francis J. (Technical Monitor)
The nocturnal land breeze at the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) is both operationally significant and challenging to forecast. The occurrence and timing of land breezes impact low-level winds, atmospheric stability, low temperatures, and fog development. Accurate predictions of the land breeze are critical for toxic material dispersion forecasts associated with space launch missions, since wind direction and low-level stability can change noticeably with the onset of a land breeze. This report presents a seven-year observational study of land breezes over east-central Florida from 1995 to 2001. This comprehensive analysis was enabled by the high-resolution tower observations over KSC/CCAFS. Five-minute observations of winds, temperature, and moisture along with 9 15-MHz Doppler Radar Wind Profiler data were used to analyze specific land-breeze cases, while the tower data were used to construct a composite climatology. Utilities derived from this climatology were developed to assist forecasters in determining the land-breeze occurrence, timing, and movement based on predicted meteorological conditions.
NASA Technical Reports Server (NTRS)
Macdonald, R. B.; Hall, F. G.
The needs for and remote sensing means of global crop forecasting are discussed, and key results of the Large Area Crop Inventory Experiment (LACIE) are presented. Current crop production estimates provided by foreign countries are shown often to be inadequate, and the basic elements of crop production forecasts are reviewed. The LACIE project is introduced as a proof-of-concept experiment designed to assimilate remote sensing technology, monitor global wheat production, evaluate key technical problems, modify the technique accordingly and demonstrate the feasibility of a global agricultural monitoring system. The global meteorological data, sampling and aggregation techniques, Landsat data analysis procedures and yield forecast procedures used in the experiment are outlined. Accuracy assessment procedures employed to evaluate LACIE technology performance are presented, and improvements in system efficiency and capacity during the three years of operation are pointed out. Results of LACIE estimates of Soviet, U.S. and Canadian wheat production are presented which demonstrate the feasibility and accuracy of the remote-sensing approach for global food and fiber monitoring.
NASA Astrophysics Data System (ADS)
Meng, C.; Wing, S.; Johnson, J. R.; Jen, J.; Carr, S.; Sibeck, D. G.; Costello, K.; Freeman, J.; Balikhin, M.; Bechtold, K.; Vandegriff, J.
Magnetically active times, e.g., Kp > 5, are notoriously difficult to predict, precisely when the predictions are crucial to the space weather users. Taking advantage of the routinely available solar wind measurements at Langrangian point (L1) and nowcast Kps, Kp forecast models based on neural networks were developed with the focus on improving the forecast for active times. In order to satisfy different needs and operational constraints, three models were developed: (1) model that inputs nowcast Kp, solar wind parameters, and predict Kp 1 hr ahead; (2) model with the same input as (1) and predict Kp 4 hr ahead; and (3) model that inputs only solar wind parameters and predict Kp 1 hr ahead (the exact prediction lead time depends on the solar wind speed and the location of the solar wind monitor). Extensive evaluations of these models and other major operational Kp forecast models show that while the new models can predict Kps more accurately for all activities, the most dramatic improvements occur for moderate and active times. The evaluations of the models over 2 solar cycles, 1975-2001, show that solar wind driven models predict Kp more accurately during solar maximum than solar minimum. This result, as well as information dynamics analysis of Kp, suggests that geospace is more dominated by internal dynamics during solar minimum than solar maximum, when it is more directly driven by external inputs, namely solar wind and IMF.
NASA Astrophysics Data System (ADS)
Wing, S.; Johnson, J. R.; Jen, J.; Meng, C.-I.; Sibeck, D. G.; Bechtold, K.; Freeman, J.; Costello, K.; Balikhin, M.; Takahashi, K.
Magnetically active times, e.g., Kp > 5, are notoriously difficult to predict, precisely the times when such predictions are crucial to the space weather users. Taking advantage of the routinely available solar wind measurements at Langrangian point (L1) and nowcast Kps, Kp forecast models based on neural networks were developed with the focus on improving the forecast for active times. To satisfy different needs and operational constraints, three models were developed: (1) a model that inputs nowcast Kp and solar wind parameters and predicts Kp 1 hour ahead; (2) a model with the same input as model 1 and predicts Kp 4 hour ahead; and (3) a model that inputs only solar wind parameters and predicts Kp 1 hour ahead (the exact prediction lead time depends on the solar wind speed and the location of the solar wind monitor). Extensive evaluations of these models and other major operational Kp forecast models show that while the new models can predict Kps more accurately for all activities, the most dramatic improvements occur for moderate and active times. Information dynamics analysis of Kp suggests that geospace is more dominated by internal dynamics near solar minimum than near solar maximum, when it is more directly driven by external inputs, namely solar wind and interplanetary magnetic field (IMF).
NASA Astrophysics Data System (ADS)
Wing, S.; Johnson, J. R.; Meng, C.; Takahashi, K.
Magnetically active times, e.g., Kp > 5, are notoriously difficult to predict, precisely the times when such predictions are crucial to the space weather users. Taking advantage of the routinely available solar wind measurements at Langrangian point (L1) and nowcast Kps, Kp forecast models based on neural networks were developed with the focus on improving the forecast for active times. To satisfy different needs and operational constraints, three models were developed: (1) a model that inputs nowcast Kp and solar wind parameters and predicts Kp 1 hr ahead; (2) a model with the same input as model 1 and predicts Kp 4 hr ahead; and (3) a model that inputs only solar wind parameters and predicts Kp 1 hr ahead (the exact prediction lead time depends on the solar wind speed and the location of the solar wind monitor.) Extensive evaluations of these models and other major operational Kp forecast models show that, while the new models can predict Kps more accurately for all activities, the most dramatic improvements occur for moderate and active times. Information dynamics analysis of Kp, suggests that geospace is more dominated by internal dynamics near solar minimum than near solar maximum, when it is more directly driven by external inputs, namely solar wind and interplanetary magnetic field (IMF).
Forecasting geomagnetic activity indices
NASA Astrophysics Data System (ADS)
Schofield, J.; Wing, S.; Johnson, J. R.
Magnetically active times, e.g., Kp > 5, are notoriously difficult to predict, precisely the times when such predictions are crucial to the space weather users. Taking advantage of the routinely available solar wind measurements at Langrangian point (L1) and nowcast Kps, Kp and Dst forecast models based on neural networks were developed with the focus on improving the forecast for active times. To satisfy different needs and operational constraints, three models were developed: (1) a model that inputs nowcast Kp and solar wind parameters and predicts Kp 1 hr ahead; (2) a model with the same input as model 1 and predicts Kp 4 hr ahead; and (3) a model that inputs only solar wind parameters and predicts Kp 1 hr ahead (the exact prediction lead time depends on the solar wind speed and the location of the solar wind monitor.) Extensive evaluations of these models and other major operational Kp forecast models show that, while the new models can predict Kps more accurately for all activities, the most dramatic improvements occur for moderate and active times. Similar Dst models were developed. Information dynamics analysis of Kp, suggests that geospace is more dominated by internal dynamics near solar minimum than near solar maximum, when it is more directly driven by external inputs, namely solar wind and interplanetary magnetic field (IMF).
EU pharmaceutical expenditure forecast
Urbinati, Duccio; Rémuzat, Cécile; Kornfeld, Åsa; Vataire, Anne-Lise; Cetinsoy, Laurent; Aballéa, Samuel; Mzoughi, Olfa; Toumi, Mondher
Background and Objectives With constant incentives for healthcare payers to contain their pharmaceutical budgets, forecasting has become critically important. Some countries have, for instance, developed pharmaceutical horizon scanning units. The objective of this project was to build a model to assess the net effect of the entrance of new patented medicinal products versus medicinal products going off-patent, with a defined forecast horizon, on selected European Union (EU) Member States' pharmaceutical budgets. This model took into account population ageing, as well as current and future country-specific pricing, reimbursement, and market access policies (the project was performed for the European Commission; see http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). Method In order to have a representative heterogeneity of EU Member States, the following countries were selected for the analysis: France, Germany, Greece, Hungary, Poland, Portugal, and the United Kingdom. A forecasting period of 5 years (2012–2016) was chosen to assess the net pharmaceutical budget impact. A model for generics and biosimilars was developed for each country. The model estimated a separate and combined effect of the direct and indirect impacts of the patent cliff. A second model, estimating the sales development and the risk of development failure, was developed for new drugs. New drugs were reviewed individually to assess their clinical potential and translate it into commercial potential. The forecast was carried out according to three perspectives (healthcare public payer, society, and manufacturer), and several types of distribution chains (retail, hospital, and combined retail and hospital). Probabilistic and deterministic sensitivity analyses were carried out. Results According to the model, all countries experienced drug budget reductions except Poland (+€41 million). Savings were expected to be the highest in the United Kingdom (−€9,367 million), France
Consistency among microphysics-convection-radiation processes in a numerical forecasting model
NASA Astrophysics Data System (ADS)
Bae, Soo Ya; Park, Raeseol; Hong, Song-You
Radiative fluxes are mainly affected by the cloud optical properties calculated with effective radius, water path of hydrometeors, and cloud fraction. A prognostic cloud fraction scheme, which considers the cloud fraction with increments as a result of each physics process, is implemented in the Global/Regional Integrated Model system (GRIMs) (Park et al., 2016). However, the original RRTMG scheme does not consider the hydrometeor information from convection processes, resulting in inconsistency between cloud process and radiation activity. To ensure consistency among physics processes, the amount of hydrometeors from both the cumulus parameterization scheme (CPS) and microphysics schemes is explicitly taken into account in computing radiative fluxes. The effects of this modification are tested for a heavy rainfall over Korea to identify the feedback between the precipitation and radiation processes. It is found that the information of hydrometeors from CPS tends to increase water path, which leads to larger cloud optical depth and cooling. Skill scores of the simulated precipitation in a medium-range forecast testbed confirm benefits of the consistent treatment of hydrometeors in both CPS and radiation processes.
Hydrological Forecasting Practices in Brazil
NASA Astrophysics Data System (ADS)
Fan, Fernando; Paiva, Rodrigo; Collischonn, Walter; Ramos, Maria-Helena
This work brings a review on current hydrological and flood forecasting practices in Brazil, including the main forecasts applications, the different kinds of techniques that are currently being employed and the institutions involved on forecasts generation. A brief overview of Brazil is provided, including aspects related to its geography, climate, hydrology and flood hazards. A general discussion about the Brazilian practices on hydrological short and medium range forecasting is presented. Detailed examples of some hydrological forecasting systems that are operational or in a research/pre-operational phase using the large scale hydrological model MGB-IPH are also presented. Finally, some suggestions are given about how the forecasting practices in Brazil can be understood nowadays, and what are the perspectives for the future.
Operational hydrological forecasting in Bavaria. Part I: Forecast uncertainty
NASA Astrophysics Data System (ADS)
Ehret, U.; Vogelbacher, A.; Moritz, K.; Laurent, S.; Meyer, I.; Haag, I.
In Bavaria, operational flood forecasting has been established since the disastrous flood of 1999. Nowadays, forecasts based on rainfall information from about 700 raingauges and 600 rivergauges are calculated and issued for nearly 100 rivergauges. With the added experience of the 2002 and 2005 floods, awareness grew that the standard deterministic forecast, neglecting the uncertainty associated with each forecast is misleading, creating a false feeling of unambiguousness. As a consequence, a system to identify, quantify and communicate the sources and magnitude of forecast uncertainty has been developed, which will be presented in part I of this study. In this system, the use of ensemble meteorological forecasts plays a key role which will be presented in part II. Developing the system, several constraints stemming from the range of hydrological regimes and operational requirements had to be met: Firstly, operational time constraints obviate the variation of all components of the modeling chain as would be done in a full Monte Carlo simulation. Therefore, an approach was chosen where only the most relevant sources of uncertainty were dynamically considered while the others were jointly accounted for by static error distributions from offline analysis. Secondly, the dominant sources of uncertainty vary over the wide range of forecasted catchments: In alpine headwater catchments, typically of a few hundred square kilometers in size, rainfall forecast uncertainty is the key factor for forecast uncertainty, with a magnitude dynamically changing with the prevailing predictability of the atmosphere. In lowland catchments encompassing several thousands of square kilometers, forecast uncertainty in the desired range (usually up to two days) is mainly dependent on upstream gauge observation quality, routing and unpredictable human impact such as reservoir operation. The determination of forecast uncertainty comprised the following steps: a) From comparison of gauge
Solar Indices Forecasting Tool
NASA Astrophysics Data System (ADS)
Henney, Carl John; Shurkin, Kathleen; Arge, Charles; Hill, Frank
Progress to forecast key space weather parameters using SIFT (Solar Indices Forecasting Tool) with the ADAPT (Air Force Data Assimilative Photospheric flux Transport) model is highlighted in this presentation. Using a magnetic flux transport model, ADAPT, we estimate the solar near-side field distribution that is used as input into empirical models for predicting F10.7(solar 10.7 cm, 2.8 GHz, radio flux), the Mg II core-to-wing ratio, and selected bands of solar far ultraviolet (FUV) and extreme ultraviolet (EUV) irradiance. Input to the ADAPT model includes the inferred photospheric magnetic field from the NISP ground-based instruments, GONG & VSM. Besides a status update regarding ADAPT and SIFT models, we will summarize the findings that: 1) the sum of the absolute value of strong magnetic fields, associated with sunspots, is shown to correlate well with the observed daily F10.7 variability (Henney et al. 2012); and 2) the sum of the absolute value of weak magnetic fields, associated with plage regions, is shown to correlate well with EUV and FUV irradiance variability (Henney et al. 2015). This work utilizes data produced collaboratively between Air Force Research Laboratory (AFRL) and the National Solar Observatory (NSO). The ADAPT model development is supported by AFRL. The input data utilized by ADAPT is obtained by NISP (NSO Integrated Synoptic Program). NSO is operated by the Association of Universities for Research in Astronomy (AURA), Inc., under a cooperative agreement with the National Science Foundation (NSF). The 10.7 cm solar radio flux data service, utilized by the ADAPT/SIFT F10.7 forecasting model, is operated by the National Research Council of Canada and National Resources Canada, with the support of the Canadian Space Agency.
Turnipseed, D. Phil
In 2009, U.S. Secretary of State Hillary R. Clinton joined with the Foreign Ministers of Cambodia, Laos, Thailand, and Vietnam in launching the Lower Mekong Initiative to enhance U.S. engagement with the Lower Mekong countries in the areas of environment, health, education, and infrastructure. Part of the Lower Mekong Initiative, the U.S. Geological Survey's Forecast Mekong project is engaging the United States in scientific research relevant to environmental issues in the Lower Mekong River countries and is staying the course in support of the Mekong Nations with a suite of new projects for 2011.
Forecasting in Complex Systems
NASA Astrophysics Data System (ADS)
Rundle, J. B.; Holliday, J. R.; Graves, W. R.; Turcotte, D. L.; Donnellan, A.
Complex nonlinear systems are typically characterized by many degrees of freedom, as well as interactions between the elements. Interesting examples can be found in the areas of earthquakes and finance. In these two systems, fat tails play an important role in the statistical dynamics. For earthquake systems, the Gutenberg-Richter magnitude-frequency is applicable, whereas for daily returns for the securities in the financial markets are known to be characterized by leptokurtotic statistics in which the tails are power law. Very large fluctuations are present in both systems. In earthquake systems, one has the example of great earthquakes such as the M9.1, March 11, 2011 Tohoku event. In financial systems, one has the example of the market crash of October 19, 1987. Both were largely unexpected events that severely impacted the earth and financial systems systemically. Other examples include the M9.3 Andaman earthquake of December 26, 2004, and the Great Recession which began with the fall of Lehman Brothers investment bank on September 12, 2013. Forecasting the occurrence of these damaging events has great societal importance. In recent years, national funding agencies in a variety of countries have emphasized the importance of societal relevance in research, and in particular, the goal of improved forecasting technology. Previous work has shown that both earthquakes and financial crashes can be described by a common Landau-Ginzburg-type free energy model. These metastable systems are characterized by fat tail statistics near the classical spinodal. Correlations in these systems can grow and recede, but do not imply causation, a common source of misunderstanding. In both systems, a common set of techniques can be used to compute the probabilities of future earthquakes or crashes. In this talk, we describe the basic phenomenology of these systems and emphasize their similarities and differences. We also consider the problem of forecast validation and verification
NASA Technical Reports Server (NTRS)
Weather forecasters are usually very precise in reporting such conditions as temperature, wind velocity and humidity. They also provide exact information on barometric pressure at a given moment, and whether the barometer is "rising" or "falling"- but not how rapidly or how slowly it is rising or falling. Until now, there has not been available an instrument which measures precisely the current rate of change of barometric pressure. A meteorological instrument called a barograph traces the historical ups and downs of barometric pressure and plots a rising or falling curve, but, updated every three hours, it is only momentarily accurate at each updating.
Interactive Forecasting with the National Weather Service River Forecast System
NASA Technical Reports Server (NTRS)
Smith, George F.; Page, Donna
The National Weather Service River Forecast System (NWSRFS) consists of several major hydrometeorologic subcomponents to model the physics of the flow of water through the hydrologic cycle. The entire NWSRFS currently runs in both mainframe and minicomputer environments, using command oriented text input to control the system computations. As computationally powerful and graphically sophisticated scientific workstations became available, the National Weather Service (NWS) recognized that a graphically based, interactive environment would enhance the accuracy and timeliness of NWS river and flood forecasts. Consequently, the operational forecasting portion of the NWSRFS has been ported to run under a UNIX operating system, with X windows as the display environment on a system of networked scientific workstations. In addition, the NWSRFS Interactive Forecast Program was developed to provide a graphical user interface to allow the forecaster to control NWSRFS program flow and to make adjustments to forecasts as necessary. The potential market for water resources forecasting is immense and largely untapped. Any private company able to market the river forecasting technologies currently developed by the NWS Office of Hydrology could provide benefits to many information users and profit from providing these services.
Social Indicators and Social Forecasting.
ERIC Educational Resources Information Center
Johnston, Denis F.
The paper identifies major types of social indicators and explains how they can be used in social forecasting. Social indicators are defined as statistical measures relating to major areas of social concern and/or individual well being. Examples of social indicators are projections, forecasts, outlook statements, time-series statistics, and…
Regional-seasonal weather forecasting
Abarbanel, H.; Foley, H.; MacDonald, G.; Rothaus, O.; Rudermann, M.; Vesecky, J.
In the interest of allocating heating fuels optimally, the state-of-the-art for seasonal weather forecasting is reviewed. A model using an enormous data base of past weather data is contemplated to improve seasonal forecasts, but present skills do not make that practicable. 90 references. (PSB)
Forecasting School District Fiscal Health.
ERIC Educational Resources Information Center
Smith, Curtis A.
This paper's goal is to redefine fiscal health by broadening its predictive function and to determine which fiscal indicators are useful for forecasting fiscal health for one, two, and three years. Results indicate that school district fiscal health forecasts are potentially great planning tools for local for local decision makers. Includes 11…
The pioneers of weather forecasting
NASA Astrophysics Data System (ADS)
Ballard, Susan
In The Weather Experiment author Peter Moore takes us on a compelling journey through the early history of weather forecasting, bringing to life the personalities, lives and achievements of the men who put in place the building blocks required for forecasts to be possible.
Statistical Earthquake Focal Mechanism Forecasts
NASA Astrophysics Data System (ADS)
Kagan, Y. Y.; Jackson, D. D.
The new whole Earth focal mechanism forecast, based on the GCMT catalog, has been created. In the present forecast, the sum of normalized seismic moment tensors within 1000 km radius is calculated and the P- and T-axes for the focal mechanism are evaluated on the basis of the sum. Simultaneously we calculate an average rotation angle between the forecasted mechanism and all the surrounding mechanisms. This average angle shows tectonic complexity of a region and indicates the accuracy of the prediction. The method was originally proposed by Kagan and Jackson (1994, JGR). Recent interest by CSEP and GEM has motivated some improvements, particularly to extend the previous forecast to polar and near-polar regions. The major problem in extending the forecast is the focal mechanism calculation on a spherical surface. In the previous forecast as our average focal mechanism was computed, it was assumed that longitude lines are approximately parallel within 1000 km radius. This is largely accurate in the equatorial and near-equatorial areas. However, when one approaches the 75 degree latitude, the longitude lines are no longer parallel: the bearing (azimuthal) difference at points separated by 1000 km reach about 35 degrees. In most situations a forecast point where we calculate an average focal mechanism is surrounded by earthquakes, so a bias should not be strong due to the difference effect cancellation. But if we move into polar regions, the bearing difference could approach 180 degrees. In a modified program focal mechanisms have been projected on a plane tangent to a sphere at a forecast point. New longitude axes which are parallel in the tangent plane are corrected for the bearing difference. A comparison with the old 75S-75N forecast shows that in equatorial regions the forecasted focal mechanisms are almost the same, and the difference in the forecasted focal mechanisms rotation angle is close to zero. However, though the forecasted focal mechanisms are similar
Weather Forecasting Systems and Methods
NASA Technical Reports Server (NTRS)
Mecikalski, John (Inventor); MacKenzie, Wayne M., Jr. (Inventor); Walker, John Robert (Inventor)
A weather forecasting system has weather forecasting logic that receives raw image data from a satellite. The raw image data has values indicative of light and radiance data from the Earth as measured by the satellite, and the weather forecasting logic processes such data to identify cumulus clouds within the satellite images. For each identified cumulus cloud, the weather forecasting logic applies interest field tests to determine a score indicating the likelihood of the cumulus cloud forming precipitation and/or lightning in the future within a certain time period. Based on such scores, the weather forecasting logic predicts in which geographic regions the identified cumulus clouds will produce precipitation and/or lighting within during the time period. Such predictions may then be used to provide a weather map thereby providing users with a graphical illustration of the areas predicted to be affected by precipitation within the time period.
Atmospheric composition forecasting in Europe
NASA Astrophysics Data System (ADS)
Menut, L.; Bessagnet, B.
The atmospheric composition is a societal issue and, following new European directives, its forecast is now recommended to quantify the air quality. It concerns both gaseous and particles species, identified as potential problems for health. In Europe, numerical systems providing daily air quality forecasts are numerous and, mostly, operated by universities. Following recent European research projects (GEMS, PROMOTE), an organization of the air quality forecast is currently under development. But for the moment, many platforms exist, each of them with strengths and weaknesses. This overview paper presents all existing systems in Europe and try to identify the main remaining gaps in the air quality forecast knowledge. As modeling systems are now able to reasonably forecast gaseous species, and in a lesser extent aerosols, the future directions would concern the use of these systems with ensemble approaches and satellite data assimilation. If numerous improvements were recently done on emissions and chemistry knowledge, improvements are still needed especially concerning meteorology, which remains a weak point of forecast systems. Future directions will also concern the use of these forecast tools to better understand and quantify the air pollution impact on health.
Forecasts of geomagnetic secular variation
NASA Astrophysics Data System (ADS)
Wardinski, Ingo
We attempt to forecast the geomagnetic secular variation based on stochastic models, non-parametric regression and singular spectrum analysis of the observed past field changes. Although this modelling approach is meant to be phenomenological, it may provide some insight into the mechanisms underlying typical time scales of geomagnetic field changes. We follow two strategies to forecast secular variation: Firstly, by applying time series models, and secondly, by using time-dependent kinematic models of the advected secular variation. These forecasts can span decades, to longer periods. This depends on the length of the past observations used as input, with different input models leading to different details in the forecasts. These forecasts become more uncertain over longer forecasting periods. One appealing reason is the disregard of magnetic diffusion in the kinematic modelling. But also the interactions of unobservable small scale core field with core flow at all scale unsettle the kinematic forecasting scheme. A further (obvious) reason is that geomagnetic secular variation can not be mimicked by linear time series models as the dynamo action itself is highly non-linear. Whether the dynamo action can be represented by a simple low-dimensional system requires further analysis.
Characterizing the uncertainty in river stage forecasts conditional on point forecast values
NASA Astrophysics Data System (ADS)
Yan, Jun; Liao, Gong-Yi; Gebremichael, Mekonnen; Shedd, Robert; Vallee, David R.
Uncertainty information about river level forecast is as important as the forecast itself for forecast users. This paper presents a flexible, statistical approach that processes deterministic forecasts into probabilistic forecasts. The model is a smoothly changing conditional distribution of river stage given point forecast and other information available, such as lagged river level at the time of forecasting. The parametric distribution is a four-parameter skewt distribution, with each parameter modeled as a smooth function of the point forecast and the 1 day ago observed river level. The model was applied to 9 years of daily 6 h lead forecasts and 24 h lead forecasts in the warm season and their matching observations at the Plymouth station on the Pemigewasset River in New Hampshire. For each point forecast, the conditional distribution and resulting prediction intervals provide uncertainty information that are potentially very important to forecast users and algorithm developers in decision making and improvement of forecast quality.
Application of quantitative precipitation forecasting and precipitation ensemble prediction for hydrological forecasting
NASA Astrophysics Data System (ADS)
Tao, P.; Tie-Yuan, S.; Zhi-Yuan, Y.; Jun-Chao, W.
The precipitation in the forecast period influences flood forecasting precision, due to the uncertainty of the input to the hydrological model. Taking the ZhangHe basin as the example, the research adopts the precipitation forecast and ensemble precipitation forecast product of the AREM model, uses the Xin Anjiang hydrological model, and tests the flood forecasts. The results show that the flood forecast result can be clearly improved when considering precipitation during the forecast period. Hydrological forecast based on Ensemble Precipitation prediction gives better hydrological forecast information, better satisfying the need for risk information for flood prevention and disaster reduction, and has broad development opportunities.
Value of Wind Power Forecasting
Lew, D.; Milligan, M.; Jordan, G.; Piwko, R.
This study, building on the extensive models developed for the Western Wind and Solar Integration Study (WWSIS), uses these WECC models to evaluate the operating cost impacts of improved day-ahead wind forecasts.
Method Forecasts Global Energy Substitution
ERIC Educational Resources Information Center
Chemical and Engineering News, 1975
Describes a model developed to forecast energy demands and determine trends in demand for primary fuels. The energy model essentially considers primary energy sources as competing commodities in a market. (MLH)
Practical Meteor Stream Forecasting
NASA Technical Reports Server (NTRS)
Cooke, William J.; Suggs, Robert M.
Inspired by the recent Leonid meteor storms, researchers have made great strides in our ability to predict enhanced meteor activity. However, the necessary calibration of the meteor stream models with Earth-based ZHRs (Zenith Hourly Rates) has placed emphasis on the terran observer and meteor activity predictions are published in such a manner to reflect this emphasis. As a consequence, many predictions are often unusable by the satellite community, which has the most at stake and the greatest interest in meteor forecasting. This paper suggests that stream modelers need to pay more attention to the needs of this community and publish not just durations and times of maxima for Earth, but everything needed to characterize the meteor stream in and out of the plane of the ecliptic, which, at a minimum, consists of the location of maximum stream density (ZHR) and the functional form of the density decay with distance from this point. It is also suggested that some of the terminology associated with meteor showers may need to be more strictly defined in order to eliminate the perception of crying wolf by meteor scientists. An outburst is especially problematic, as it usually denotes an enhancement by a factor of 2 or more to researchers, but conveys the notion of a sky filled with meteors to satellite operators and the public. Experience has also taught that predicted ZHRs often lead to public disappointment, as these values vastly overestimate what is seen.
Preparing for an Uncertain Forecast
ERIC Educational Resources Information Center
Karolak, Eric
Navigating the world of government relations and public policy can be a little like predicting the weather. One can't always be sure what's in store or how it will affect him/her down the road. But there are common patterns and a few basic steps that can help one best prepare for a change in the forecast. Though the forecast is uncertain, early…
Municipal water consumption forecast accuracy
NASA Astrophysics Data System (ADS)
Fullerton, Thomas M.; Molina, Angel L.
Municipal water consumption planning is an active area of research because of infrastructure construction and maintenance costs, supply constraints, and water quality assurance. In spite of that, relatively few water forecast accuracy assessments have been completed to date, although some internal documentation may exist as part of the proprietary "grey literature." This study utilizes a data set of previously published municipal consumption forecasts to partially fill that gap in the empirical water economics literature. Previously published municipal water econometric forecasts for three public utilities are examined for predictive accuracy against two random walk benchmarks commonly used in regional analyses. Descriptive metrics used to quantify forecast accuracy include root-mean-square error and Theil inequality statistics. Formal statistical assessments are completed using four-pronged error differential regression F tests. Similar to studies for other metropolitan econometric forecasts in areas with similar demographic and labor market characteristics, model predictive performances for the municipal water aggregates in this effort are mixed for each of the municipalities included in the sample. Given the competitiveness of the benchmarks, analysts should employ care when utilizing econometric forecasts of municipal water consumption for planning purposes, comparing them to recent historical observations and trends to insure reliability. Comparative results using data from other markets, including regions facing differing labor and demographic conditions, would also be helpful.
Survey of air cargo forecasting techniques
NASA Technical Reports Server (NTRS)
Kuhlthan, A. R.; Vermuri, R. S.
Forecasting techniques currently in use in estimating or predicting the demand for air cargo in various markets are discussed with emphasis on the fundamentals of the different forecasting approaches. References to specific studies are cited when appropriate. The effectiveness of current methods is evaluated and several prospects for future activities or approaches are suggested. Appendices contain summary type analyses of about 50 specific publications on forecasting, and selected bibliographies on air cargo forecasting, air passenger demand forecasting, and general demand and modalsplit modeling.
HESS Opinions "Forecaster priorities for improving probabilistic flood forecasts"
NASA Astrophysics Data System (ADS)
Wetterhall, F.; Pappenberger, F.; Alfieri, L.; Cloke, H. L.; Thielen-del Pozo, J.; Balabanova, S.; Daňhelka, J.; Vogelbacher, A.; Salamon, P.; Carrasco, I.; Cabrera-Tordera, A. J.; Corzo-Toscano, M.; Garcia-Padilla, M.; Garcia-Sanchez, R. J.; Ardilouze, C.; Jurela, S.; Terek, B.; Csik, A.; Casey, J.; Stankūnavičius, G.; Ceres, V.; Sprokkereef, E.; Stam, J.; Anghel, E.; Vladikovic, D.; Alionte Eklund, C.; Hjerdt, N.; Djerv, H.; Holmberg, F.; Nilsson, J.; Nyström, K.; Sušnik, M.; Hazlinger, M.; Holubecka, M.
Hydrological ensemble prediction systems (HEPS) have in recent years been increasingly used for the operational forecasting of floods by European hydrometeorological agencies. The most obvious advantage of HEPS is that more of the uncertainty in the modelling system can be assessed. In addition, ensemble prediction systems generally have better skill than deterministic systems both in the terms of the mean forecast performance and the potential forecasting of extreme events. Research efforts have so far mostly been devoted to the improvement of the physical and technical aspects of the model systems, such as increased resolution in time and space and better description of physical processes. Developments like these are certainly needed; however, in this paper we argue that there are other areas of HEPS that need urgent attention. This was also the result from a group exercise and a survey conducted to operational forecasters within the European Flood Awareness System (EFAS) to identify the top priorities of improvement regarding their own system. They turned out to span a range of areas, the most popular being to include verification of an assessment of past forecast performance, a multi-model approach for hydrological modelling, to increase the forecast skill on the medium range (>3 days) and more focus on education and training on the interpretation of forecasts. In light of limited resources, we suggest a simple model to classify the identified priorities in terms of their cost and complexity to decide in which order to tackle them. This model is then used to create an action plan of short-, medium- and long-term research priorities with the ultimate goal of an optimal improvement of EFAS in particular and to spur the development of operational HEPS in general.
NASA Astrophysics Data System (ADS)
Boucheron, L.; Al-Ghraibah, Amani; McAteer, J.; Cao, H.; Jackiewicz, J.; McNamara, B.; Voelz, D.; Calabro, B.; DeGrave, K.; Kirk, M.; Madadi, A.; Petsov, A.; Taylor, G.
Solar active regions are the source of many energetic and geo-effective events such as solar flares and coronal mass ejections (CMEs). Understanding how these complex source regions evolve and produce these events is of fundamental importance, not only to solar physics, but also to the demands of space weather forecasting. We propose to investigate the physical properties of active region magnetic fields using fractal-, gradient-, neutral line-, emerging flux-, wavelet- and general image-based techniques, and to correlate them to solar activity. The combination of these projects with solarmonitor.org and the international Max Millenium Campaign presents an opportunity for accurate and timely flare predictions for the first time. Many studies have attempted to relate solar flares to their concomitant magnetic field distributions. However, a consistent, causal relationship between the magnetic field on the photosphere and the production of solar flares is unknown. Often the local properties of the active region magnetic field - critical in many theories of activity - are lost in the global definition of their diagnostics, in effect smoothing out variations that occur on small spatial scales. Mindful of this, our overall goal is to create measures that are sensitive to both the global and the small-scale nature of energy storage and release in the solar atmosphere in order to study solar flare prediction. This set of active region characteristics will be automatically explored for discriminating features through the use of feature selection methods. Such methods search a feature space while optimizing a criterion - the prediction of a flare in this case. The large size of the datasets used in this project make it well suited for an exploration of a large feature space. This work is funded through a New Mexico State University Interdisciplinary Research Grant.
Statistical earthquake focal mechanism forecasts
NASA Astrophysics Data System (ADS)
Kagan, Yan Y.; Jackson, David D.
Forecasts of the focal mechanisms of future shallow (depth 0-70 km) earthquakes are important for seismic hazard estimates and Coulomb stress, and other models of earthquake occurrence. Here we report on a high-resolution global forecast of earthquake rate density as a function of location, magnitude and focal mechanism. In previous publications we reported forecasts of 0.5° spatial resolution, covering the latitude range from -75° to +75°, based on the Global Central Moment Tensor earthquake catalogue. In the new forecasts we have improved the spatial resolution to 0.1° and the latitude range from pole to pole. Our focal mechanism estimates require distance-weighted combinations of observed focal mechanisms within 1000 km of each gridpoint. Simultaneously, we calculate an average rotation angle between the forecasted mechanism and all the surrounding mechanisms, using the method of Kagan & Jackson proposed in 1994. This average angle reveals the level of tectonic complexity of a region and indicates the accuracy of the prediction. The procedure becomes problematical where longitude lines are not approximately parallel, and where shallow earthquakes are so sparse that an adequate sample spans very large distances. North or south of 75°, the azimuths of points 1000 km away may vary by about 35°. We solved this problem by calculating focal mechanisms on a plane tangent to the Earth's surface at each forecast point, correcting for the rotation of the longitude lines at the locations of earthquakes included in the averaging. The corrections are negligible between -30° and +30° latitude, but outside that band uncorrected rotations can be significantly off. Improved forecasts at 0.5° and 0.1° resolution are posted at http://eq.ess.ucla.edu/kagan/glob_gcmt_index.html.
Using ecological forecasting of future vegetation transition and fire frequency change in the Sierra Nevada to assess fire management strategies
NASA Astrophysics Data System (ADS)
Thorne, J. H.; Schwartz, M. W.; Holguin, A. J.; Moritz, M.; Batllori, E.; Folger, K.; Nydick, K.
strong upslope shifting of open grassland, chaparral and hardwood types, which may be initiated by increased fire frequencies, particularly where fires have not recently burned within normal fire recurrence interval departures (FRID). An evaluation of four fire management strategies (business as usual; resist change; foster orderly change; protect vital resources) across four combinations of future climate and fire frequency found that no single management strategy was uniformly successful in protecting critical resources across the range of future conditions examined. This limitation is somewhat driven by current management constraints on the amount of management available to resource managers, which suggests management will need to use a triage approach to application of proactive fire management strategies, wherein MOC landscape projections can be used in decision support.
CME Ensemble Forecasting - A Primer
NASA Astrophysics Data System (ADS)
Pizzo, V. J.; de Koning, C. A.; Cash, M. D.; Millward, G. H.; Biesecker, D. A.; Codrescu, M.; Puga, L.; Odstrcil, D.
SWPC has been evaluating various approaches for ensemble forecasting of Earth-directed CMEs. We have developed the software infrastructure needed to support broad-ranging CME ensemble modeling, including composing, interpreting, and making intelligent use of ensemble simulations. The first step is to determine whether the physics of the interplanetary propagation of CMEs is better described as chaotic (like terrestrial weather) or deterministic (as in tsunami propagation). This is important, since different ensemble strategies are to be pursued under the two scenarios. We present the findings of a comprehensive study of CME ensembles in uniform and structured backgrounds that reveals systematic relationships between input cone parameters and ambient flow states and resulting transit times and velocity/density amplitudes at Earth. These results clearly indicate that the propagation of single CMEs to 1 AU is a deterministic process. Thus, the accuracy with which one can forecast the gross properties (such as arrival time) of CMEs at 1 AU is determined primarily by the accuracy of the inputs. This is no tautology - it means specifically that efforts to improve forecast accuracy should focus upon obtaining better inputs, as opposed to developing better propagation models. In a companion paper (deKoning et al., this conference), we compare in situ solar wind data with forecast events in the SWPC operational archive to show how the qualitative and quantitative findings presented here are entirely consistent with the observations and may lead to improved forecasts of arrival time at Earth.
Advances in Solar Power Forecasting
NASA Astrophysics Data System (ADS)
Haupt, S. E.; Kosovic, B.; Drobot, S.
The National Center for Atmospheric Research and partners are building a blended SunCast Solar Power Forecasting system. This system includes several short-range nowcasting models and improves upon longer range numerical weather prediction (NWP) models as part of the "Public-Private-Academic Partnership to Advance Solar Power Forecasting." The nowcasting models being built include statistical learning models that include cloud regime prediction, multiple sky imager-based advection models, satellite image-based advection models, and rapid update NWP models with cloud assimilation. The team has also integrated new modules into the Weather Research and Forecasting Model (WRF) to better predict clouds, aerosols, and irradiance. The modules include a new shallow convection scheme; upgraded physics parameterizations of clouds; new radiative transfer modules that specify GHI, DNI, and DIF prediction; better satellite assimilation methods; and new aerosol estimation methods. These new physical models are incorporated into WRF-Solar, which is then integrated with publically available NWP models via the Dynamic Integrated Forecast (DICast) system as well as the Nowcast Blender to provide seamless forecasts at partner utility and balancing authority commercial solar farms. The improvements will be described and results to date discussed.
Automation of energy demand forecasting
NASA Astrophysics Data System (ADS)
Siddique, Sanzad
Automation of energy demand forecasting saves time and effort by searching automatically for an appropriate model in a candidate model space without manual intervention. This thesis introduces a search-based approach that improves the performance of the model searching process for econometrics models. Further improvements in the accuracy of the energy demand forecasting are achieved by integrating nonlinear transformations within the models. This thesis introduces machine learning techniques that are capable of modeling such nonlinearity. Algorithms for learning domain knowledge from time series data using the machine learning methods are also presented. The novel search based approach and the machine learning models are tested with synthetic data as well as with natural gas and electricity demand signals. Experimental results show that the model searching technique is capable of finding an appropriate forecasting model. Further experimental results demonstrate an improved forecasting accuracy achieved by using the novel machine learning techniques introduced in this thesis. This thesis presents an analysis of how the machine learning techniques learn domain knowledge. The learned domain knowledge is used to improve the forecast accuracy.
Dynamic SEP event probability forecasts
NASA Astrophysics Data System (ADS)
Kahler, S. W.; Ling, A.
The forecasting of solar energetic particle (SEP) event probabilities at Earth has been based primarily on the estimates of magnetic free energy in active regions and on the observations of peak fluxes and fluences of large (≥ M2) solar X-ray flares. These forecasts are typically issued for the next 24 h or with no definite expiration time, which can be deficient for time-critical operations when no SEP event appears following a large X-ray flare. It is therefore important to decrease the event probability forecast with time as a SEP event fails to appear. We use the NOAA listing of major (≥10 pfu) SEP events from 1976 to 2014 to plot the delay times from X-ray peaks to SEP threshold onsets as a function of solar source longitude. An algorithm is derived to decrease the SEP event probabilities with time when no event is observed to reach the 10 pfu threshold. In addition, we use known SEP event size distributions to modify probability forecasts when SEP intensity increases occur below the 10 pfu event threshold. An algorithm to provide a dynamic SEP event forecast, Pd, for both situations of SEP intensities following a large flare is derived.
Communicating Storm Surge Forecast Uncertainty
NASA Astrophysics Data System (ADS)
Troutman, J. A.; Rhome, J.
When it comes to tropical cyclones, storm surge is often the greatest threat to life and property along the coastal United States. The coastal population density has dramatically increased over the past 20 years, putting more people at risk. Informing emergency managers, decision-makers and the public about the potential for wind driven storm surge, however, has been extremely difficult. Recently, the Storm Surge Unit at the National Hurricane Center in Miami, Florida has developed a prototype experimental storm surge watch/warning graphic to help communicate this threat more effectively by identifying areas most at risk for life-threatening storm surge. This prototype is the initial step in the transition toward a NWS storm surge watch/warning system and highlights the inundation levels that have a 10% chance of being exceeded. The guidance for this product is the Probabilistic Hurricane Storm Surge (P-Surge) model, which predicts the probability of various storm surge heights by statistically evaluating numerous SLOSH model simulations. Questions remain, however, if exceedance values in addition to the 10% may be of equal importance to forecasters. P-Surge data from 2014 Hurricane Arthur is used to ascertain the practicality of incorporating other exceedance data into storm surge forecasts. Extracting forecast uncertainty information through analyzing P-surge exceedances overlaid with track and wind intensity forecasts proves to be beneficial for forecasters and decision support.
Medium Range Forecasts Representation (and Long Range Forecasts?)
NASA Astrophysics Data System (ADS)
Vincendon, J.-C.
The progress of the numerical forecasts urges us to interest us in more and more distant ranges. We thus supply more and more forecasts with term of some days. Nevertheless, precautions of use are necessary to give the most reliable and the most relevant possible information. Available in a TV bulletin or on quite other support (Internet, mobile phone), the interpretation and the representation of a medium range forecast (5 - 15 days) must be different from those of a short range forecast. Indeed, the "foresee-ability" of a meteorological phenomenon decreases gradually in the course of the ranges, it decreases all the more quickly that the phenomenon is of small scale. So, at the end of some days, the probability character of a forecast becomes very widely dominating. That is why in Meteo-France the forecasts of D+4 to D+7 are accompanied with a confidence index since around ten years. It is a figure between 1 and 5: the more we approach 5, the more the confidence in the supplied forecast is good. In the practice, an indication is supplied for period D+4 / D+5, the other one for period D+6 / D+7, every day being able to benefit from a different forecast, that is be represented in a independent way. We thus supply a global tendency over 24 hours with less and less precise symbols as the range goes away. Concrete examples will be presented. From now on two years, we also publish forecasts to D+8 / J+9, accompanied with a sign of confidence (" good reliability " or " to confirm "). These two days are grouped together on a single map because for us, the described tendency to this term is relevant on a duration about 48 hours with a spatial scale slightly superior to the synoptic scale. So, we avoid producing more than two zones of types of weather over France and we content with giving an evolution for the temperatures (still, in increase or in decline). Newspapers began to publish this information, it should soon be the case of televisions. It is particularly
Smooth Sailing for Weather Forecasting
NASA Technical Reports Server (NTRS)
Through a cooperative venture with NASA's Stennis Space Center, WorldWinds, Inc., developed a unique weather and wave vector map using space-based radar satellite information and traditional weather observations. Called WorldWinds, the product provides accurate, near real-time, high-resolution weather forecasts. It was developed for commercial and scientific users. In addition to weather forecasting, the product's applications include maritime and terrestrial transportation, aviation operations, precision farming, offshore oil and gas operations, and coastal hazard response support. Target commercial markets include the operational maritime and aviation communities, oil and gas providers, and recreational yachting interests. Science applications include global long-term prediction and climate change, land-cover and land-use change, and natural hazard issues. Commercial airlines have expressed interest in the product, as it can provide forecasts over remote areas. WorldWinds, Inc., is currently providing its product to commercial weather outlets.
Aggregate vehicle travel forecasting model
Greene, D.L.; Chin, Shih-Miao; Gibson, R.
This report describes a model for forecasting total US highway travel by all vehicle types, and its implementation in the form of a personal computer program. The model comprises a short-run, econometrically-based module for forecasting through the year 2000, as well as a structural, scenario-based longer term module for forecasting through 2030. The short-term module is driven primarily by economic variables. It includes a detailed vehicle stock model and permits the estimation of fuel use as well as vehicle travel. The longer-tenn module depends on demographic factors to a greater extent, but also on trends in key parameters such as vehicle load factors, and the dematerialization of GNP. Both passenger and freight vehicle movements are accounted for in both modules. The model has been implemented as a compiled program in the Fox-Pro database management system operating in the Windows environment.
Forecasting improves for polar lows
NASA Astrophysics Data System (ADS)
Thanks to a 3-year research program recently concluded by the Norwegian Meteorological Institute in Oslo, Norwegian meteorologists are better able to forecast the intense low-pressure phenomena that threaten the safety of the country's coastal areas during the winter season.During the course of the program, meteorologists developed and tested "objective forecasting methods," as well as a numerical model suitable for small-scale weather phenomena. They also improved the processing of satellite data, and gained experience with the observing systems used, according to a bulletin prepared by the institute. The monitoring and forecasting systems should improve as the observation network improves and as the mesoscale numerical model is refined, explained Arne Grammeltvedt, director of the institute.
GEM: Statistical weather forecasting procedure
NASA Technical Reports Server (NTRS)
Miller, R. G.
The objective of the Generalized Exponential Markov (GEM) Program was to develop a weather forecast guidance system that would: predict between 0 to 6 hours all elements in the airways observations; respond instantly to the latest observed conditions of the surface weather; process these observations at local sites on minicomputing equipment; exceed the accuracy of current persistence predictions at the shortest prediction of one hour and beyond; exceed the accuracy of current forecast model output statistics inside eight hours; and be capable of making predictions at one location for all locations where weather information is available.
Acquisition forecast: Fiscal year 1995
NASA Technical Reports Server (NTRS)
This volume includes projections of all anticipated FY95, and beyond, NASA contract actions above $25,000 that small and small disadvantaged businesses may be able to perform under direct contract with the government or as subcontractors. The forecast consolidates anticipated procurements at each NASA center into an agencywide report, with the aim of increasing industries' advance knowledge of NASA requirements and enhancing competition in contracting. Each center forecast report is divided into three principal categories of procurement: research and development, services, and supplies and equipment.
Forecast communication through the newspaper Part 1: Framing the forecaster
NASA Astrophysics Data System (ADS)
Harris, Andrew J. L.
This review is split into two parts both of which address issues of forecast communication of an environmental disaster through the newspaper during a period of crisis. The first part explores the process by which information passes from the scientist or forecaster, through the media filter, to the public. As part of this filter preference, omission, selection of data, source, quote and story, as well as placement of the same information within an individual piece or within the newspaper itself, can serve to distort the message. The result is the introduction of bias and slant—that is, the message becomes distorted so as to favor one side of the argument against another as it passes through the filter. Bias can be used to support spin or agenda setting, so that a particular emphasis becomes placed on the story which exerts an influence on the reader's judgment. The net result of the filter components is either a negative (contrary) or positive (supportive) frame. Tabloidization of the news has also resulted in the use of strong, evocative, exaggerated words, headlines and images to support a frame. I illustrate these various elements of the media filter using coverage of the air space closure due to the April 2010 eruption of Eyjafjallajökull (Iceland). Using the British press coverage of this event it is not difficult to find examples of all media filter elements, application of which resulted in bias against the forecast and forecaster. These actors then became named and blamed. Within this logic, it becomes only too easy for forecasters and scientists to be framed in a negative way through blame culture. The result is that forecast is framed in such a way so as to cause the forecaster to be blamed for all losses associated with the loss-causing event. Within the social amplification of risk framework (SARF), this can amplify a negative impression of the risk, the event and the response. However, actions can be taken to avoid such an outcome. These actions
Accuracy of forecasts in strategic intelligence
Mandel, David R.; Barnes, Alan
The accuracy of 1,514 strategic intelligence forecasts abstracted from intelligence reports was assessed. The results show that both discrimination and calibration of forecasts was very good. Discrimination was better for senior (versus junior) analysts and for easier (versus harder) forecasts. Miscalibration was mainly due to underconfidence such that analysts assigned more uncertainty than needed given their high level of discrimination. Underconfidence was more pronounced for harder (versus easier) forecasts and for forecasts deemed more (versus less) important for policy decision making. Despite the observed underconfidence, there was a paucity of forecasts in the least informative 0.4–0.6 probability range. Recalibrating the forecasts substantially reduced underconfidence. The findings offer cause for tempered optimism about the accuracy of strategic intelligence forecasts and indicate that intelligence producers aim to promote informativeness while avoiding overstatement. PMID:25024176
A Delphi forecast of technology in education
NASA Technical Reports Server (NTRS)
Robinson, B. E.
The results are reported of a Delphi forecast of the utilization and social impacts of large-scale educational telecommunications technology. The focus is on both forecasting methodology and educational technology. The various methods of forecasting used by futurists are analyzed from the perspective of the most appropriate method for a prognosticator of educational technology, and review and critical analysis are presented of previous forecasts and studies. Graphic responses, summarized comments, and a scenario of education in 1990 are presented.
Forecasting Consumer Adoption of Information Technology and Services--Lessons from Home Video Forecasting.
ERIC Educational Resources Information Center
Klopfenstein, Bruce C.
Describes research that examined the strengths and weaknesses of technological forecasting methods by analyzing forecasting studies made for home video players. The discussion covers assessments and explications of correct and incorrect forecasting assumptions, and their implications for forecasting the adoption of home information technologies…
Newsletter. Social and Human Forecasting.
ERIC Educational Resources Information Center
Istituto Ricerche Applicate Documentazione e Studi, Rome (Italy).
The newsletter is not only a means of information on social and human forecasting but, moreover, a way of world intercommunication on the topic. Typical issues include current announcements and information (written primarily in English but also in other languages with English translations provided) on: 1) aims, intentions, and activities of…
Severe Weather Forecast Decision Aid
NASA Technical Reports Server (NTRS)
Bauman, William H., III; Wheeler, Mark M.; Short, David A.
This report presents a 15-year climatological study of severe weather events and related severe weather atmospheric parameters. Data sources included local forecast rules, archived sounding data, Cloud-to-Ground Lightning Surveillance System (CGLSS) data, surface and upper air maps, and two severe weather event databases covering east-central Florida. The local forecast rules were used to set threat assessment thresholds for stability parameters that were derived from the sounding data. The severe weather events databases were used to identify days with reported severe weather and the CGLSS data was used to differentiate between lightning and non-lightning days. These data sets provided the foundation for analyzing the stability parameters and synoptic patterns that were used to develop an objective tool to aid in forecasting severe weather events. The period of record for the analysis was May - September, 1989 - 2003. The results indicate that there are certain synoptic patterns more prevalent on days with severe weather and some of the stability parameters are better predictors of severe weather days based on locally tuned threat values. The results also revealed the stability parameters that did not display any skill related to severe weather days. An interactive web-based Severe Weather Decision Aid was developed to assist the duty forecaster by providing a level of objective guidance based on the analysis of the stability parameters, CGLSS data, and synoptic-scale dynamics. The tool will be tested and evaluated during the 2005 warm season.
Severe Weather Forecast Decision Aid
NASA Technical Reports Server (NTRS)
Bauman, William H., III; Wheeler, Mark
The Applied Meteorology Unit developed a forecast tool that provides an assessment of the likelihood of local convective severe weather for the day in order to enhance protection of personnel and material assets of the 45th Space Wing Cape Canaveral Air Force Station (CCAFS), and Kennedy Space Center (KSC).
Worldwide satellite market demand forecast
NASA Technical Reports Server (NTRS)
Bowyer, J. M.; Frankfort, M.; Steinnagel, K. M.
The forecast is for the years 1981 - 2000 with benchmark years at 1985, 1990 and 2000. Two typs of markets are considered for this study: Hardware (worldwide total) - satellites, earth stations and control facilities (includes replacements and spares); and non-hardware (addressable by U.S. industry) - planning, launch, turnkey systems and operations. These markets were examined for the INTELSAT System (international systems and domestic and regional systems using leased transponders) and domestic and regional systems. Forecasts were determined for six worldwide regions encompassing 185 countries using actual costs for existing equipment and engineering estimates of costs for advanced systems. Most likely (conservative growth rate estimates) and optimistic (mid range growth rate estimates) scenarios were employed for arriving at the forecasts which are presented in constant 1980 U.S. dollars. The worldwide satellite market demand forecast predicts that the market between 181 and 2000 will range from $35 to $50 billion. Approximately one-half of the world market, $16 to $20 billion, will be generated in the United States.
An Experiment in Probabilistic Forecasting.
ERIC Educational Resources Information Center
Brown, Thomas A.
Students were asked to make forecasts of fourteen quantities where true values would not become known for five or six months. The quantities were selected to be typical of the subjects which would be of interest to a decisionmaker in business or government, and included GNP, consumer prices, draft calls, deaths in South Vietnam, and election…
Understanding and Forecasting Ethnolinguistic Vitality
ERIC Educational Resources Information Center
Karan, Mark E.
Forecasting of ethnolinguistic vitality can only be done within a well-functioning descriptive and explanatory model of the dynamics of language stability and shift. It is proposed that the Perceived Benefit Model of Language Shift, used with a taxonomy of language shift motivations, provides that model. The model, based on individual language…
Military needs and forecast, 2
NASA Technical Reports Server (NTRS)
Goldstayn, Alan B.
FORECAST 2 has accomplished its objectives of identifying high leverage technologies for corporate Air Force review. Implementation is underway with emphasis on restructuring existing programs and programming resources in the FY88 BES/FY89 POM. Many joint service/agency opportunities exist.
In Brief: Forecasting meningitis threats
NASA Astrophysics Data System (ADS)
Showstack, Randy
The University Corporation for Atmospheric Research (UCAR), in conjunction with a team of health and weather organizations, has launched a project to provide weather forecasts to medical officials in Africa to help reduce outbreaks of meningitis. The forecasts will enable local health care providers to target vaccination programs more effectively. In 2009, meteorologists with the National Center for Atmospheric Research, which is managed by UCAR, will begin issuing 14-day forecasts of atmospheric conditions in Ghana. Later, UCAR plans to work closely with health experts from several African countries to design and test a decision support system to provide health officials with useful meteorological information. ``By targeting forecasts in regions where meningitis is a threat, we may be able to help vulnerable populations. Ultimately, we hope to build on this project and provide information to public health programs battling weather-related diseases in other parts of the world,'' said Rajul Pandya, director of UCAR's Community Building Program. Funding for the project comes from a $900,000 grant from Google.org, the philanthropic arm of the Internet search company.
Possible future directions in crop yield forecasting
NASA Technical Reports Server (NTRS)
Colwell, J. E.
This paper examines present and future possible applications of remote sensing to crop yield forecasting. It is concluded that there are ways in which Landsat data could be used to assist in crop yield forecasting using present technology. A framework for global crop yield forecasting which uses remote sensing, meteorological, field and ancillary data, as available, is proposed for the future.
Beat the Instructor: An Introductory Forecasting Game
ERIC Educational Resources Information Center
Snider, Brent R.; Eliasson, Janice B.
This teaching brief describes a 30-minute game where student groups compete in-class in an introductory time-series forecasting exercise. The students are challenged to "beat the instructor" who competes using forecasting techniques that will be subsequently taught. All forecasts are graphed prior to revealing the randomly generated…
Methodological Problems in the Forecasting of Education
ERIC Educational Resources Information Center
Kostanian, S. L.
Examines how forecasting of educational development in the Soviet Union can be coordinated with forecasts of scientific and technical progress. Predicts that the efficiency of social forecasting will increase when more empirical data on macro- and micro-processes is collected. (Author/DB)
Can Business Students Forecast Their Own Grade?
ERIC Educational Resources Information Center
Hossain, Belayet; Tsigaris, Panagiotis
This study examines grade expectations of two groups of business students for their final course mark. We separate students that are on average "better" forecasters on the basis of them not making significant forecast errors during the semester from those students that are poor forecasters of their final grade. We find that the better…
Use of Financial Forecasting in Educational Retrenchment.
ERIC Educational Resources Information Center
Chabotar, Kent John
Demonstrates local government's use of alternative forecasting techniques in school planning and retrenchment. Argues that forecasting is an art blending academic and political concerns. While statistical techniques and historical data are useful forecasting tools, the most significant influence should be school officials' plans and preferences.…
Richardson's Barotropic Forecast: A Reappraisal.
NASA Astrophysics Data System (ADS)
Lynch, Peter
To elucidate his numerical technique and to examine the effectiveness of geostrophic initial winds, Lewis Fry Richardson carried out an idealized forecast using the linear shallow-water equations and simple analytical pressure and velocity fields. This barotropic forecast has been repeated and extended using a global numerical model, and the results are presented in this paper. Richardson's conclusions regarding the use of geostrophic winds as initial data are reconsidered.An analysis of Richardson's data into normal modes shows that almost 85% of the energy is accounted for by a single eigenmode, the gravest symmetric rotational Hough mode, which travels westward with a period of about five days. This five-day wave has been detected in analyses of stratospheric data. It is striking that the fields chosen by Richardson on considerations of smoothness should so closely resemble a natural oscillation of the atmosphere.The numerical model employed in this study uses an implicit differencing technique, which is stable for large time steps. The numerical instability that would have destroyed Richardson's barotropic forecast, had it been extended, is thereby circumvented. It is sometimes said that computational instability was the cause of the failure of Richardson's baroclinic forecast, for which he obtained a pressure tendency value two orders of magnitude too large. However, the initial tendency is independent of the time step (at least for the explicit scheme used by Richardson). In fact, the spurious tendency resulted from the presence of unrealistically large high-frequency gravity-wave components in the initial fields.High-frequency oscillations are also found in the evolution starting from the idealized data in the barotropic forecast. They are shown to be due to the gravity-wave components of the initial data. These oscillations may be removed by a slight modification of the initial fields. This initialization is effected by means of a simple digital filtering
On the reliability of seasonal climate forecasts
Weisheimer, A.; Palmer, T. N.
Seasonal climate forecasts are being used increasingly across a range of application sectors. A recent UK governmental report asked: how good are seasonal forecasts on a scale of 1–5 (where 5 is very good), and how good can we expect them to be in 30 years time? Seasonal forecasts are made from ensembles of integrations of numerical models of climate. We argue that 'goodness' should be assessed first and foremost in terms of the probabilistic reliability of these ensemble-based forecasts; reliable inputs are essential for any forecast-based decision-making. We propose that a '5' should be reserved for systems that are not only reliable overall, but where, in particular, small ensemble spread is a reliable indicator of low ensemble forecast error. We study the reliability of regional temperature and precipitation forecasts of the current operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts, universally regarded as one of the world-leading operational institutes producing seasonal climate forecasts. A wide range of 'goodness' rankings, depending on region and variable (with summer forecasts of rainfall over Northern Europe performing exceptionally poorly) is found. Finally, we discuss the prospects of reaching '5' across all regions and variables in 30 years time. PMID:24789559
ECMWF SSW forecast evaluation using infrasound
NASA Astrophysics Data System (ADS)
Smets, P. S. M.; Assink, J. D.; Le Pichon, A.; Evers, L. G.
Accurate prediction of Sudden Stratospheric Warming (SSW) events is important for the performance of numerical weather prediction due to significant stratosphere-troposphere coupling. In this study, for the first time middle atmospheric numerical weather forecasts are evaluated using infrasound. A year of near-continuous infrasound from the volcano Mount Tolbachik (Kamchatka, Russian Federation) is compared with simulations using high-resolution deterministic forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF). For the entire time span the nowcast generally performs best, indicated by a higher continuity of the predicted wavefront characteristics with a minimal back azimuth difference. Best performance for all forecasts is obtained in summer. The difference between the infrasound observations and the predictions based on the forecasts is significantly larger during the 2013 SSW period for all forecasts. Simulations show that the SSW onset is better captured by the 10 day forecast while the recovery is better captured by the nowcast.
Climate Time Series Analysis and Forecasting
NASA Astrophysics Data System (ADS)
Young, P. C.; Fildes, R.
This paper will discuss various aspects of climate time series data analysis, modelling and forecasting being carried out at Lancaster. This will include state-dependent parameter, nonlinear, stochastic modelling of globally averaged atmospheric carbon dioxide; the computation of emission strategies based on modern control theory; and extrapolative time series benchmark forecasts of annual average temperature, both global and local. The key to the forecasting evaluation will be the iterative estimation of forecast error based on rolling origin comparisons, as recommended in the forecasting research literature. The presentation will conclude with with a comparison of the time series forecasts with forecasts produced from global circulation models and a discussion of the implications for climate modelling research.
Univariate time series forecasting algorithm validation
NASA Astrophysics Data System (ADS)
Ismail, Suzilah; Zakaria, Rohaiza; Muda, Tuan Zalizam Tuan
Forecasting is a complex process which requires expert tacit knowledge in producing accurate forecast values. This complexity contributes to the gaps between end users and expert. Automating this process by using algorithm can act as a bridge between them. Algorithm is a well-defined rule for solving a problem. In this study a univariate time series forecasting algorithm was developed in JAVA and validated using SPSS and Excel. Two set of simulated data (yearly and non-yearly); several univariate forecasting techniques (i.e. Moving Average, Decomposition, Exponential Smoothing, Time Series Regressions and ARIMA) and recent forecasting process (such as data partition, several error measures, recursive evaluation and etc.) were employed. Successfully, the results of the algorithm tally with the results of SPSS and Excel. This algorithm will not just benefit forecaster but also end users that lacking in depth knowledge of forecasting process.
Airfreight forecasting methodology and results
NASA Technical Reports Server (NTRS)
A series of econometric behavioral equations was developed to explain and forecast the evolution of airfreight traffic demand for the total U.S. domestic airfreight system, the total U.S. international airfreight system, and the total scheduled international cargo traffic carried by the top 44 foreign airlines. The basic explanatory variables used in these macromodels were the real gross national products of the countries involved and a measure of relative transportation costs. The results of the econometric analysis reveal that the models explain more than 99 percent of the historical evolution of freight traffic. The long term traffic forecasts generated with these models are based on scenarios of the likely economic outlook in the United States and 31 major foreign countries.
Errors in Moral Forecasting: Perceptions of Affect Shape the Gap Between Moral Behaviors and Moral Forecasts.
Teper, Rimma; Tullett, Alexa M; Page-Gould, Elizabeth; Inzlicht, Michael
Research in moral decision making has shown that there may not be a one-to-one relationship between peoples' moral forecasts and behaviors. Although past work suggests that physiological arousal may account for part of the behavior-forecasting discrepancy, whether or not perceptions of affect play an important determinant remains unclear. Here, we investigate whether this discrepancy may arise because people fail to anticipate how they will feel in morally significant situations. In Study 1, forecasters predicted cheating significantly more on a test than participants in a behavior condition actually cheated. Importantly, forecasters who received false somatic feedback, indicative of high arousal, produced forecasts that aligned more closely with behaviors. In Study 2, forecasters who misattributed their arousal to an extraneous source forecasted cheating significantly more. In Study 3, higher dispositional emotional awareness was related to less forecasted cheating. These findings suggest that perceptions of affect play a key role in the behavior-forecasting dissociation. PMID:25900823
Probabilistic Downscaling Methods for Developing Categorical Streamflow Forecasts using Climate Forecasts
NASA Astrophysics Data System (ADS)
Mazrooei, A. H.
Statistical information from climate forecast ensembles can be utilized in developing probabilistic streamflow forecasts for providing the uncertainty in streamflow forecast potential. This study examines the use of Multinomial Logistic Regression (MLR) in downscaling the probabilistic information from the large-scale climate forecast ensembles into a point-scale categorical streamflow forecasts. Performance of MLR in developing one-month lead categorical forecasts is evaluated for various river basins over the US Sunbelt. Comparison of MLR with the estimated categorical forecasts from Principle Component Regression (PCR) method under both cross-validation and split-sampling validation reveals that in general the forecasts from MLR has better performance and lower Rank Probability Score (RPS) compared to the PCR forecasts. In addition, MLR performs better than PCR method particularly in arid basins that exhibit strong skewness in seasonal flows with records of distinct dry years. A theoretical underpinning for this improved performance of MLR is also provided.
Improving Forecasts for Water Management
NASA Astrophysics Data System (ADS)
Arumugam, Sankar; Wood, Andy; Rajagopalan, Balaji; Schaake, John
Recent advances in seasonal to interannual hydroclimate predictions provide an opportunity for developing a proactive approach toward water management. This motivated a recent AGU Chapman Conference (see program details at http://chapman.agu.org/watermanagement/). Approximately 85 participants from the United States, Oceania, Asia, Europe, and South America presented and discussed the current state of successes, challenges, and opportunities in seasonal to interannual hydroclimate forecasts and water management, and a number of key messages emerged.
Post Processing Numerical Weather Prediction Model Rainfall Forecasts for Use in Ensemble Streamflow Forecasting in Australia
NASA Astrophysics Data System (ADS)
Shrestha, D. L.; Robertson, D.; Bennett, J.; Ward, P.; Wang, Q. J.
Through the water information research and development alliance (WIRADA) project, CSIRO is conducting research to improve flood and short-term streamflow forecasting services delivered by the Australian Bureau of Meteorology. WIRADA aims to build and test systems to generate ensemble flood and short-term streamflow forecasts with lead times of up to 10 days by integrating rainfall forecasts from Numerical Weather Prediction (NWP) models and hydrological modelling. Here we present an overview of the latest progress towards developing this system. Rainfall during the forecast period is a major source of uncertainty in streamflow forecasting. Ensemble rainfall forecasts are used in streamflow forecasting to characterise the rainfall uncertainty. In Australia, NWP models provide forecasts of rainfall and other weather conditions for lead times of up to 10 days. However, rainfall forecasts from Australian NWP models are deterministic and often contain systematic errors. We use a simplified Bayesian joint probability (BJP) method to post-process rainfall forecasts from the latest generation of Australian NWP models. The BJP method generates reliable and skilful ensemble rainfall forecasts. The post-processed rainfall ensembles are then used to force a semi-distributed conceptual rainfall runoff model to produce ensemble streamflow forecasts. The performance of the ensemble streamflow forecasts is evaluated on a number of Australian catchments and the benefits of using post processed rainfall forecasts are demonstrated.
Short-term ensemble streamflow forecasting using operationally-produced single-valued streamflow forecasts
NASA Astrophysics Data System (ADS)
Regonda, Satish; Seo, Dong-Jun; Lawrence, Bill
We present a statistical procedure that generates short-term streamflow ensemble forecasts from single-valued, or deterministic, forecasts operationally produced by the National Weather Service (NWS) River Forecast Centers (RFC). The resulting ensemble forecast provides an estimate of the uncertainty in the single-valued forecast to aid risk-based decision making by the emergency managers and by the users of the forecast products and services. The single-valued forecasts are produced at a 6-hr time step for 5 days into the future, and reflect single-valued short-term quantitative precipitation and temperature forecasts (QPF, QTF) and various run-time modifications (MOD), or manual data assimilation, by human forecasters to reduce various sources of error in the end-to-end forecast process. The proposed procedure generates 5 day-ahead ensemble traces of streamflow from a very parsimonious approximation of the conditional multivariate probability distribution of future streamflow given the single-valued streamflow forecasts, QPF and recent streamflow observations. For parameter estimation and evaluation, we used a 10-year archive of the single-valued river stage forecasts for six forecast points in Oklahoma produced operationally by the Arkansas-Red River Basin River Forecast Center (ABRFC). To evaluate the procedure, we carried out dependent and leave-one-year-out cross validation. The resulting ensemble hindcasts are then verified using the Ensemble Verification System (EVS) developed at the NWS Office of Hydrologic Development (OHD).
Scorecard on winter weather forecast
NASA Astrophysics Data System (ADS)
Richman, Barbara T.
A comparison of the observed temperatures and precipitation for this past winter (maps on left) with predicted temperatures and precipitation (maps on right) shows that the National Weather Service (NWS) temperature prediction was below par, but that the NWS precipitation forecast was 'quite good,' according to Don L. Gilman, chief of the NWS long-range forecast branch. The predictions, issued November 29, 1982 (Eos, December 14, 1982, p. 1211), covered December, January, and February.NWS long-range forecasters had thought that frigid Arctic air would swoop far south to bring below-normal temperatures to the western United States. Instead, an east Pacific trough, which may have been the strongest since 1900, brought a strong influx of air from the west, according to Gilman. The intense, low-pressure anomaly in the east Pacific, with the strong westerly winds, teamed with heavy rains south and southwest of Hawaii and warm equatorial Pacific waters to bring warm, wet air to the western United States. The results (see maps): Throughout most of the country, observed temperatures were above normal (A) or normal (N), while observed precipitation was heavy (H) o r normal (no code). Below-normal temperatures (B) occurred only in a portion of the southcentral U.S. and the Florida Keys. Light precipitation (L) fell over two patches in the northern plains, in the Appalachian region, and along the Maine coast.
Forecast of iceberg ensemble drift
El-Tahan, M.S.; El-Tahan, H.W.; Venkatesh, S.
The objectives of the study are to gain a better understanding of the characteristics of iceberg motion and the factors controlling iceberg drift, and to develop an iceberg ensemble drift forecast system to be operated by the Canadian Atmospheric Environment Service. An extensive review of field and theoretical studies on iceberg behaviour, and the factors controlling iceberg motion has been carried out. Long term and short term behaviour of icebergs are critically examined. A quantitative assessment of the effects of the factors controlling iceberg motion is presented. The study indicated that wind and currents are the primary driving forces. Coriolis Force and ocean surface slope also have significant effects. As for waves, only the higher waves have a significant effect. Iceberg drift is also affected by iceberg size characteristics. Based on the findings of the study a comprehensive computerized forecast system to predict the drift of iceberg ensembles off Canada's east coast has been designed. The expected accuracy of the forecast system is discussed and recommendations are made for future improvements to the system.
Communicating uncertainty in hydrological forecasts: mission impossible?
NASA Astrophysics Data System (ADS)
Ramos, Maria-Helena; Mathevet, Thibault; Thielen, Jutta; Pappenberger, Florian
Cascading uncertainty in meteo-hydrological modelling chains for forecasting and integrated flood risk assessment is an essential step to improve the quality of hydrological forecasts. Although the best methodology to quantify the total predictive uncertainty in hydrology is still debated, there is a common agreement that one must avoid uncertainty misrepresentation and miscommunication, as well as misinterpretation of information by users. Several recent studies point out that uncertainty, when properly explained and defined, is no longer unwelcome among emergence response organizations, users of flood risk information and the general public. However, efficient communication of uncertain hydro-meteorological forecasts is far from being a resolved issue. This study focuses on the interpretation and communication of uncertain hydrological forecasts based on (uncertain) meteorological forecasts and (uncertain) rainfall-runoff modelling approaches to decision-makers such as operational hydrologists and water managers in charge of flood warning and scenario-based reservoir operation. An overview of the typical flow of uncertainties and risk-based decisions in hydrological forecasting systems is presented. The challenges related to the extraction of meaningful information from probabilistic forecasts and the test of its usefulness in assisting operational flood forecasting are illustrated with the help of two case-studies: 1) a study on the use and communication of probabilistic flood forecasting within the European Flood Alert System; 2) a case-study on the use of probabilistic forecasts by operational forecasters from the hydroelectricity company EDF in France. These examples show that attention must be paid to initiatives that promote or reinforce the active participation of expert forecasters in the forecasting chain. The practice of face-to-face forecast briefings, focusing on sharing how forecasters interpret, describe and perceive the model output forecasted
Assessment of reservoir system variable forecasts
NASA Astrophysics Data System (ADS)
Kistenmacher, Martin; Georgakakos, Aris P.
Forecast ensembles are a convenient means to model water resources uncertainties and to inform planning and management processes. For multipurpose reservoir systems, forecast types include (i) forecasts of upcoming inflows and (ii) forecasts of system variables and outputs such as reservoir levels, releases, flood damage risks, hydropower production, water supply withdrawals, water quality conditions, navigation opportunities, and environmental flows, among others. Forecasts of system variables and outputs are conditional on forecasted inflows as well as on specific management policies and can provide useful information for decision-making processes. Unlike inflow forecasts (in ensemble or other forms), which have been the subject of many previous studies, reservoir system variable and output forecasts are not formally assessed in water resources management theory or practice. This article addresses this gap and develops methods to rectify potential reservoir system forecast inconsistencies and improve the quality of management-relevant information provided to stakeholders and managers. The overarching conclusion is that system variable and output forecast consistency is critical for robust reservoir management and needs to be routinely assessed for any management model used to inform planning and management processes. The above are demonstrated through an application from the Sacramento-American-San Joaquin reservoir system in northern California.
Forecasting Space Weather from Magnetograms
NASA Technical Reports Server (NTRS)
Falconer, David A.; Moore, Ronald L.; Barghouty, Abdulnasser F.; Khazanov, Igor
Large flares and fast CMEs are the drivers of the most severe space weather including Solar Energetic Particle Events (SEP Events). Large flares and their co-produced CMEs are powered by the explosive release of free magnetic energy stored in non-potential magnetic fields of sunspot active regions. The free energy is stored in and released from the low-beta regime of the active region s magnetic field above the photosphere, in the chromosphere and low corona. From our work over the past decade and from similar work of several other groups, it is now well established that (1) a proxy of the free magnetic energy stored above the photosphere can be measured from photospheric magnetograms, maps of the measured field in the photosphere, and (2) an active region s rate of production of major CME/flare eruptions in the coming day or so is strongly correlated with its present measured value of the free-energy proxy. These results have led us to use the large database of SOHO/MDI full-disk magnetograms spanning Solar Cycle 23 to obtain empirical forecasting curves that from an active region s present measured value of the free-energy proxy give the active region s expected rates of production of major flares, CMEs, fast CMEs, and SEP Events in the coming day or so (Falconer et al 2011, Space Weather, 9, S04003). For each type of event, the expected rate is readily converted to the chance that the active region will produce such an event in any given forward time window of a day or so. If the chance is small enough (e.g. <5%), the forecast is All Clear for that type of event. We will present these forecasting curves and demonstrate the accuracy of their forecasts. In addition, we will show that the forecasts for major flares and fast CMEs can be made significantly more accurate by taking into account not only the value of the free energy proxy but also the active region s recent productivity of major flares; specifically, whether the active region has produced a major flare
Grim Job Talks Are a Buzz Kill
ERIC Educational Resources Information Center
Shapiro, Dan
This article takes a look at five mistakes that candidates should avoid making during their research presentations. These mistakes are the following: (1) they didn't do any research on the norms of the campus culture; (2) they presented a single, well-thought-out project that had no future; (3) they didn't use the opportunity to demonstrate their…
Evaluation of seasonal forecast skill over China
NASA Astrophysics Data System (ADS)
Roads, John O.; Chen, Shyh-Chin
Since Sept. 26, 1997, the Scripps Experimental Climate Prediction Center (ECPC) has been making experimental, near real-time seasonal global forecasts. Images of these forecasts, at daily to seasonal time scales, are provided on the World Wide Web, and experimental digital forecast products are made available to international collaborators. Over Asia, these experimental forecasts are now being used to drive regional prediction and various application models at National Taiwan University (NTU) and the Hong Kong Observatory. Roads et al. [Bull. Am. Meteorol. Soc. 82 (2001) 639] and Terra Chen et al. [Atmos. Oceanogr. Sci. 12 (2003a) 377] previously discussed the basic forecast and analysis system. The purpose of this paper is to discuss specific synoptic characteristics of recent seasonal forecasts as a guide to further application and development.
ECMWF SSW forecast evaluation using infrasound
NASA Astrophysics Data System (ADS)
Smets, Pieter; Assink, Jelle; Le Pichon, Alexis; Evers, Läslo
Accurate prediction of Sudden Stratospheric Warming (SSW) events is important for the performance of numerical weather prediction due to significant stratosphere--troposphere coupling. In this study, for the first time middle atmospheric numerical weather forecasts are evaluated using infrasound. A year of near continuous infrasound from Mt. Tolbachik (Kamchatka, Russian Federation) is compared with simulations using high resolution deterministic forecasts of the European Centre for Medium-range Weather Forecasts (ECMWF). For the entire timespan the nowcast generally performs best, indicated by a higher continuity and and smaller bearing difference. However, focussing on the period around the 2013 major SSW shows that while the SSW onset is better captured by the ten day forecast, the recovery is better captured by the nowcast. As such, this study demonstrates the use of infrasound in the evaluation of middle atmospheric weather forecasts and therefore its potential in the assessment of tropospheric forecast skill.
How rolling forecasting facilitates dynamic, agile planning.
Miller, Debra; Allen, Michael; Schnittger, Stephanie; Hackman, Theresa
Rolling forecasting may be used to replace or supplement the annual budget process. The rolling forecast typically builds on the organization's strategic financial plan, focusing on the first three years of plan projections and comparing the strategic financial plan assumptions with the organization's expected trajectory. Leaders can then identify and respond to gaps between the rolling forecast and the strategic financial plan on an ongoing basis. PMID:24340653
Seasonal hydrological ensemble forecasts over Europe
NASA Astrophysics Data System (ADS)
Arnal, Louise; Wetterhall, Fredrik; Pappenberger, Florian
Seasonal forecasts have an important socio-economic value in hydro-meteorological forecasting. The applications are for example hydropower management, spring flood prediction and water resources management. The latter includes prediction of low flows, primordial for navigation, water quality assessment, droughts and agricultural water needs. Traditionally, seasonal hydrological forecasts are done using the observed discharge from previous years, so called Ensemble Streamflow Prediction (ESP). With the recent increasing development of seasonal meteorological forecasts, the incentive for developing and improving seasonal hydrological forecasts is great. In this study, a seasonal hydrological forecast, driven by the ECMWF's System 4 (SEA), was compared with an ESP of modelled discharge using observations. The hydrological model used for both forecasts was the LISFLOOD model, run over a European domain with a spatial resolution of 5 km. The forecasts were produced from 1990 until the present time, with a daily time step. They were issued once a month with a lead time of seven months. The SEA forecasts are constituted of 15 ensemble members, extended to 51 members every three months. The ESP forecasts comprise 20 ensembles and served as a benchmark for this comparative study. The forecast systems were compared using a diverse set of verification metrics, such as continuous ranked probability scores, ROC curves, anomaly correlation coefficients and Nash-Sutcliffe efficiency coefficients. These metrics were computed over several time-scales, ranging from a weekly to a six-months basis, for each season. The evaluation enabled the investigation of several aspects of seasonal forecasting, such as limits of predictability, timing of high and low flows, as well as exceedance of percentiles. The analysis aimed at exploring the spatial distribution and timely evolution of the limits of predictability.
Operational Short-Term Flood Forecasting for Bangladesh: Application of ECMWF Ensemble Precipitation Forecasts
NASA Astrophysics Data System (ADS)
Hopson, T. M.; Webster, P. J.
The country of Bangladesh frequently experiences severe catchment-scale flooding from the combined discharges of the Ganges and Brahmaputra rivers. Beginning in 2003, we have been disseminating upper-catchment discharge forecasts for this country to provide advanced warning for evacuation and relief measures. These forecasts are being generated using the European Centre for Medium-Range Weather Forecasting (ECMWF) shortterm ensemble weather forecasts and a combination of distributed and data-based modeling techniques. The forecasts from each of these models are combined using the multi-ensemble technique commonly employed in numerical weather prediction. This leads to a reduction in the overall forecast error and capitalizes on the strengths of each model during different periods of the monsoon season. In addition, the models are combined such that the probabilistic nature of the ensemble precipitation forecasts is retained while being combined with the discharge modeling error to produce true probabilistic forecasts of discharge that are being employed operationally.
Geothermal wells: a forecast of drilling activity
Brown, G.L.; Mansure, A.J.; Miewald, J.N.
Numbers and problems for geothermal wells expected to be drilled in the United States between 1981 and 2000 AD are forecasted. The 3800 wells forecasted for major electric power projects (totaling 6 GWe of capacity) are categorized by type (production, etc.), and by location (The Geysers, etc.). 6000 wells are forecasted for direct heat projects (totaling 0.02 Quads per year). Equations are developed for forecasting the number of wells, and data is presented. Drilling and completion problems in The Geysers, The Imperial Valley, Roosevelt Hot Springs, the Valles Caldera, northern Nevada, Klamath Falls, Reno, Alaska, and Pagosa Springs are discussed. Likely areas for near term direct heat projects are identified.
Combining forecast weights: Why and how?
NASA Astrophysics Data System (ADS)
Yin, Yip Chee; Kok-Haur, Ng; Hock-Eam, Lim
This paper proposes a procedure called forecast weight averaging which is a specific combination of forecast weights obtained from different methods of constructing forecast weights for the purpose of improving the accuracy of pseudo out of sample forecasting. It is found that under certain specified conditions, forecast weight averaging can lower the mean squared forecast error obtained from model averaging. In addition, we show that in a linear and homoskedastic environment, this superior predictive ability of forecast weight averaging holds true irrespective whether the coefficients are tested by t statistic or z statistic provided the significant level is within the 10% range. By theoretical proofs and simulation study, we have shown that model averaging like, variance model averaging, simple model averaging and standard error model averaging, each produces mean squared forecast error larger than that of forecast weight averaging. Finally, this result also holds true marginally when applied to business and economic empirical data sets, Gross Domestic Product (GDP growth rate), Consumer Price Index (CPI) and Average Lending Rate (ALR) of Malaysia.
Wheat yield forecasts using LANDSAT data
NASA Technical Reports Server (NTRS)
Colwell, J. E.; Rice, D. P.; Nalepka, R. F.
Several considerations of winter wheat yield prediction using LANDSAT data were discussed. In addition, a simple technique which permits direct early season forecasts of wheat production was described.
Optimized Flood Forecasts Using a Statistical Enemble
NASA Astrophysics Data System (ADS)
Silver, Micha; Fredj, Erick
The method presented here assembles an optimized flood forecast from a set of consecutive WRF-Hydro simulations by applying coefficients which we derive from straightforward statistical procedures. Several government and research institutions that produce climate data offer ensemble forecasts, which merge predictions from different models to gain a more accurate fit to observed data. Existing ensemble forecasts present climate and weather predictions only. In this research we propose a novel approach to constructing hydrological ensembles for flood forecasting. The ensemble flood forecast is created by combining predictions from the same model, but initiated at different times. An operative flood forecasting system, run by the Israeli Hydrological Service, produces flood forecasts twice daily with a 72 hour forecast period. By collating the output from consecutive simulation runs we have access to multiple overlapping forecasts. We then apply two statistical procedures to blend these consecutive forecasts, resulting in a very close fit to observed flood runoff. We first employ cross-correlation with a time lag to determine a time shift for each of the original, consecutive forecasts. This shift corrects for two possible sources of error: slow or fast moving weather fronts in the base climate data; and mis-calibrations of the WRF-Hydro model in determining the rate of flow of surface runoff and in channels. We apply this time shift to all consecutive forecasts, then run a linear regression with the observed runoff data as the dependent variable and all shifted forecasts as the predictor variables. The solution to the linear regression equation is a set of coefficients that corrects the amplitude errors in the forecasts. These resulting regression coefficients are then applied to the consecutive forecasts producing a statistical ensemble which, by design, closely matches the observed runoff. After performing this procedure over many storm events in the Negev region
The Economic Value of Air Quality Forecasting
NASA Astrophysics Data System (ADS)
Anderson-Sumo, Tasha
Both long-term and daily air quality forecasts provide an essential component to human health and impact costs. According the American Lung Association, the estimated current annual cost of air pollution related illness in the United States, adjusted for inflation (3% per year), is approximately $152 billion. Many of the risks such as hospital visits and morality are associated with poor air quality days (where the Air Quality Index is greater than 100). Groups such as sensitive groups become more susceptible to the resulting conditions and more accurate forecasts would help to take more appropriate precautions. This research focuses on evaluating the utility of air quality forecasting in terms of its potential impacts by building on air quality forecasting and economical metrics. Our analysis includes data collected during the summertime ozone seasons between 2010 and 2012 from air quality models for the Washington, DC/Baltimore, MD region. The metrics that are relevant to our analysis include: (1) The number of times that a high ozone or particulate matter (PM) episode is correctly forecasted, (2) the number of times that high ozone or PM episode is forecasted when it does not occur and (3) the number of times when the air quality forecast predicts a cleaner air episode when the air was observed to have high ozone or PM. Our collection of data included available air quality model forecasts of ozone and particulate matter data from the U.S. Environmental Protection Agency (EPA)'s AIRNOW as well as observational data of ozone and particulate matter from Clean Air Partners. We evaluated the performance of the air quality forecasts with that of the observational data and found that the forecast models perform well for the Baltimore/Washington region and the time interval observed. We estimate the potential amount for the Baltimore/Washington region accrues to a savings of up to 5,905 lives and 5.9 billion dollars per year. This total assumes perfect compliance with
1992 five year battery forecast
Amistadi, D.
Five-year trends for automotive and industrial batteries are projected. Topic covered include: SLI shipments; lead consumption; automotive batteries (5-year annual growth rates); industrial batteries (standby power and motive power); estimated average battery life by area/country for 1989; US motor vehicle registrations; replacement battery shipments; potential lead consumption in electric vehicles; BCI recycling rates for lead-acid batteries; US average car/light truck battery life; channels of distribution; replacement battery inventory end July; 2nd US battery shipment forecast.
Forecasting California's earthquakes
Kerr, R. A.
For the first time, researchers have reached to a consensus on the threat of large earthquakes to California, things look no worse for Los Angles than before. It still has about a 60 percent chance of being shaken by a large earthquake sometime during the next 30 years. But other heavily populated areas of California, such as San Bernardino and the East Bay area of San Francisco, are now getting their fair share of attention. The new consensus also points up the considerable uncertainties invloved in earthquake forecasting.
Forecast Mekong: navigating changing waters
Powell, Janine
The U.S. Geological Survey (USGS) is using research and data from the Mekong River Delta in Southeast Asia to compare restoration, conservation, and management efforts there with those done in other major river deltas, such as the Mississippi River Delta in the United States. The project provides a forum to engage regional partners in the Mekong Basin countries to share data and support local research efforts. Ultimately, Forecast Mekong will lead to more informed decisions about how to make the Mekong and Mississippi Deltas resilient in the face of climate change, economic stresses, and other impacts.
Mental Models of Software Forecasting
NASA Technical Reports Server (NTRS)
Hihn, J.; Griesel, A.; Bruno, K.; Fouser, T.; Tausworthe, R.
The majority of software engineers resist the use of the currently available cost models. One problem is that the mathematical and statistical models that are currently available do not correspond with the mental models of the software engineers. In an earlier JPL funded study (Hihn and Habib-agahi, 1991) it was found that software engineers prefer to use analogical or analogy-like techniques to derive size and cost estimates, whereas curren CER's hide any analogy in the regression equations. In addition, the currently available models depend upon information which is not available during early planning when the most important forecasts must be made.
Seasonal forecast skill of Arctic sea ice area in a dynamical forecast system
NASA Astrophysics Data System (ADS)
Sigmond, M.; Fyfe, J. C.; Flato, G. M.; Kharin, V. V.; Merryfield, W. J.
Perturbation of convection-permitting NWP forecasts for flash-flood ensemble forecasting
NASA Astrophysics Data System (ADS)
Vincendon, B.; Ducrocq, V.; Nuissier, O.; Vié, B.
Mediterranean intense weather events often lead to devastating flash-floods. Extending the forecasting lead times further than the watershed response times, implies the use of numerical weather prediction (NWP) to drive hydrological models. However, the nature of the precipitating events and the temporal and spatial scales of the watershed response make them difficult to forecast, even using a high-resolution convection-permitting NWP deterministic forecasting. This study proposes a new method to sample the uncertainties of high-resolution NWP precipitation forecasts in order to quantify the predictability of the streamflow forecasts. We have developed a perturbation method based on convection-permitting NWP-model error statistics. It produces short-term precipitation ensemble forecasts from single-value meteorological forecasts. These rainfall ensemble forecasts are then fed into a hydrological model dedicated to flash-flood forecasting to produce ensemble streamflow forecasts. The verification on two flash-flood events shows that this forecasting ensemble performs better than the deterministic forecast. The performance of the precipitation perturbation method has also been found to be broadly as good as that obtained using a state-of-the-art research convection-permitting NWP ensemble, while requiring less computing time.
A Streamflow Forecast Model for Central Arizona.
NASA Astrophysics Data System (ADS)
Young, Kenneth C.; Gall, Robert L.
A spring-runoff forecast model for central Arizona was developed based on multiple discriminant analysis. More than 6500 potential predictor variables were analyzed, including local precipitation and temperature variables, as well as global sea level pressure variables. The forecast model was evaluated on nine years exclusive of the years on which the model was based. Forecasts are provided in the form of a cumulative distribution function (cdf) of the expected runoff, based on analogs. A ranked probability score to evaluate forecast skill for the cdf forecasts was developed. Ranked probability skill scores ranged from 25% to 45%.Local and global forecast models were developed and compared to the combined data source model. The global forecast model was equivalent in skill to the local forecast model. The combined model exhibited a marked improvement in skill over either the local or global models.Three recurrent patterns in the predictor variables used by the forecast model are analyzed in some depth. Above-normal pressure at Raoul Island northeast of New Zealand 14 to 18 months prior to the event forecast was found to be associated with above-normal runoff. A westward shift of the Bermuda high, as evidenced by the pressure change at Charleston, South Carolina, from December to August of the preceding year, was found to be associated with above-normal runoff. Above-normal pressure at Port Moresby, New Guinea coupled with below-normal pressure at San Diego, California, the month prior to the forecast, was found to be associated with above-normal runoff.
Improved low visibility forecasts at Amsterdam Airport
NASA Astrophysics Data System (ADS)
Wijngaard, J.; Vogelezang, D.; Maat, N.; van Bruggen, H.
Accurate, reliable and unambiguous information concerning the actual and expected (low) visibility conditions at Amsterdam Airport Schiphol is very important for the available operational flow capacity. Therefore visibility forecast errors can have a negative impact on safety and operational expenses. KNMI has performed an update of the visibility forecast system in close collaboration with the main users of the forecasts (Air Traffic Control, the airport authorities and KLM airlines). This automatic forecasting system consists of a Numerical Weather Prediction Model (Hirlam) with a statistical post processing module on top of it. Output of both components is supplied to a human forecaster who issues a special probabilistic forecast bulletin. This bulletin is tailored to the specific requirements of the airport community. The improvements made to the forecast system are twofold: 1) In addition to the Meteorological Optical Range (MOR) values, RVR (Runway Visual Range) is forecasted. Since RVR depends on both MOR and the local Background Luminance, a (deterministic) statistical forecast for the latter has been developed. 2) Another improvement was achieved by calculating joint probabilities for specific combinations of visibility and cloud base height for thresholds which have direct impact on the flow capacity at the airport. The development of this new visibility forecast will be presented briefly. Also a few verification results will be shown to demonstrate the improvements made. Finally, the importance of explaining the user the use of the forecast information, in relation to their decision making process, will be discussed. For that reason, a simple guideline model to make a cost-optimal choice will be introduced.
Streamflow forecasting using functional regression
NASA Astrophysics Data System (ADS)
Masselot, Pierre; Dabo-Niang, Sophie; Chebana, Fateh; Ouarda, Taha B. M. J.
Streamflow, as a natural phenomenon, is continuous in time and so are the meteorological variables which influence its variability. In practice, it can be of interest to forecast the whole flow curve instead of points (daily or hourly). To this end, this paper introduces the functional linear models and adapts it to hydrological forecasting. More precisely, functional linear models are regression models based on curves instead of single values. They allow to consider the whole process instead of a limited number of time points or features. We apply these models to analyse the flow volume and the whole streamflow curve during a given period by using precipitations curves. The functional model is shown to lead to encouraging results. The potential of functional linear models to detect special features that would have been hard to see otherwise is pointed out. The functional model is also compared to the artificial neural network approach and the advantages and disadvantages of both models are discussed. Finally, future research directions involving the functional model in hydrology are presented.
Phantosmia as a meteorological forecaster
NASA Astrophysics Data System (ADS)
Aiello, S. R.; Hirsch, A. R.
In normosmics, olfactory ability has been found to vary with ambient humidity, barometric pressure, and season. While hallucinated sensations of phantom pain associated with changes in weather have been described, a linkage to chemosensory hallucinations has heretofore not been reported. A 64-year-old white male with Parkinson's disease presents with 5 years of phantosmia of a smoky burnt wood which changed to onion-gas and then to a noxious skunk-onion excrement odor. Absent upon waking it increases over the day and persists for hours. When severe, there appears a phantom taste with the same qualities as the odor. It is exacerbated by factors that manipulate intranasal pressure, such as coughing. When eating or sniffing, the actual flavors replace the phantosmia. Since onset, he noted the intensity and frequency of the phantosmia forecasted the weather. Two to 3 h before a storm, the phantosmia intensifies from a level 0 to a 7-10, which persists through the entire thunderstorm. Twenty years prior, he reported the ability to forecast the weather, based on pain in a torn meniscus, which vanished after surgical repair. Extensive olfactory testing demonstrates underlying hyposmia. Possible mechanisms for such chemosensory-meteorological linkage includes: air pressure induced synesthesia, disinhibition of spontaneous olfactory discharge, exacerbation of ectopic discharge, affect mediated somatic sensory amplification, and misattribution error with expectation and recall bias. This is the first reported case of weather-induced exacerbation of phantosmia. Further investigation of the connection between chemosensory complaints and ambient weather is warranted.
Earthquakes - Volcanoes (Causes and Forecast)
NASA Astrophysics Data System (ADS)
Tsiapas, E.
EARTHQUAKES - VOLCANOES (CAUSES AND FORECAST) ELIAS TSIAPAS RESEARCHER NEA STYRA, EVIA,GREECE TEL.0302224041057 email@example.com The earthquakes are caused by large quantities of liquids (e.g. H2O, H2S, SO2, ect.) moving through lithosphere and pyrosphere (MOHO discontinuity) till they meet projections (mountains negative projections or projections coming from sinking lithosphere). The liquids are moved from West Eastward carried away by the pyrosphere because of differential speed of rotation of the pyrosphere by the lithosphere. With starting point an earthquake which was noticed at an area and from statistical studies, we know when, where and what rate an earthquake may be, which earthquake is caused by the same quantity of liquids, at the next east region. The forecast of an earthquake ceases to be valid if these components meet a crack in the lithosphere (e.g. limits of lithosphere plates) or a volcano crater. In this case the liquids come out into the atmosphere by the form of gasses carrying small quantities of lava with them (volcano explosion).
The use of MOGREPS ensemble rainfall forecasts in operational flood forecasting systems across England and Wales
NASA Astrophysics Data System (ADS)
Schellekens, J.; Weerts, A. H.; Moore, R. J.; Pierce, C. E.; Hildon, S.
Operational flood forecasting systems share a fundamental challenge: forecast uncertainty which needs to be considered when making a flood warning decision. One way of representing this uncertainty is through employing an ensemble approach. This paper presents research funded by the Environment Agency in which ensemble rainfall forecasts are utilised and tested for operational use. The form of ensemble rainfall forecast used is the Met Office short-range product called MOGREPS. It is tested for operational use within the Environment Agency's National Flood Forecasting System (NFFS) for England and Wales. Currently, the NFFS uses deterministic forecasts only. The operational configuration of the NFFS for Thames Region is extended to trial the use of the new ensemble rainfall forecasts in support of probabilistic flood forecasting. Evaluation includes considering issues of model performance, configuration (how to fit the ensemble forecasts within the current configurations), data volumes, run times and options for displaying probabilistic forecasts. Although ensemble rainfall forecasts available from MOGREPS are not extensive enough to fully verify product performance, it is concluded that their use within current Environment Agency regional flood forecasting systems can provide better information to the forecaster than use of the deterministic forecasts alone. Of note are the small number of false alarms of river flow exceedance generated when using MOGREPS as input and that small flow events are also forecasted rather well, notwithstanding the rather coarse resolution of the MOGREPS grid (24 km) compared to the studied catchments. In addition, it is concluded that, with careful configuration in NFFS, MOGREPS can be used in existing systems without a significant increase in system load.
Modeled Forecasts of Dengue Fever in San Juan, Puerto Rico Using NASA Satellite Enhanced Weather Forecasts
NASA Astrophysics Data System (ADS)
Morin, C.; Quattrochi, D. A.; Zavodsky, B.; Case, J.
Dengue fever (DF) is an important mosquito transmitted disease that is strongly influenced by meteorological and environmental conditions. Recent research has focused on forecasting DF case numbers based on meteorological data. However, these forecasting tools have generally relied on empirical models that require long DF time series to train. Additionally, their accuracy has been tested retrospectively, using past meteorological data. Consequently, the operational utility of the forecasts are still in question because the error associated with weather and climate forecasts are not reflected in the results. Using up-to-date weekly dengue case numbers for model parameterization and weather forecast data as meteorological input, we produced weekly forecasts of DF cases in San Juan, Puerto Rico. Each week, the past weeks' case counts were used to re-parameterize a process-based DF model driven with updated weather forecast data to generate forecasts of DF case numbers. Real-time weather forecast data was produced using the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) system enhanced using additional high-resolution NASA satellite data. This methodology was conducted in a weekly iterative process with each DF forecast being evaluated using county-level DF cases reported by the Puerto Rico Department of Health. The one week DF forecasts were accurate especially considering the two sources of model error. First, weather forecasts were sometimes inaccurate and generally produced lower than observed temperatures. Second, the DF model was often overly influenced by the previous weeks DF case numbers, though this phenomenon could be lessened by increasing the number of simulations included in the forecast. Although these results are promising, we would like to develop a methodology to produce longer range forecasts so that public health workers can better prepare for dengue epidemics.
School Science Inspired by Improving Weather Forecasts
ERIC Educational Resources Information Center
Reid, Heather; Renfrew, Ian A.; Vaughan, Geraint
High winds and heavy rain are regular features of the British weather, and forecasting these events accurately is a major priority for the Met Office and other forecast providers. This is the challenge facing DIAMET, a project involving university groups from Manchester, Leeds, Reading, and East Anglia, together with the Met Office. DIAMET is part…
Solar power deployment: Forecasting and planning
NASA Astrophysics Data System (ADS)
Alanazi, Mohana
The rapid growth of Photovoltaic (PV) technology has been very visible over the past decade. Recently, the penetration of PV plants to the existing grid has significantly increased. Such increase in the integration of solar energy has brought attention to the solar irradiance forecasting. This thesis presents a thorough research of PV technology, how solar power can be forecasted, and PV planning under uncertainty. Over the last decade, the PV was one of the fastest growing renewable energy technologies. However, the PV system output varies based on weather conditions. Due to the variability and the uncertainty of solar power, the integration of the electricity generated by PV system is considered one of the challenges that have confronted the PV system. This thesis proposes a new forecasting method to reduce the uncertainty of the PV output so the power operator will be able to accommodate its variability. The new forecasting method proposes different processes to be undertaken before the data is fed to the forecasting model. The method converts the data sets included in the forecasting from non-stationary data to a stationary data by applying different processes including: removing the offset, removing night time solar values, and normalization. The new forecasting method aims to reduce the forecasting error and analyzes the error effect on the long term planning through calculating the payback period considering different errors.
Analog forecasting with dynamics-adapted kernels
NASA Astrophysics Data System (ADS)
Zhao, Zhizhen; Giannakis, Dimitrios
Analog forecasting is a nonparametric technique introduced by Lorenz in 1969 which predicts the evolution of states of a dynamical system (or observables defined on the states) by following the evolution of the sample in a historical record of observations which most closely resembles the current initial data. Here, we introduce a suite of forecasting methods which improve traditional analog forecasting by combining ideas from kernel methods developed in harmonic analysis and machine learning and state-space reconstruction for dynamical systems. A key ingredient of our approach is to replace single-analog forecasting with weighted ensembles of analogs constructed using local similarity kernels. The kernels used here employ a number of dynamics-dependent features designed to improve forecast skill, including Takens' delay-coordinate maps (to recover information in the initial data lost through partial observations) and a directional dependence on the dynamical vector field generating the data. Mathematically, our approach is closely related to kernel methods for out-of-sample extension of functions, and we discuss alternative strategies based on the Nyström method and the multiscale Laplacian pyramids technique. We illustrate these techniques in applications to forecasting in a low-order deterministic model for atmospheric dynamics with chaotic metastability, and interannual-scale forecasting in the North Pacific sector of a comprehensive climate model. We find that forecasts based on kernel-weighted ensembles have significantly higher skill than the conventional approach following a single analog.
A Delphi Forecast of Technology in Education.
ERIC Educational Resources Information Center
Robinson, Burke E.
The forecast reported here surveys expected utilization levels, organizational structures, and values concerning technology in education in 1990. The focus is upon educational technology and forecasting methodology; televised instruction, computer-assisted instruction (CAI), and information services are considered. The methodology employed…
Climate forecasts for corn producer decision making
Technology Transfer Automated Retrieval System (TEKTRAN)
Corn is the most widely grown crop in the Americas, with annual production in the United States of approximately 332 million metric tons. Improved climate forecasts, together with climate-related decision tools for corn producers based on these improved forecasts, could substantially reduce uncertai...
Chesapeake Bay hypoxic volume forecasts and results
Scavia, Donald; Evans, Mary Anne
The 2013 Forecast - Given the average Jan-May 2013 total nitrogen load of 162,028 kg/day, this summer's hypoxia volume forecast is 6.1 km3, slightly smaller than average size for the period of record and almost the same as 2012. The late July 2013 measured volume was 6.92 km3.
Some Initiatives in a Business Forecasting Course
ERIC Educational Resources Information Center
Chu, Singfat
The paper reports some initiatives to freshen up the typical undergraduate business forecasting course. These include (1) students doing research and presentations on contemporary tools and industry practices such as neural networks and collaborative forecasting (2) insertion of Logistic Regression in the curriculum (3) productive use of applets…
Methods and Techniques of Revenue Forecasting.
ERIC Educational Resources Information Center
Caruthers, J. Kent; Wentworth, Cathi L.
Revenue forecasting is the critical first step in most college and university budget-planning processes. While it seems a straightforward exercise, effective forecasting requires consideration of a number of interacting internal and external variables, including demographic trends, economic conditions, and broad social priorities. The challenge…
Flood Forecasting in Wales: Challenges and Solutions
NASA Astrophysics Data System (ADS)
How, Andrew; Williams, Christopher
With steep, fast-responding river catchments, exposed coastal reaches with large tidal ranges and large population densities in some of the most at-risk areas; flood forecasting in Wales presents many varied challenges. Utilising advances in computing power and learning from best practice within the United Kingdom and abroad have seen significant improvements in recent years - however, many challenges still remain. Developments in computing and increased processing power comes with a significant price tag; greater numbers of data sources and ensemble feeds brings a better understanding of uncertainty but the wealth of data needs careful management to ensure a clear message of risk is disseminated; new modelling techniques utilise better and faster computation, but lack the history of record and experience gained from the continued use of more established forecasting models. As a flood forecasting team we work to develop coastal and fluvial forecasting models, set them up for operational use and manage the duty role that runs the models in real time. An overview of our current operational flood forecasting system will be presented, along with a discussion on some of the solutions we have in place to address the challenges we face. These include: • real-time updating of fluvial models • rainfall forecasting verification • ensemble forecast data • longer range forecast data • contingency models • offshore to nearshore wave transformation • calculation of wave overtopping
Streamflow Ensemble Generation using Climate Forecasts
NASA Astrophysics Data System (ADS)
Watkins, D. W.; O'Connell, S.; Wei, W.; Nykanen, D.; Mahmoud, M.
Although significant progress has been made in understanding the correlation between large-scale atmospheric circulation patterns and regional streamflow anomalies, there is a general perception that seasonal climate forecasts are not being used to the fullest extent possible for optimal water resources management. Possible contributing factors are limited knowledge and understanding of climate processes and prediction capabilities, noise in climate signals and inaccuracies in forecasts, and hesitancy on the part of water managers to apply new information or methods that could expose them to greater liability. This work involves a decision support model based on streamflow ensembles developed for the Lower Colorado River Authority in Central Texas. Predicative skill is added to ensemble forecasts that are based on climatology by conditioning the ensembles on observable climate indicators, including streamflow (persistence), soil moisture, land surface temperatures, and large-scale recurrent patterns such as the El Ni¤o-Southern Oscillation, Pacific Decadal Oscillation, and the North Atlantic Oscillation. A Bayesian procedure for updating ensemble probabilities is outlined, and various skill scores are reviewed for evaluating forecast performance. Verification of the ensemble forecasts using a resampling procedure indicates a small but potentially significant improvement in forecast skill that could be exploited in seasonal water management decisions. The ultimate goal of this work will be explicit incorporation of climate forecasts in reservoir operating rules and estimation of the value of the forecasts.
Gambling scores for earthquake predictions and forecasts
NASA Astrophysics Data System (ADS)
Zhuang, Jiancang
This paper presents a new method, namely the gambling score, for scoring the performance earthquake forecasts or predictions. Unlike most other scoring procedures that require a regular scheme of forecast and treat each earthquake equally, regardless their magnitude, this new scoring method compensates the risk that the forecaster has taken. Starting with a certain number of reputation points, once a forecaster makes a prediction or forecast, he is assumed to have betted some points of his reputation. The reference model, which plays the role of the house, determines how many reputation points the forecaster can gain if he succeeds, according to a fair rule, and also takes away the reputation points betted by the forecaster if he loses. This method is also extended to the continuous case of point process models, where the reputation points betted by the forecaster become a continuous mass on the space-time-magnitude range of interest. We also calculate the upper bound of the gambling score when the true model is a renewal process, the stress release model or the ETAS model and when the reference model is the Poisson model.
Forecast of geothermal-drilling activity
Mansure, A.J.; Brown, G.L.
The number of geothermal wells that will be drilled to support electric power production in the United States through 2000 A.D. are forecasted. Results of the forecast are presented by 5-year periods for the five most significant geothermal resources.
Forecasting Enrollments with Fuzzy Time Series.
ERIC Educational Resources Information Center
Song, Qiang; Chissom, Brad S.
The concept of fuzzy time series is introduced and used to forecast the enrollment of a university. Fuzzy time series, an aspect of fuzzy set theory, forecasts enrollment using a first-order time-invariant model. To evaluate the model, the conventional linear regression technique is applied and the predicted values obtained are compared to the…
Resources and Long-Range Forecasts
ERIC Educational Resources Information Center
Smith, Waldo E.
The author argues that forecasts of quick depletion of resources in the environment as a result of overpopulation and increased usage may not be free from error. Ignorance still exists in understanding the recovery mechanisms of nature. Long-range forecasts are likely to be wrong in such situations. (PS)
Techniques for Forecasting Air Passenger Traffic
NASA Technical Reports Server (NTRS)
Taneja, N.
The basic techniques of forecasting the air passenger traffic are outlined. These techniques can be broadly classified into four categories: judgmental, time-series analysis, market analysis and analytical. The differences between these methods exist, in part, due to the degree of formalization of the forecasting procedure. Emphasis is placed on describing the analytical method.
Evaluation and first forecasts of the German Climate Forecast System 1 (GCFS1)
NASA Astrophysics Data System (ADS)
Fröhlich, Kristina; Baehr, Johanna; Müller, Wolfgang; Bunzel, Felix; Pohlmann, Holger; Dobrynin, Mikhail
We present the near-operational seasonal forecast system GCFS1 (German Climate Forecast System version 1), based on the CMIP5 version of the global coupled climate model MPI-ESM-LR. For GCFS1 we also present a detailed assessment on the predictive skill of the model. GCFS1 has been developed in cooperation between the Max Planck Institute for Meteorology, University of Hamburg and German Meteorological Service (DWD), the forecasts are conducted by DWD. The system is running at ECMWF with a re-forecast ensemble of 15 member and a forecast ensemble of 30 member. The re-forecasts are initialised with full field nudging in the atmosphere (using ERA Interim), in the ocean (using ORAS4) and in the sea-ice component (using NSIDC sea-ice concentration). For the initialization of the forecasts analyses from the ECMWF NWP model and recent ORAS4 analyses are taken. The ensemble perturbations are, for both re-forecasts and forecasts, generated through bred vectors in the ocean which provide initial perturbations for the ensemble in combination with simple physics perturbations in the atmosphere. Evaluation of the re-forecasted climatologies will be presented for different variables, start dates and regions. The first winter forecast during the strong El Niño phase is also subject of evaluation.
Monthly forecasting of agricultural pests in Switzerland
NASA Astrophysics Data System (ADS)
Hirschi, M.; Dubrovsky, M.; Spirig, C.; Samietz, J.; Calanca, P.; Weigel, A. P.; Fischer, A. M.; Rotach, M. W.
Given the repercussions of pests and diseases on agricultural production, detailed forecasting tools have been developed to simulate the degree of infestation depending on actual weather conditions. The life cycle of pests is most successfully predicted if the micro-climate of the immediate environment (habitat) of the causative organisms can be simulated. Sub-seasonal pest forecasts therefore require weather information for the relevant habitats and the appropriate time scale. The pest forecasting system SOPRA (www.sopra.info) currently in operation in Switzerland relies on such detailed weather information, using hourly weather observations up to the day the forecast is issued, but only a climatology for the forecasting period. Here, we aim at improving the skill of SOPRA forecasts by transforming the weekly information provided by ECMWF monthly forecasts (MOFCs) into hourly weather series as required for the prediction of upcoming life phases of the codling moth, the major insect pest in apple orchards worldwide. Due to the probabilistic nature of operational monthly forecasts and the limited spatial and temporal resolution, their information needs to be post-processed for use in a pest model. In this study, we developed a statistical downscaling approach for MOFCs that includes the following steps: (i) application of a stochastic weather generator to generate a large pool of daily weather series consistent with the climate at a specific location, (ii) a subsequent re-sampling of weather series from this pool to optimally represent the evolution of the weekly MOFC anomalies, and (iii) a final extension to hourly weather series suitable for the pest forecasting model. Results show a clear improvement in the forecast skill of occurrences of upcoming codling moth life phases when incorporating MOFCs as compared to the operational pest forecasting system. This is true both in terms of root mean squared errors and of the continuous rank probability scores of the
Economic Value of Weather and Climate Forecasts
NASA Astrophysics Data System (ADS)
Katz, Richard W.; Murphy, Allan H.
Weather and climate extremes can significantly impact the economics of a region. This book examines how weather and climate forecasts can be used to mitigate the impact of the weather on the economy. Interdisciplinary in scope, it explores the meteorological, economic, psychological, and statistical aspects of weather prediction. Chapters by area specialists provide a comprehensive view of this timely topic. They encompass forecasts over a wide range of temporal scales, from weather over the next few hours to the climate months or seasons ahead, and address the impact of these forecasts on human behavior. Economic Value of Weather and Climate Forecasts seeks to determine the economic benefits of existing weather forecasting systems and the incremental benefits of improving these systems, and will be an interesting and essential text for economists, statisticians, and meteorologists.
Load forecast and need for power
This portion of the Energy Vision 2020 draft report discusses the models used for forecasting the load growth over the period of this report. To deal with uncertainties in load growth, TVA has used a range of forecasts: low, medium, and high. Based on the medium forecast, TVA has determined that an additional 800 MWe will be required by 1998 and 16,500 MWe by 2020. based on the high growth forecast, additional power will be needed in 1997 and increasing thereafter. Based on the low growth forecast, no additional capacity would be needed during the period of this report. These estimates include a reserve margin of 15% through 1997, 13% average through the period 1998 to 2010, and 12% average during the remainder of the reporting period.
Uncertainty in dispersion forecasts using meteorological ensembles
Chin, H N; Leach, M J
The usefulness of dispersion forecasts depends on proper interpretation of results. Understanding the uncertainty in model predictions and the range of possible outcomes is critical for determining the optimal course of action in response to terrorist attacks. One of the objectives for the Modeling and Prediction initiative is creating tools for emergency planning for special events such as the upcoming the Olympics. Meteorological forecasts hours to days in advance are used to estimate the dispersion at the time of the event. However, there is uncertainty in any meteorological forecast, arising from both errors in the data (both initial conditions and boundary conditions) and from errors in the model. We use ensemble forecasts to estimate the uncertainty in the forecasts and the range of possible outcomes.
Do probabilistic forecasts lead to better decisions?
NASA Astrophysics Data System (ADS)
Ramos, M. H.; van Andel, S. J.; Pappenberger, F.
The last decade has seen growing research in producing probabilistic hydro-meteorological forecasts and increasing their reliability. This followed the promise that, supplied with information about uncertainty, people would take better risk-based decisions. In recent years, therefore, research and operational developments have also start putting attention to ways of communicating the probabilistic forecasts to decision makers. Communicating probabilistic forecasts includes preparing tools and products for visualization, but also requires understanding how decision makers perceive and use uncertainty information in real-time. At the EGU General Assembly 2012, we conducted a laboratory-style experiment in which several cases of flood forecasts and a choice of actions to take were presented as part of a game to participants, who acted as decision makers. Answers were collected and analyzed. In this paper, we present the results of this exercise and discuss if indeed we make better decisions on the basis of probabilistic forecasts.
Guideline for developing an ozone forecasting program
Dye, T.S.; MacDonald, C.P.; Anderson, C.B.
The purpose of this document is to provide guidance to help air quality agencies develop, operate, and evaluate ozone forecasting programs. This guidance document provides: Background information about ozone and the weather`s effect on ozone; A list of how ozone forecasts are currently used; A summary and evaluation of methods currently used to forecast ozone; and Steps you can follow to develop and operate an ozone forecasting program. The intended audience of this document is project managers, meteorologists, air quality analysts, and data analysts. Project managers can learn about the level of effort needed to set up and operate a forecasting program. Meteorologists can learn about the various methods to predict ozone and the steps needed to create a program.
Demand forecast model based on CRM
NASA Astrophysics Data System (ADS)
Cai, Yuancui; Chen, Lichao
With interiorizing day by day management thought that regarding customer as the centre, forecasting customer demand becomes more and more important. In the demand forecast of customer relationship management, the traditional forecast methods have very great limitation because much uncertainty of the demand, these all require new modeling to meet the demands of development. In this paper, the notion is that forecasting the demand according to characteristics of the potential customer, then modeling by it. The model first depicts customer adopting uniform multiple indexes. Secondly, the model acquires characteristic customers on the basis of data warehouse and the technology of data mining. The last, there get the most similar characteristic customer by their comparing and forecast the demands of new customer by the most similar characteristic customer.
Do probabilistic forecasts lead to better decisions?
NASA Astrophysics Data System (ADS)
Ramos, M. H.; van Andel, S. J.; Pappenberger, F.
The last decade has seen growing research in producing probabilistic hydro-meteorological forecasts and increasing their reliability. This followed the promise that, supplied with information about uncertainty, people would take better risk-based decisions. In recent years, therefore, research and operational developments have also started focusing attention on ways of communicating the probabilistic forecasts to decision-makers. Communicating probabilistic forecasts includes preparing tools and products for visualisation, but also requires understanding how decision-makers perceive and use uncertainty information in real time. At the EGU General Assembly 2012, we conducted a laboratory-style experiment in which several cases of flood forecasts and a choice of actions to take were presented as part of a game to participants, who acted as decision-makers. Answers were collected and analysed. In this paper, we present the results of this exercise and discuss if we indeed make better decisions on the basis of probabilistic forecasts.
Forecasting the Chilean Tsunami, February 27 2010
NASA Astrophysics Data System (ADS)
Sterling, K.; Knight, W.; Whitmore, P.
The West Coast and Alaska Tsunami Warning Center (WC/ATWC) is responsible for issuing tsunami warnings, advisories, and watches for the United States and Canadian coastlines. Utilizing well defined criteria related to earthquake magnitude and location an initial alert message is transmitted. The situation is monitored closely and analyzed using forecast models and real-time sea level observations. If a tsunami is detected then a tsunami warning, advisory, or watch is issued. On February 27, 2010 at 06:34:14 UTC, a M8.8 earthquake occurred off the coast of Maule, Chile, initiating a tsunami that propagated throughout the Pacific Ocean. With approximately 13 hours before the tsunami arrived on the US west coast, the WC/ATWC utilized all available forecasting tools to refine predicted tsunami amplitudes and inundation estimates, thereby providing the best possible estimates to emergency managers and the public. The guidance from the tsunami forecast models, used in concurrence with sea-level observations, resulted in a tsunami advisory being issued for the Pacific coastal regions of the United States and Canada, the extent of which was expanded and then decreased as the event evolved. The WC/ATWC used two tsunami forecast models: the Alaska Tsunami Forecast Model (ATFM) and the Short-term Inundation Forecasting for Tsunamis (SIFT) to formulate a solution. Each model provided an initial tsunami forecast based on the earthquake magnitude and location that was subsequently refined over the following hours as Deep-ocean Assessment and Reporting of Tsunamis (DART) observations became available. After the DART data was assimilated into the models, the two forecasts were used in conjunction to publicly issue predicted maximum amplitudes for 77 locations along the US west coast and in Alaska. As the tsunami reached the US coastline, tide gauge observations were used in scaling the forecasted maximum amplitudes from the ATFM, thereby increasing the forecast accuracy
Space Weather Forecasting: An Enigma
NASA Astrophysics Data System (ADS)
Sojka, J. J.
-pipe" disciplines. The perceived progress in space weather understanding differs significantly depending upon which community (scientific, technology, forecaster, society) is addressing the question. Even more divergent are these thoughts when the question is how valuable is the scientific capability of forecasting space weather. This talk will discuss present day as well as future potential for forecasting space weather for a few selected examples. The author will attempt to straddle the divergent community opinions.
Wind speed forecasting for wind energy applications
NASA Astrophysics Data System (ADS)
Liu, Hong
With more wind energy being integrated into our grid systems, forecasting wind energy has become a necessity for all market participants. Recognizing the market demands, a physical approach to site-specific hub-height wind speed forecasting system has been developed. This system is driven by the outputs from the Canadian Global Environmental Multiscale (GEM) model. A simple interpolation approach benchmarks the forecasting accuracy inherited from GEM. Local, site specific winds are affected on a local scale by a variety of factors including representation of the land surface and local boundary-layer process over heterogeneous terrain which have been a continuing challenge in NWP models like GEM with typical horizontal resolution of order 15-km. In order to resolve these small scale effects, a wind energy industry standard model, WAsP, is coupled with GEM to improve the forecast. Coupling the WAsP model with GEM improves the overall forecasts, but remains unsatisfactory for forecasting winds with abrupt surface condition changes. Subsequently in this study, a new coupler that uses a 2-D RANS model of boundary-layer flow over surface condition changes with improved physics has been developed to further improve the forecasts when winds coming from a water surface to land experience abrupt changes in surface conditions. It has been demonstrated that using vertically averaged wind speeds to represent geostrophic winds for input into the micro-scale models could reduce forecast errors. The hub-height wind speed forecasts could be further improved using a linear MOS approach. The forecasting system has been evaluated, using a wind energy standard evaluation matrix, against data from an 80-m mast located near the north shore of Lake Erie. Coupling with GEM-LAM and a power conversion model using a theoretical power curve have also been investigated. For hub-height wind speeds GEM appears to perform better with a 15-Ian grid than the high resolution GEM-2.5Ian version at the
A multisite seasonal ensemble streamflow forecasting technique
NASA Astrophysics Data System (ADS)
Bracken, Cameron; Rajagopalan, Balaji; Prairie, James
We present a technique for providing seasonal ensemble streamflow forecasts at several locations simultaneously on a river network. The framework is an integration of two recent approaches: the nonparametric multimodel ensemble forecast technique and the nonparametric space-time disaggregation technique. The four main components of the proposed framework are as follows: (1) an index gauge streamflow is constructed as the sum of flows at all the desired spatial locations; (2) potential predictors of the spring season (April-July) streamflow at this index gauge are identified from the large-scale ocean-atmosphere-land system, including snow water equivalent; (3) the multimodel ensemble forecast approach is used to generate the ensemble flow forecast at the index gauge; and (4) the ensembles are disaggregated using a nonparametric space-time disaggregation technique resulting in forecast ensembles at the desired locations and for all the months within the season. We demonstrate the utility of this technique in skillful forecast of spring seasonal streamflows at four locations in the Upper Colorado River Basin at different lead times. Where applicable, we compare the forecasts to the Colorado Basin River Forecast Center's Ensemble Streamflow Prediction (ESP) and the National Resource Conservation Service "coordinated" forecast, which is a combination of the ESP, Statistical Water Supply, a principal component regression technique, and modeler knowledge. We find that overall, the proposed method is equally skillful to existing operational models while tending to better predict wet years. The forecasts from this approach can be a valuable input for efficient planning and management of water resources in the basin.
Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose
NASA Astrophysics Data System (ADS)
Shrestha, D. L.; Robertson, D. E.; Wang, Q. J.; Pagano, T. C.; Hapuarachchi, H. A. P.
The quality of precipitation forecasts from four Numerical Weather Prediction (NWP) models is evaluated over the Ovens catchment in Southeast Australia. Precipitation forecasts are compared with observed precipitation at point and catchment scales and at different temporal resolutions. The four models evaluated are the Australian Community Climate Earth-System Simulator (ACCESS) including ACCESS-G with a 80 km resolution, ACCESS-R 37.5 km, ACCESS-A 12 km, and ACCESS-VT 5 km. The skill of the NWP precipitation forecasts varies considerably between rain gauging stations. In general, high spatial resolution (ACCESS-A and ACCESS-VT) and regional (ACCESS-R) NWP models overestimate precipitation in dry, low elevation areas and underestimate in wet, high elevation areas. The global model (ACCESS-G) consistently underestimates the precipitation at all stations and the bias increases with station elevation. The skill varies with forecast lead time and, in general, it decreases with the increasing lead time. When evaluated at finer spatial and temporal resolution (e.g. 5 km, hourly), the precipitation forecasts appear to have very little skill. There is moderate skill at short lead times when the forecasts are averaged up to daily and/or catchment scale. The precipitation forecasts fail to produce a diurnal cycle shown in observed precipitation. Significant sampling uncertainty in the skill scores suggests that more data are required to get a reliable evaluation of the forecasts. The non-smooth decay of skill with forecast lead time can be attributed to diurnal cycle in the observation and sampling uncertainty. Future work is planned to assess the benefits of using the NWP rainfall forecasts for short-term streamflow forecasting. Our findings here suggest that it is necessary to remove the systematic biases in rainfall forecasts, particularly those from low resolution models, before the rainfall forecasts can be used for streamflow forecasting.
Decision support for financial forecasting
Jairam, B.N.; Morris, J.D.; Emrich, M.L.; Hardee, H.K.
A primary mission of the Budget Management Division of the Air Force is fiscal analysis. This involves formulating, justifying, and tracking financial data during budget preparation and execution. An essential requirement of this process is the ready availability and easy manipulation of past and current budget data. This necessitates the decentralization of the data. A prototypical system, BAFS (Budget Analysis and Forecasting System), that provides such a capability is presented. In its current state, the system is designed to be a decision support tool. A brief report of the budget decisions and activities is presented. The system structure and its major components are discussed. An insight into the implementation strategies and the tool used is provided. The paper concludes with a discussion of future enhancements and the system's evolution into an expert system. 4 refs., 3 figs.
Lai, C.; Tsay, T.-K.; Chien, C.-H.; Wu, I.-L.
Researchers at the Hydroinformatic Research and Development Team (HIRDT) of the National Taiwan University undertook a project to create a real time flood forecasting model, with an aim to predict the current in the Tamsui River Basin. The model was designed based on deterministic approach with mathematic modeling of complex phenomenon, and specific parameter values operated to produce a discrete result. The project also devised a rainfall-stage model that relates the rate of rainfall upland directly to the change of the state of river, and is further related to another typhoon-rainfall model. The geographic information system (GIS) data, based on precise contour model of the terrain, estimate the regions that were perilous to flooding. The HIRDT, in response to the project's progress, also devoted their application of a deterministic model to unsteady flow of thermodynamics to help predict river authorities issue timely warnings and take other emergency measures.
Emulation for probabilistic weather forecasting
NASA Astrophysics Data System (ADS)
Cornford, Dan; Barillec, Remi
Numerical weather prediction models are typically very expensive to run due to their complexity and resolution. Characterising the sensitivity of the model to its initial condition and/or to its parameters requires numerous runs of the model, which is impractical for all but the simplest models. To produce probabilistic forecasts requires knowledge of the distribution of the model outputs, given the distribution over the inputs, where the inputs include the initial conditions, boundary conditions and model parameters. Such uncertainty analysis for complex weather prediction models seems a long way off, given current computing power, with ensembles providing only a partial answer. One possible way forward that we develop in this work is the use of statistical emulators. Emulators provide an efficient statistical approximation to the model (or simulator) while quantifying the uncertainty introduced. In the emulator framework, a Gaussian process is fitted to the simulator response as a function of the simulator inputs using some training data. The emulator is essentially an interpolator of the simulator output and the response in unobserved areas is dictated by the choice of covariance structure and parameters in the Gaussian process. Suitable parameters are inferred from the data in a maximum likelihood, or Bayesian framework. Once trained, the emulator allows operations such as sensitivity analysis or uncertainty analysis to be performed at a much lower computational cost. The efficiency of emulators can be further improved by exploiting the redundancy in the simulator output through appropriate dimension reduction techniques. We demonstrate this using both Principal Component Analysis on the model output and a new reduced-rank emulator in which an optimal linear projection operator is estimated jointly with other parameters, in the context of simple low order models, such as the Lorenz 40D system. We present the application of emulators to probabilistic weather
Construction Safety Forecast for ITER
cadwallader, lee charles
The International Thermonuclear Experimental Reactor (ITER) project is poised to begin its construction activity. This paper gives an estimate of construction safety as if the experiment was being built in the United States. This estimate of construction injuries and potential fatalities serves as a useful forecast of what can be expected for construction of such a major facility in any country. These data should be considered by the ITER International Team as it plans for safety during the construction phase. Based on average U.S. construction rates, ITER may expect a lost workday case rate of < 4.0 and a fatality count of 0.5 to 0.9 persons per year.
Experimental forecasts of El Nino
NASA Technical Reports Server (NTRS)
Cane, M. A.; Zebiak, S. E.; Dolan, S. C.
A deterministic numerical model of the coupled evolution of the tropical ocean and atmosphere was used to forecast all El Nino/Southern Oscillation (ENSO) events from 1970 to 1986. More particularly, the model, originally developed for studying large-scale ocean-atmosphere interactions in the tropics, successfully predicted the characteristics of the spatial and temporal structure of ENSO observed in the study interval. The model indicated that rainfall moving eastward over the Pacific slackens the surface winds that would otherwise cool the eastern Pacific by drawing up cooler subsurface waters. The oceanic thermocline increases, a poleward flow of westerly flowing warn waters deplets the equatorial warm water reservoir, and sea surface temperatures decline. These ENSO conditions are statistically tractable with the model several months in advance, provided upper ocean layer thermal data are available.
Phantosmia as a meteorological forecaster.
Aiello, S R; Hirsch, A R
In normosmics, olfactory ability has been found to vary with ambient humidity, barometric pressure, and season. While hallucinated sensations of phantom pain associated with changes in weather have been described, a linkage to chemosensory hallucinations has heretofore not been reported. A 64-year-old white male with Parkinson's disease presents with 5 years of phantosmia of a smoky burnt wood which changed to onion-gas and then to a noxious skunk-onion excrement odor. Absent upon waking it increases over the day and persists for hours. When severe, there appears a phantom taste with the same qualities as the odor. It is exacerbated by factors that manipulate intranasal pressure, such as coughing. When eating or sniffing, the actual flavors replace the phantosmia. Since onset, he noted the intensity and frequency of the phantosmia forecasted the weather. Two to 3 h before a storm, the phantosmia intensifies from a level 0 to a 7-10, which persists through the entire thunderstorm. Twenty years prior, he reported the ability to forecast the weather, based on pain in a torn meniscus, which vanished after surgical repair. Extensive olfactory testing demonstrates underlying hyposmia. Possible mechanisms for such chemosensory-meteorological linkage includes: air pressure induced synesthesia, disinhibition of spontaneous olfactory discharge, exacerbation of ectopic discharge, affect mediated somatic sensory amplification, and misattribution error with expectation and recall bias. This is the first reported case of weather-induced exacerbation of phantosmia. Further investigation of the connection between chemosensory complaints and ambient weather is warranted. PMID:23456373
Challenges in Forecasting SEP Events
NASA Astrophysics Data System (ADS)
Luhmann, Janet; Mays, M. Leila; Odstrcil, Dusan; Bain, Hazel; Li, Yan; Leske, Richard; Cohen, Christina
A long-standing desire of space weather prediction providers has been the ability to forecast SEP (Solar Energetic Particle) events as a part of their offerings. SEPs can have deleterious effects on the space environment and space hardware, that also impact human exploration missions. Developments of observationally driven, physics based models in the last solar cycle have made it possible to use solar magnetograms and coronagraph images to simulate, up to a month in advance for solar wind structure, and up to days in advance for interplanetary Coronal Mass Ejection (ICME) driven shocks, time series of upstream parameters similar in content to those obtained by L1 spacecraft. However, SEPs have been missing from these predictions. Because SEP event modeling requires different physical considerations it has typically been approached with cosmic ray transport concepts and treatments. However, many extra complications arise because of the moving, evolving nature of the ICME shock source of the largest events. In general, a realistic SEP event model for these so-called 'gradual' events requires an accurate description of the time-dependent 3D heliosphere as an underlying framework. We describe some applications of an approach to SEP event simulations that uses the widely-applied ENLIL heliospheric model to describe both underlying solar wind and ICME shock characteristics. Experimentation with this set-up illustrates the importance of knowing the shock connectivity to the observer, and of the need to include even non-observer-impacting CMEs in the heliospheric model. It also provides a possible path forward toward the goal of having routine SEP forecasts together with the other heliospheric predictions.
Prospective Tests of Southern California Earthquake Forecasts
NASA Astrophysics Data System (ADS)
Jackson, D. D.; Schorlemmer, D.; Gerstenberger, M.; Kagan, Y. Y.; Helmstetter, A.; Wiemer, S.; Field, N.
We are testing earthquake forecast models prospectively using likelihood ratios. Several investigators have developed such models as part of the Southern California Earthquake Center's project called Regional Earthquake Likelihood Models (RELM). Various models are based on fault geometry and slip rates, seismicity, geodetic strain, and stress interactions. Here we describe the testing procedure and present preliminary results. Forecasts are expressed as the yearly rate of earthquakes within pre-specified bins of longitude, latitude, magnitude, and focal mechanism parameters. We test models against each other in pairs, which requires that both forecasts in a pair be defined over the same set of bins. For this reason we specify a standard "menu" of bins and ground rules to guide forecasters in using common descriptions. One menu category includes five-year forecasts of magnitude 5.0 and larger. Contributors will be requested to submit forecasts in the form of a vector of yearly earthquake rates on a 0.1 degree grid at the beginning of the test. Focal mechanism forecasts, when available, are also archived and used in the tests. Interim progress will be evaluated yearly, but final conclusions would be made on the basis of cumulative five-year performance. The second category includes forecasts of earthquakes above magnitude 4.0 on a 0.1 degree grid, evaluated and renewed daily. Final evaluation would be based on cumulative performance over five years. Other types of forecasts with different magnitude, space, and time sampling are welcome and will be tested against other models with shared characteristics. Tests are based on the log likelihood scores derived from the probability that future earthquakes would occur where they do if a given forecast were true [Kagan and Jackson, J. Geophys. Res.,100, 3,943-3,959, 1995]. For each pair of forecasts, we compute alpha, the probability that the first would be wrongly rejected in favor of the second, and beta, the probability
Evaluation of Flood Forecast and Warning in Elbe river basin - Impact of Forecaster's Strategy
NASA Astrophysics Data System (ADS)
Danhelka, Jan; Vlasak, Tomas
Czech Hydrometeorological Institute (CHMI) is responsible for flood forecasting and warning in the Czech Republic. To meet that issue CHMI operates hydrological forecasting systems and publish flow forecast in selected profiles. Flood forecast and warning is an output of system that links observation (flow and atmosphere), data processing, weather forecast (especially NWP's QPF), hydrological modeling and modeled outputs evaluation and interpretation by forecaster. Forecast users are interested in final output without separating uncertainties of separate steps of described process. Therefore an evaluation of final operational forecasts was done for profiles within Elbe river basin produced by AquaLog forecasting system during period 2002 to 2008. Effects of uncertainties of observation, data processing and especially meteorological forecasts were not accounted separately. Forecast of flood levels exceedance (peak over the threshold) during forecasting period was the main criterion as flow increase forecast is of the highest importance. Other evaluation criteria included peak flow and volume difference. In addition Nash-Sutcliffe was computed separately for each time step (1 to 48 h) of forecasting period to identify its change with the lead time. Textual flood warnings are issued for administrative regions to initiate flood protection actions in danger of flood. Flood warning hit rate was evaluated at regions level and national level. Evaluation found significant differences of model forecast skill between forecasting profiles, particularly less skill was evaluated at small headwater basins due to domination of QPF uncertainty in these basins. The average hit rate was 0.34 (miss rate = 0.33, false alarm rate = 0.32). However its explored spatial difference is likely to be influenced also by different fit of parameters sets (due to different basin characteristics) and importantly by different impact of human factor. Results suggest that the practice of interactive
Testing the Value of Probability Forecasts for Calibrated Combining
Lahiri, Kajal; Peng, Huaming; Zhao, Yongchen
We combine the probability forecasts of a real GDP decline from the U.S. Survey of Professional Forecasters, after trimming the forecasts that do not have "value", as measured by the Kuiper Skill Score and in the sense of Merton (1981). For this purpose, we use a simple test to evaluate the probability forecasts. The proposed test does not require the probabilities to be converted to binary forecasts before testing, and it accommodates serial correlation and skewness in the forecasts. We find that the number of forecasters making valuable forecasts decreases sharply as the horizon increases. The beta-transformed linear pool combination scheme, based on the valuable individual forecasts, is shown to outperform the simple average for all horizons on a number of performance measures, including calibration and sharpness. The test helps to identify the good forecasters ex ante, and therefore contributes to the accuracy of the combined forecasts. PMID:25530646
Performance of aftershock forecasts: problem and formulation
NASA Astrophysics Data System (ADS)
Jiang, C.; Wu, Z.; Li, L.
WFSD project deals with the problems of earthquake physics, in which one of the important designed aims is the forecast of the on-going aftershock activity of the Wenchuan earthquake, taking the advantage of the fast response to great earthquakes. Correlation between fluid measurements and aftershocks provided heuristic clues to the forecast of aftershocks, invoking the discussion on the performance of such 'precursory anomalies', even if in a retrospective perspective. In statistical seismology, one of the critical issues is how to test the statistical significance of an earthquake forecast scheme against real seismic activity. Due to the special characteristics of aftershock series and the feature of aftershock forecasts that it deals with a limited spatial range and specific temporal duration, the test of the performance of aftershock forecasts has to be different from the standard tests for main shock series. In this presentation we address and discuss the possible schemes for testing the performance of aftershock forecasts - a seemingly simple but practically important issue in statistical seismology. As a simple and preliminary approach, we use an alternative form of Receiver Operating Characteristic (ROC) test, as well as other similar tests, considering the properties of aftershock series by using Omori law, ETAS model, and/or CFS calculation. We also discussed the lessons and experiences of the Wenchuan aftershock forecasts, exploring how to make full use of the present knowledge of the regularity of aftershocks to serve the earthquake rescue and relief endeavor as well as the post-earthquake reconstruction.
Precision Fiber Optic Sensor Market Forecast
NASA Astrophysics Data System (ADS)
Montgomery, Jeff D.; Glasco, Jon; Dixon, Frank W.
The worldwide market for precision fiber optic sensors is forecasted, 1984-1994. The forecast is based upon o Analysis of fiber optic sensor and related component current technology, and a forecast of technology advancement o Review and projection of demand for precision sensing, and the penetration which fiber optics will make into this market The analysis and projections are based mainly on interviews conducted worldwide with research teams, government agencies, systems contractors, medical and industrial laboratories, component suppliers and others. The worldwide market for precision (interferometric) fiber optic sensing systems is forecasted to exceed $0.8 billion by 1994. The forecast is segmented by geographical region (Europe, Japan and North America) and by function; o Gyroscope o Sonar o Gradiometer/Magnetometer o Other - Chemical Composition - Atmospheric Acoustic - Temperature - Position - Pressure Requirements for components are reviewed. These include special fiber, emitters and detectors, modulators, couplers, switches, integrated optical circuits and integrated optoelectronics. The advancement in component performance is forecasted. The major driving forces creating fiber optic sensor markets are reviewed. These include fiber optic sensor technical and economic advantages, increasingly stringent operational requirements, and technology evolution. The leading fiber optic sensor and related component development programs are reviewed. Component sources are listed. Funding sources for sensor and component development are outlined, and trends forecasted.
Toward Improving Streamflow Forecasts Using SNODAS Products
NASA Astrophysics Data System (ADS)
Barth, C.; Boyle, D. P.; Lamorey, G. W.; Bassett, S. D.
As part of the Water 2025 initiative, researchers at the Desert Research Institute in collaboration with the U.S. Bureau of Reclamation are developing and improving water decision support system (DSS) tools to make seasonal streamflow forecasts for management and operations of water resources in the mountainous western United States. Streamflow forecasts in these areas may have errors that are directly related to uncertainties resulting from the lack of direct high resolution snow water equivalent (SWE) measurements. The purpose of this study is to investigate the possibility of improving the accuracy of streamflow forecasts through the use of Snow Data Assimilation System (SNODAS) products, which are high-resolution daily estimates of snow cover and associated hydrologic variables such as SWE and snowmelt runoff that are available for the coterminous United States. To evaluate the benefit of incorporating the SNODAS product into streamflow forecasts, a variety of Ensemble Streamflow Predictions (ESP) are generated using the Precipitation-Runoff Modeling System (PRMS). A series of manual and automatic calibrations of PRMS to different combinations of measured (streamflow) and estimated (SNODAS SWE) hydrologic variables is performed for several watersheds at various scales of spatial resolution. This study, which is embedded in the constant effort to improve streamflow forecasts and hence water operations DSS, shows the potential of using a product such as SNODAS SWE estimates to decrease parameter uncertainty related to snow variables and enhance forecast skills early in the forecast season.
A Simulation Optimization Approach to Epidemic Forecasting.
Nsoesie, Elaine O; Beckman, Richard J; Shashaani, Sara; Nagaraj, Kalyani S; Marathe, Madhav V
Reliable forecasts of influenza can aid in the control of both seasonal and pandemic outbreaks. We introduce a simulation optimization (SIMOP) approach for forecasting the influenza epidemic curve. This study represents the final step of a project aimed at using a combination of simulation, classification, statistical and optimization techniques to forecast the epidemic curve and infer underlying model parameters during an influenza outbreak. The SIMOP procedure combines an individual-based model and the Nelder-Mead simplex optimization method. The method is used to forecast epidemics simulated over synthetic social networks representing Montgomery County in Virginia, Miami, Seattle and surrounding metropolitan regions. The results are presented for the first four weeks. Depending on the synthetic network, the peak time could be predicted within a 95% CI as early as seven weeks before the actual peak. The peak infected and total infected were also accurately forecasted for Montgomery County in Virginia within the forecasting period. Forecasting of the epidemic curve for both seasonal and pandemic influenza outbreaks is a complex problem, however this is a preliminary step and the results suggest that more can be achieved in this area. PMID:23826222
National Weather Service Forecast Reference Evapotranspiration
NASA Astrophysics Data System (ADS)
Osborne, H. D.; Palmer, C. K.; Krone-Davis, P.; Melton, F. S.; Hobbins, M.
The National Weather Service (NWS), Weather Forecasting Offices (WFOs) are producing daily reference evapotranspiration (ETrc) forecasts or FRET across the Western Region and in other selected locations since 2009, using the Penman - Monteith Reference Evapotranspiration equation for a short canopy (12 cm grasses), adopted by the Environmental Water Resources Institute of the American Society of Civil Engineers (ASCE-EWRI, 2004). The sensitivity of these daily calculations to fluctuations in temperatures, humidity, winds, and sky cover allows forecasters with knowledge of local terrain and weather patterns to better forecast in the ETrc inputs. The daily FRET product then evolved into a suite of products, including a weekly ETrc forecast for better water planning and a tabular point forecast for easy ingest into local water management-models. The ETrc forecast product suite allows water managers, the agricultural community, and the public to make more informed water-use decisions. These products permit operational planning, especially with the impending drought across much of the West. For example, the California Department of Water Resources not only ingests the FRET into their soil moisture models, but uses the FRET calculations when determining the reservoir releases in the Sacramento and American Rivers. We will also focus on the expansion of FRET verification, which compares the daily FRET to the observations of ETo from the California Irrigation Management Information System (CIMIS) across California's Central Valley for the 2012 water year.
Urban Air Quality Forecasting in Canada
NASA Astrophysics Data System (ADS)
Pavlovic, Radenko; Menard, Sylvain; Cousineau, Sophie; Stroud, Craig; Moran, Michael
Environment and Climate Change Canada has been providing air quality (AQ) forecasts for major Canadian urban centers since 2001. Over this period, the Canadian AQ Forecast Program has expanded and evolved. It currently uses the Regional Air Quality Deterministic Prediction System (RAQDPS) modelling framework. At the heart of the RAQDPS is the GEM-MACH model, an on-line coupled meteorology‒chemistry model configured for a North American domain with 10 km horizontal grid spacing and 80 vertical levels. A statistical post-processing model (UMOS-AQ) is then applied to the RAQDPS hourly forecasts for locations with AQ monitors to reduce point forecast bias and error. These outputs provide the primary guidance from which operational meteorologists disseminate Air Quality Health Index (AQHI) forecasts to the public for major urban centres across Canada. During the 2015 summer Pan Am and Parapan Am Games, which were held in Ontario, Canada, an experimental version of the RAQDPS at 2.5 km horizontal grid spacing was run for a domain over the greater Toronto area. Currently, there is ongoing research to develop and assess AQ systems run at 1 km resolution. This presentation will show analyses of operational AQ forecast performance for several pollutants over the last few years in major Canadian urban centres such as Toronto, Montreal, Vancouver, Ottawa, and Calgary. Trends in observed pollution along with short- and long-term development plans for urban AQ forecasting will also be presented.
Tropical ocean initialisation strategies for seasonal forecasting
NASA Astrophysics Data System (ADS)
Mulholland, David; Haines, Keith
Operational seasonal ENSO forecasts show substantial skill in tropical regions, but are sensitive to the initialisation procedure used in the ocean. Due to errors in wind stress forcing and in modelling the vertical transfer of momentum, a bias correction method is often used during ocean data assimilation in order to assimilate hydrographic data, e.g. from the TOGA/TAO array. While this improves the ocean state, particularly the circulation, during the analysis, it leads to an inconsistency at the beginning of a coupled forecast, since the bias correction term is generally not retained during the forecast itself. We present results from a number of ensemble simulations carried out with the European Centre for Medium-range Weather Forecasts (ECMWF) coupled forecast system, comparing different initialisation strategies for the equatorial ocean. Rapid adjustments in the ocean at the beginning of the forecast are found to induce additional variability in the thermocline. We then show that this spurious variability can be substantially reduced by persisting or more slowly adjusting the bias correction term during the first month, and that this leads to significant improvements in ENSO SST forecast skill, at lead times of 3-7 months. The results highlight the importance of ocean initialisation in maximising the skill of ENSO predictions.
Improving weather forecasts for wind energy applications
NASA Astrophysics Data System (ADS)
Kay, Merlinde; MacGill, Iain
Weather forecasts play an important role in the energy industry particularly because of the impact of temperature on electrical demand. Power system operation requires that this variable and somewhat unpredictable demand be precisely met at all times and locations from available generation. As wind generation makes up a growing component of electricity supply around the world, it has become increasingly important to be able to provide useful forecasting for this highly variable and uncertain energy resource. Of particular interest are forecasts of weather events that rapidly change wind energy production from one or more wind farms. In this paper we describe work underway to improve the wind forecasts currently available from standard Numerical Weather Prediction (NWP) through a bias correction methodology. Our study has used the Australian Bureau of Meteorology MesoLAPS 5 km limited domain model over the Victoria/Tasmania region, providing forecasts for the Woolnorth wind farm, situated in Tasmania, Australia. The accuracy of these forecasts has been investigated, concentrating on the key wind speed ranges 5 - 15 ms-1 and around 25 ms-1. A bias correction methodology was applied to the NWP hourly forecasts to help account for systematic issues such as the NWP grid point not being at the exact location of the wind farm. An additional correction was applied for timing issues by using meteorological data from the wind farm. Results to date show a reduction in spread of forecast error for hour ahead forecasts by as much as half using this double correction methodology - a combination of both bias correction and timing correction.
Seasonal hydrological ensemble forecasts over Europe
NASA Astrophysics Data System (ADS)
Arnal, Louise; Wetterhall, Fredrik; Stephens, Elisabeth; Cloke, Hannah; Pappenberger, Florian
This study investigates the limits of predictability in dynamical seasonal discharge forecasting, in both space and time, over Europe. Seasonal forecasts have an important socioeconomic value. Applications are numerous and cover hydropower management, spring flood prediction, low flow prediction for navigation and agricultural water demands. Additionally, the constant increase in NWP skill for longer lead times and the predicted increase in the intensity and frequency of hydro-meteorological extremes, have amplified the incentive to promote and further improve hydrological forecasts on sub-seasonal to seasonal timescales. In this study, seasonal hydrological forecasts (SEA), driven by the ECMWF's System 4 in hindcast mode, were analysed against an Ensemble Streamflow Prediction (ESP) benchmark. The ESP was forced with an ensemble of resampled historical meteorological observations and started with perfect initial conditions. Both forecasts were produced by the LISFLOOD model, run on the pan-European scale with a spatial resolution of 5 by 5 km. The forecasts were issued monthly on a daily time step, from 1990 until the current time, up to a lead time of 7 months. The seasonal discharge forecasts were analysed against the ESP on a catchment scale in terms of their accuracy, skill and sharpness, using a diverse set of verification metrics (e.g. KGE, CRPSS and ROC). Additionally, a reverse-ESP was constructed by forcing the LISFLOOD model with a single perfect meteorological set of observations and initiated from an ensemble of resampled historical initial conditions. The comparison of the ESP with the reverse-ESP approach enabled the identification of the respective contribution of meteorological forcings and hydrologic initial conditions errors to seasonal discharge forecasting uncertainties in Europe. These results could help pinpoint target elements of the forecasting chain which, after being improved, could lead to substantial increase in discharge predictability
Accuracy analysis of TDRSS demand forecasts
NASA Astrophysics Data System (ADS)
Stern, Daniel C.; Levine, Allen J.; Pitt, Karl J.
This paper reviews Space Network (SN) demand forecasting experience over the past 16 years and describes methods used in the forecasts. The paper focuses on the Single Access (SA) service, the most sought-after resource in the Space Network. Of the ten years of actual demand data available, only the last five years (1989 to 1993) were considered predictive due to the extensive impact of the Challenger accident of 1986. NASA's Space Network provides tracking and communications services to user spacecraft such as the Shuttle and the Hubble Space Telescope. Forecasting the customer requirements is essential to planning network resources and to establishing service commitments to future customers. The lead time to procure Tracking and Data Relay Satellites (TDRS's) requires demand forecasts ten years in the future a planning horizon beyond the funding commitments for missions to be supported. The long range forecasts are shown to have had a bias toward underestimation in the 1991 -1992 period. The trend of underestimation can be expected to be replaced by overestimation for a number of years starting with 1998. At that time demand from new missions slated for launch will be larger than the demand from ongoing missions, making the potential for delay the dominant factor. If the new missions appear as scheduled, the forecasts are likely to be moderately underestimated. The SN commitment to meet the negotiated customer's requirements calls for conservatism in the forecasting. Modification of the forecasting procedure to account for a delay bias is, therefore, not advised. Fine tuning the mission model to more accurately reflect the current actual demand is recommended as it may marginally improve the first year forecasting.
Using Forecast Information in Reservoir Operation
NASA Astrophysics Data System (ADS)
Faber, B.
Reservoir operation is a series of decisions made over time. We choose whether to release water for various downstream purposes, or store it for later use. We choose whether to detain high flows to protect downstream areas, or pass that flow to retain space to store imminent higher flows. These decisions are driven by current and future inflows to the reservoir, and yet those inflows are uncertain and extremely variable. Conceptually, more information provides opportunity for better decisions, and so information about future inflows can improve reservoir operations. However, uncertain information must be used carefully, with awareness of the uncertainty and the likely consequence of "wrong" decisions (i.e., those with consequences worse than decisions that might otherwise have been made.) The historical streamflow record offers one source of information on the range and timing of streamflows. Streamflow forecasting provides additional valuable information on coming reservoir inflows, both at short and longer time scales. For example, 5-day flow forecasts based on forecasted precipitation can inform rain-flood operations, while seasonal snowmelt forecasts can aid snowmelt-flood operation, reservoir refill, and seasonal allocation of water supply. Forecast information can aid our decision-making greatly, but too much reliance on an incorrect forecast can make operation worse. Informed use of forecasts requires an understanding of the expected range of the actual streamflow (the error distribution). Forecast products must therefore be provided with a description of skill and error distribution understood by the producers and users of that information. Using forecasts wisely, with an understanding of their uncertainty, is an important aspect of the operation of our nation's Federal reservoirs.
Accuracy analysis of TDRSS demand forecasts
NASA Technical Reports Server (NTRS)
Stern, Daniel C.; Levine, Allen J.; Pitt, Karl J.
This paper reviews Space Network (SN) demand forecasting experience over the past 16 years and describes methods used in the forecasts. The paper focuses on the Single Access (SA) service, the most sought-after resource in the Space Network. Of the ten years of actual demand data available, only the last five years (1989 to 1993) were considered predictive due to the extensive impact of the Challenger accident of 1986. NASA's Space Network provides tracking and communications services to user spacecraft such as the Shuttle and the Hubble Space Telescope. Forecasting the customer requirements is essential to planning network resources and to establishing service commitments to future customers. The lead time to procure Tracking and Data Relay Satellites (TDRS's) requires demand forecasts ten years in the future a planning horizon beyond the funding commitments for missions to be supported. The long range forecasts are shown to have had a bias toward underestimation in the 1991 -1992 period. The trend of underestimation can be expected to be replaced by overestimation for a number of years starting with 1998. At that time demand from new missions slated for launch will be larger than the demand from ongoing missions, making the potential for delay the dominant factor. If the new missions appear as scheduled, the forecasts are likely to be moderately underestimated. The SN commitment to meet the negotiated customer's requirements calls for conservatism in the forecasting. Modification of the forecasting procedure to account for a delay bias is, therefore, not advised. Fine tuning the mission model to more accurately reflect the current actual demand is recommended as it may marginally improve the first year forecasting.
Accurate Weather Forecasting for Radio Astronomy
NASA Astrophysics Data System (ADS)
Maddalena, Ronald J.
The NRAO Green Bank Telescope routinely observes at wavelengths from 3 mm to 1 m. As with all mm-wave telescopes, observing conditions depend upon the variable atmospheric water content. The site provides over 100 days/yr when opacities are low enough for good observing at 3 mm, but winds on the open-air structure reduce the time suitable for 3-mm observing where pointing is critical. Thus, to maximum productivity the observing wavelength needs to match weather conditions. For 6 years the telescope has used a dynamic scheduling system (recently upgraded; www.gb.nrao.edu/DSS) that requires accurate multi-day forecasts for winds and opacities. Since opacity forecasts are not provided by the National Weather Services (NWS), I have developed an automated system that takes available forecasts, derives forecasted opacities, and deploys the results on the web in user-friendly graphical overviews (www.gb.nrao.edu/ rmaddale/Weather). The system relies on the "North American Mesoscale" models, which are updated by the NWS every 6 hrs, have a 12 km horizontal resolution, 1 hr temporal resolution, run to 84 hrs, and have 60 vertical layers that extend to 20 km. Each forecast consists of a time series of ground conditions, cloud coverage, etc, and, most importantly, temperature, pressure, humidity as a function of height. I use the Liebe's MWP model (Radio Science, 20, 1069, 1985) to determine the absorption in each layer for each hour for 30 observing wavelengths. Radiative transfer provides, for each hour and wavelength, the total opacity and the radio brightness of the atmosphere, which contributes substantially at some wavelengths to Tsys and the observational noise. Comparisons of measured and forecasted Tsys at 22.2 and 44 GHz imply that the forecasted opacities are good to about 0.01 Nepers, which is sufficient for forecasting and accurate calibration. Reliability is high out to 2 days and degrades slowly for longer-range forecasts.
Seasonal streamflow forecasting with the global hydrological forecasting system FEWS-World
NASA Astrophysics Data System (ADS)
Candogan Yossef, N.; Van Beek, L. P.; Winsemius, H.; Bierkens, M. F.
The year-to-year variability of river discharge brings about risks and opportunities in water resources management. Reliable hydrological forecasts and effective communication allow several sectors to make more informed management decisions. In many developing regions of the world, there are no efficient hydrological forecasting systems. For these regions, a global forecasting system which indicates increased probabilities of streamflow excesses or shortages over long lead-times can be of great value. FEWS-World is developed for this purpose. The system incorporates the global hydrological model PCR-GLOBWB and delivers streamflow forecasts on a global scale. This study investigates the skill and value of FEWS-World. Skill is defined as the ability of the system to forecast discharge extremes; and value is its usefulness for possible users and ultimately for affected populations. Skill is assessed in historical simulation mode as well as retroactive forecasting mode. The eventual goal is to transfer FEWS-World to operational forecasting mode, where the system will use operational seasonal forecasts from the European Center for Medium-Range Weather Forecasts (ECMWF). The results will be disseminated on the internet to provide valuable information for users in data and model-poor regions of the world. The preliminary skill assessment of PCR-GLOBWB in reproducing flow extremes is carried out for a selection of 20 large rivers of the world. The model is run for a historical period, with a meteorological forcing data set based on observations from the Climate Research Unit of the University of East Anglia, and the ERA-40 reanalysis of ECMWF. Model skill in reproducing monthly anomalies as well as floods and droughts is assessed by applying verification measures developed for deterministic meteorological forecasts. The results of this preliminary analysis shows that even where the simulated hydrographs are biased, higher skills can be attained in reproducing monthly
1993 Pacific Northwest Loads and Resources Study, Pacific Northwest Economic and Electricity Use Forecast, Technical Appendix: Volume 1.
United States. Bonneville Power Administration.
This publication documents the load forecast scenarios and assumptions used to prepare BPA`s Whitebook. It is divided into: intoduction, summary of 1993 Whitebook electricity demand forecast, conservation in the load forecast, projection of medium case electricity sales and underlying drivers, residential sector forecast, commercial sector forecast, industrial sector forecast, non-DSI industrial forecast, direct service industry forecast, and irrigation forecast. Four appendices are included: long-term forecasts, LTOUT forecast, rates and fuel price forecasts, and forecast ranges-calculations.
How MAG4 Improves Space Weather Forecasting
NASA Technical Reports Server (NTRS)
Falconer, David; Khazanov, Igor; Barghouty, Nasser
Dangerous space weather is driven by solar flares and Coronal Mass Ejection (CMEs). Forecasting flares and CMEs is the first step to forecasting either dangerous space weather or All Clear. MAG4 (Magnetogram Forecast), developed originally for NASA/SRAG (Space Radiation Analysis Group), is an automated program that analyzes magnetograms from the HMI (Helioseismic and Magnetic Imager) instrument on NASA SDO (Solar Dynamics Observatory), and automatically converts the rate (or probability) of major flares (M- and X-class), Coronal Mass Ejections (CMEs), and Solar Energetic Particle Events.
1993 Solid Waste Reference Forecast Summary
Valero, O.J.; Blackburn, C.L.; Kaae, P.S.; Armacost, L.L.; Garrett, S.M.K.
This report, which updates WHC-EP-0567, 1992 Solid Waste Reference Forecast Summary, (WHC 1992) forecasts the volumes of solid wastes to be generated or received at the US Department of Energy Hanford Site during the 30-year period from FY 1993 through FY 2022. The data used in this document were collected from Westinghouse Hanford Company forecasts as well as from surveys of waste generators at other US Department of Energy sites who are now shipping or plan to ship solid wastes to the Hanford Site for disposal. These wastes include low-level and low-level mixed waste, transuranic and transuranic mixed waste, and nonradioactive hazardous waste.
Seasonal Runoff Forecasts Based on the Climate Forecast System Version 2
NASA Astrophysics Data System (ADS)
Chen, L.; Mo, K. C.; Shukla, S.; Lettenmaier, D. P.
Seasonal runoff forecasts are needed for many hydroclimatological applications, such as drought outlook, agricultural planning, seasonal hydrologic prediction, and multi-purpose reservoir management. Recently, NOAA National Centers for Environmental Prediction (NCEP) has transitioned to their second generation of the Climate Forecast System (CFSv2) in operation. CFSv2 is a coupled ocean-atmosphere-land model with advanced physics, increased resolution, refined initialization, and improved land surface model, and provides forecasts up to nine months in advance. Information on the accuracy and skill of the CFSv2 forecasts is sought for the daily operation of many applications. In this study, we conduct an assessment of the prediction skill of seasonal runoff forecasts from CFSv2 using its retrospective forecasts from 1982 to 2009. Forecast skill of spatially aggregated cumulative runoff (CR) from direct CFSv2 forecasts and those obtained from the Variable Infiltration Capacity (VIC) model driven by daily precipitation, temperature, and wind forecasts from CFSv2 (i.e., hydroclimate forecasts) are compared with forecasts based on the ensemble streamflow prediction (ESP) technique. All forecasts are verified against historical VIC simulations with input forcing of precipitation and temperature derived from a set of 2131 high-quality index stations selected from the National Climatic Data Center's (NCDC's) Cooperative Observer stations across the contiguous United States. The monthly CR is spatially aggregated to 48 sub-regions created by merging the 221 U.S. Geological Survey (USGS) hydrologic sub-regions in order to evaluate regional characteristics. Preliminary results suggest that forecast skill of CR is seasonally and regionally dependent. Direct runoff forecasts from CFSv2 have the lowest skill on average, indicating limited use for hydrological drought prediction. Month-1 CR prediction from hydroclimate forecasts is superior than that from the other two forecast
Hydrocarbon Rocket Technology Impact Forecasting
NASA Technical Reports Server (NTRS)
Stuber, Eric; Prasadh, Nishant; Edwards, Stephen; Mavris, Dimitri N.
Ever since the Apollo program ended, the development of launch propulsion systems in the US has fallen drastically, with only two new booster engine developments, the SSME and the RS-68, occurring in the past few decades.1 In recent years, however, there has been an increased interest in pursuing more effective launch propulsion technologies in the U.S., exemplified by the NASA Office of the Chief Technologist s inclusion of Launch Propulsion Systems as the first technological area in the Space Technology Roadmaps2. One area of particular interest to both government agencies and commercial entities has been the development of hydrocarbon engines; NASA and the Air Force Research Lab3 have expressed interest in the use of hydrocarbon fuels for their respective SLS Booster and Reusable Booster System concepts, and two major commercially-developed launch vehicles SpaceX s Falcon 9 and Orbital Sciences Antares feature engines that use RP-1 kerosene fuel. Compared to engines powered by liquid hydrogen, hydrocarbon-fueled engines have a greater propellant density (usually resulting in a lighter overall engine), produce greater propulsive force, possess easier fuel handling and loading, and for reusable vehicle concepts can provide a shorter turnaround time between launches. These benefits suggest that a hydrocarbon-fueled launch vehicle would allow for a cheap and frequent means of access to space.1 However, the time and money required for the development of a new engine still presents a major challenge. Long and costly design, development, testing and evaluation (DDT&E) programs underscore the importance of identifying critical technologies and prioritizing investment efforts. Trade studies must be performed on engine concepts examining the affordability, operability, and reliability of each concept, and quantifying the impacts of proposed technologies. These studies can be performed through use of the Technology Impact Forecasting (TIF) method. The Technology Impact
Evaluation of numerical weather prediction model precipitation forecasts for use in short-term streamflow forecasting
NASA Astrophysics Data System (ADS)
Shrestha, D. L.; Robertson, D. E.; Wang, Q. J.; Pagano, T. C.; Hapuarachchi, P.
The quality of precipitation forecasts from four Numerical Weather Prediction (NWP) models is evaluated over the Ovens catchment in southeast Australia. Precipitation forecasts are compared with observed precipitation at point and catchment scales and at different temporal resolutions. The four models evaluated are the Australian Community Climate Earth-System Simulator (ACCESS) including ACCESS-G with a 80 km resolution, ACCESS-R 37.5 km, ACCESS-A 12 km, and ACCESS-VT 5 km. The high spatial resolution NWP models (ACCESS-A and ACCESS-VT) appear to be relatively free of bias (i.e. <30%) for 24 h total precipitation forecasts. The low resolution models (ACCESS-R and ACCESS-G) have widespread systematic biases as large as 70%. When evaluated at finer spatial and temporal resolution (e.g. 5 km, hourly) against station observations, the precipitation forecasts appear to have very little skill. There is moderate skill at short lead times when the forecasts are averaged up to daily and/or catchment scale. The skill decreases with increasing lead times and the global model ACCESS-G does not have significant skill beyond 7 days. The precipitation forecasts fail to produce a diurnal cycle shown in observed precipitation. Significant sampling uncertainty in the skill scores suggests that more data are required to get a reliable evaluation of the forecasts. Future work is planned to assess the benefits of using the NWP rainfall forecasts for short-term streamflow forecasting. Our findings here suggest that it is necessary to remove the systematic biases in rainfall forecasts, particularly those from low resolution models, before the rainfall forecasts can be used for streamflow forecasting.
Weather forecast needs from the viewpoint of hydrology
Thomas, Donald M.; Buchanan, Thomas J.
Hydrologists now depend on directly observed data in their forecasting and only infrequently use meteorological forecasts. Case studies show how reliable meteorological forecasts could be beneficial in flood and drought situations. Hydrologists need meteorological forecasts that recognize spatial variability, that are unbiased, and that have a specified degree of uncertainty. (USGS)
Comparison of Five Weather Forecast Methods at Four California Locations
NASA Astrophysics Data System (ADS)
Dempsey, D. P.; Garcia, O.; Frieberg, E.; Tidwell, W.; Chow, B.; Daquigan, D.; Long, D.; Tan, K.
In this project we compare five methods of forecasting maximum and minimum temperature and probability of precipitation at four California locations: California Academy of Sciences in San Francisco, Oakland Museum, Sacramento Executive Airport, and Truckee Airport. The five methods are applied to make 24-hour forecasts twice weekly during the period from August 18 to December 2, 2003. The five forecast methods include: (1) Persistence. A persistence forecast assumes that tomorrow's weather will be the same as today's. (2) Climatology. Our climatology-based forecasts use weather conditions for the day at or very near each of the four locations, averaged over the 30-year period from 1971 to 2000. (3) Official National Weather Service (NWS) forecasts. We use the official NWS forecasts for Oakland Museum, Sacramento Executive Airport, and Truckee Airport. For The California Academy of Sciences (CAS) we use the NWS's new Prototype Digital Forecast for the CAS's latitude and longitude. (4) Individual student forecasts, made by four 10th grade students from San Francisco's Burton High School. They consulted the most recent meteograms, satellite images, soundings, synoptic analyses, and computer model forecasts, as well as climatology, persistence, and NWS forecasts. (5) A consensus of student forecasts, comprising the average of the four student forecasts. We calculate forecast error by squaring the difference between a forecast and the verifying observation, and compare the forecast methods based on these errors.
48 CFR 232.072-3 - Cash flow forecasts.
Code of Federal Regulations, 2011 CFR
... problems. (c) Single or one-time cash flow forecasts are of limited forecasting power. As such, they should... 48 Federal Acquisition Regulations System 3 2011-10-01 2011-10-01 false Cash flow forecasts. 232..., DEPARTMENT OF DEFENSE GENERAL CONTRACTING REQUIREMENTS CONTRACT FINANCING 232.072-3 Cash flow forecasts....
48 CFR 232.072-3 - Cash flow forecasts.
Code of Federal Regulations, 2012 CFR
... problems. (c) Single or one-time cash flow forecasts are of limited forecasting power. As such, they should... 48 Federal Acquisition Regulations System 3 2012-10-01 2012-10-01 false Cash flow forecasts. 232..., DEPARTMENT OF DEFENSE GENERAL CONTRACTING REQUIREMENTS CONTRACT FINANCING 232.072-3 Cash flow forecasts....
48 CFR 232.072-3 - Cash flow forecasts.
Code of Federal Regulations, 2014 CFR
... problems. (c) Single or one-time cash flow forecasts are of limited forecasting power. As such, they should... 48 Federal Acquisition Regulations System 3 2014-10-01 2014-10-01 false Cash flow forecasts. 232..., DEPARTMENT OF DEFENSE GENERAL CONTRACTING REQUIREMENTS CONTRACT FINANCING 232.072-3 Cash flow forecasts....
48 CFR 232.072-3 - Cash flow forecasts.
Code of Federal Regulations, 2013 CFR
... problems. (c) Single or one-time cash flow forecasts are of limited forecasting power. As such, they should... 48 Federal Acquisition Regulations System 3 2013-10-01 2013-10-01 false Cash flow forecasts. 232..., DEPARTMENT OF DEFENSE GENERAL CONTRACTING REQUIREMENTS CONTRACT FINANCING 232.072-3 Cash flow forecasts....
Prediction Techniques in Operational Space Weather Forecasting
NASA Astrophysics Data System (ADS)
Zhukov, Andrei
The importance of forecasting space weather conditions is steadily increasing as our society is becoming more and more dependent on advanced technologies that may be affected by disturbed space weather. Operational space weather forecasting is still a difficult task that requires the real-time availability of input data and specific prediction techniques that are reviewed in this presentation, with an emphasis on solar and interplanetary weather. Key observations that are essential for operational space weather forecasting are listed. Predictions made on the base of empirical and statistical methods, as well as physical models, are described. Their validation, accuracy, and limitations are discussed in the context of operational forecasting. Several important problems in the scientific basis of predicting space weather are described, and possible ways to overcome them are discussed, including novel space-borne observations that could be available in future.
Impact of Seasonal Forecasts on Agriculture
NASA Astrophysics Data System (ADS)
Aldor-Noiman, S. C.
More extreme and volatile weather conditions are a threat to U.S. agricultural productivity today, as multiple environmental conditions during the growing season impact crop yields. That's why farmers' agronomic management decisions are dominated by consideration for near, medium and seasonal forecasts of climate. The Climate Corporation aims to help farmers around the world protect and improve their farming operations by providing agronomic decision support tools that leverage forecasts on multiple timescales to provide valuable insights directly to farmers. In this talk, we will discuss the impact of accurate seasonal forecasts on major decisions growers face each season. We will also discuss assessment and evaluation of seasonal forecasts in the context of agricultural applications.
Flood Forecasting in River System Using ANFIS
NASA Astrophysics Data System (ADS)
Ullah, Nazrin; Choudhury, P.
The aim of the present study is to investigate applicability of artificial intelligence techniques such as ANFIS (Adaptive Neuro-Fuzzy Inference System) in forecasting flood flow in a river system. The proposed technique combines the learning ability of neural network with the transparent linguistic representation of fuzzy system. The technique is applied to forecast discharge at a downstream station using flow information at various upstream stations. A total of three years data has been selected for the implementation of this model. ANFIS models with various input structures and membership functions are constructed, trained and tested to evaluate efficiency of the models. Statistical indices such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and Coefficient of Efficiency (CE) are used to evaluate performance of the ANFIS models in forecasting river flood. The values of the indices show that ANFIS model can accurately and reliably be used to forecast flood in a river system.
Flood forecasting for Tucurui Hydroelectrical Plant, Brazil
Solomon, S.I.; Basso, E.; Osorio, C.; Melo de Moraes, H.; Serrano, A.
The construction of the Tucurui Hydroelectric Plant on the Tocantins River basin in Brazil requires flood forecasting to ensure the safety of the cofferdam. The latter has been initially designed for a flood with a return frequency of one in 25 years. Lack of adequate forecasting facilities during the earlier stages of construction has resulted in significant damages and construction delays. Statistical forecasting models were developed by Projeto de Hidrologia y Climatologie da Amazonia (PHCA) for the purpose of preventing further damage at the site. The application of these models during the 1980 flood season, when the highest flood on record occurred at the Tucurui site, proved of great assistance in preventing the flooding of the cofferdam. In conjunction with the development of these models a number of data collection platforms using data transmission through the GOES system were installed to provide the data required for forecasting.
NOAA's Space Weather Prediction Center, Forecast Office
The Forecast Office of NOAA's Space Weather Prediction Center is the nation's official source of alerts, warnings, and watches. The office, staffed 24/7, is always vigilant for solar activity that ...
Forecasting in the presence of expectations
NASA Astrophysics Data System (ADS)
Allen, R.; Zivin, J. G.; Shrader, J.
Physical processes routinely influence economic outcomes, and actions by economic agents can, in turn, influence physical processes. This feedback creates challenges for forecasting and inference, creating the potential for complementarity between models from different academic disciplines. Using the example of prediction of water availability during a drought, we illustrate the potential biases in forecasts that only take part of a coupled system into account. In particular, we show that forecasts can alter the feedbacks between supply and demand, leading to inaccurate prediction about future states of the system. Although the example is specific to drought, the problem of feedback between expectations and forecast quality is not isolated to the particular model-it is relevant to areas as diverse as population assessments for conservation, balancing the electrical grid, and setting macroeconomic policy.
Purposes and methods of scoring earthquake forecasts
NASA Astrophysics Data System (ADS)
Zhuang, J.
There are two kinds of purposes in the studies on earthquake prediction or forecasts: one is to give a systematic estimation of earthquake risks in some particular region and period in order to give advice to governments and enterprises for the use of reducing disasters, the other one is to search for reliable precursors that can be used to improve earthquake prediction or forecasts. For the first case, a complete score is necessary, while for the latter case, a partial score, which can be used to evaluate whether the forecasts or predictions have some advantages than a well know model, is necessary. This study reviews different scoring methods for evaluating the performance of earthquake prediction and forecasts. Especially, the gambling scoring method, which is developed recently, shows its capacity in finding good points in an earthquake prediction algorithm or model that are not in a reference model, even if its overall performance is no better than the reference model.
Objective Lightning Probability Forecast Tool Phase II
NASA Technical Reports Server (NTRS)
Lambert, Winnie
This presentation describes the improvement of a set of lightning probability forecast equations that are used by the 45th Weather Squadron forecasters for their daily 1100 UTC (0700 EDT) weather briefing during the warm season months of May-September. This information is used for general scheduling of operations at Cape Canaveral Air Force Station and Kennedy Space Center. Forecasters at the Spaceflight Meteorology Group also make thunderstorm forecasts during Shuttle flight operations. Five modifications were made by the Applied Meteorology Unit: increased the period of record from 15 to 17 years, changed the method of calculating the flow regime of the day, calculated a new optimal layer relative humidity, used a new smoothing technique for the daily climatology, and used a new valid area. The test results indicated that the modified equations showed and increase in skill over the current equations, good reliability, and an ability to distinguish between lightning and non-lightning days.
Moisture Forecast Bias Correction in GEOS DAS
NASA Technical Reports Server (NTRS)
Dee, D.
Data assimilation methods rely on numerous assumptions about the errors involved in measuring and forecasting atmospheric fields. One of the more disturbing of these is that short-term model forecasts are assumed to be unbiased. In case of atmospheric moisture, for example, observational evidence shows that the systematic component of errors in forecasts and analyses is often of the same order of magnitude as the random component. we have implemented a sequential algorithm for estimating forecast moisture bias from rawinsonde data in the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The algorithm is designed to remove the systematic component of analysis errors and can be easily incorporated in an existing statistical data assimilation system. We will present results of initial experiments that show a significant reduction of bias in the GEOS DAS moisture analyses.
Flood Forecasting in River System Using ANFIS
Ullah, Nazrin; Choudhury, P.
The aim of the present study is to investigate applicability of artificial intelligence techniques such as ANFIS (Adaptive Neuro-Fuzzy Inference System) in forecasting flood flow in a river system. The proposed technique combines the learning ability of neural network with the transparent linguistic representation of fuzzy system. The technique is applied to forecast discharge at a downstream station using flow information at various upstream stations. A total of three years data has been selected for the implementation of this model. ANFIS models with various input structures and membership functions are constructed, trained and tested to evaluate efficiency of the models. Statistical indices such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and Coefficient of Efficiency (CE) are used to evaluate performance of the ANFIS models in forecasting river flood. The values of the indices show that ANFIS model can accurately and reliably be used to forecast flood in a river system.
NOAA 's operational geostationary satellite retrievals of aerosol optical depths (AODs) were used to verify National Weather Service (NWS) experimental (research mode) particulate matter (PM2.5) forecast guidance issued during the summer 2004 International Consortium for Atmosp...
Groundwater Forecasting Optimization Pertain to Dam Removal
NASA Astrophysics Data System (ADS)
Brown, L.; Berthelote, A. R.
There is increasing interest in removing dams due to changing ecological and societal values. Groundwater recharge rate is closely connected to reservoir presence or absence. With the removal of dams and their associated reservoirs, reductions in groundwater levels are likely to impact water supplies for domestic, industrial and agricultural use. Therefore accessible economic and time effective tools to forecast groundwater level declines with acceptable uncertainty following dam removals are critical for public welfare and healthy regional economies. These tools are also vital to project planning and provide beneficial information for restoration and remediation managements. The standard tool for groundwater forecasting is 3D Numerical modeling. Artificial Neural Networks (ANNs) may be an alternative tool for groundwater forecasting pertain to dam removal. This project compared these two tools throughout the Milltown Dam removal in Western Montana over a five year period. It was determined that ANN modeling had equal or greater accuracy for groundwater forecasting with far less effort and cost involved.
Satellite-advection based solar forecasting: lessons learned and progress towards probabalistic solar forecasting
NASA Astrophysics Data System (ADS)
Rogers, M. A.
Using satellite observations from GOES-E and GOES-W platforms in concert with GFS-derived cloud-level winds and a standalone radiative transfer model, an advection-derived forecast for surface GHI over the continental United States, with intercomparison between forecasts for four zones over the CONUS and Central Pacific with SURFRAD results. Primary sources for error in advection-based forecasts, primarily driven by false- or mistimed ramp events are discussed, with identification of error sources quantified along with techniques used to improve advection-based forecasts to approximately 10% MAE for designated surface locations. Development of a blended steering wind product utilizing NWP output combined with satellite-derived winds from AMV techniques to improve 0-1 hour advection forecasts will be discussed. Additionally, the use of two years' of solar forecast observations in the development of a prototype probablistic forecast for ramp events will be shown, with the intent of increasing the use of satellite-derived forecasts for grid operators and optimizing integration of renewable resources into the power grid. Elements of the work were developed under the 'Public-Private-Academic Partnership to Advance Solar Power Forecasting' project spearheaded by the National Center for Atmospheric Research.
From short term power forecasting to nowcasting - Benefiting from meteorological forecasts and measurements
NASA Astrophysics Data System (ADS)
Mey, Britta; Braun, Axel; Good, Garrett; Vogt, Stephan; Wessel, Arne; Dobschinski, Jan
Today, wind and solar power forecasts with time horizons from zero to about three hours are essential for the reliable grid and market integration of wind and solar energy. With respect to closure times of German intra-day markets, power forecasts with time horizons of about one to two hours and an update frequency of 15 minutes are required for final trading activities, reducing the uncertainty of the day-ahead forecast of the previous day. Regarding grid security aspects, grid operators utilize such forecasts to create continuous intra-day grid congestion forecasts. In addition to these preventive measures, wind and solar power become more and more important for the provision of ancillary services by wind and solar farm operators. This use case mainly requires power forecasts with time horizons of less than one hour. In general, forecasts with time horizons below three hours are investigated within the nowcasting research area. Nowcasting models are mainly based on current observations and extrapolation methods. With respect to wind and solar power forecasts with horizons of up to three hours, it has been shown in studies that real-time power measurements have the highest information content as compared to other potential model input parameters. We will present results from studies focusing on the benefit of meteorological data (forecasts and/or measurements) in the field of solar and wind power forecasts with time horizons of up to a few hours. Wind farm forecast errors are for example reduced by using numerical weather prediction (NWP) data in the wind power prediction model along with real-time wind farm power measurements. Furthermore, spatially distributed NWP data in combination with German total wind power measurements helped in the reduction of extreme forecast errors. By using global radiation forecasts as an input for wind power forecasts, forecast error during sunrise and sunset could be reduced. In the field of German total solar power, nowcasting
Hydrological Forecasting in Mexico: Extending the University of Washington West-wide Seasonal Hydrologic Forecast System
NASA Astrophysics Data System (ADS)
Munoz-Arriola, F.; Thomas, G.; Wood, A.; Wagner-Gomez, A.; Lobato-Sanchez, R.; Lettenmaier, D. P.
Hydrologic forecasting in areas constrained by the availability of hydrometeorological records is a notable challenge in water resource management. Techniques from the University of Washington West-wide Seasonal Hydrologic Forecast system www.hydro.washington.edu/forecast/westwide) for generating daily nowcasts in areas with sparse and time-varying station coverage have been extended from the western U.S. into Mexico. The primary forecasting approaches consist of ensembles based on the NWS ensemble streamflow prediction method (ESP; essentially resampling of climatology) and on NCEP Coupled Forecast System (CFS) outputs. These in turn are used to force the Variable Infiltration Capacity (VIC) macroscale hydrology model to produce streamflow ensembles. The initial hydrologic state utilized in the seasonal forecasting is generated by VIC using daily real-time hydrologic nowcasts, produced using forcings derived via an 'index-station percentile' approach from meteorological station data accessed in real time from Servicio Meteorológico Nacional (SMN). One-year lead time streamflow forecasts at monthly time step are produced at a set of major river locations in Mexico. As a case study, the streamflow forecasts, along with forecasts of reservoir evaporation, are used as input to the Simulation-Optimization (SIMOP) model of the Rio Yaqui system, one of the major agricultural production centers of Mexico. This is the first step in an eventual planned water management implementation over all of Mexico.
Ensemble Forecasts with Useful Skill-Spread Relationships for African meningitis and Asia Streamflow Forecasting
NASA Astrophysics Data System (ADS)
Hopson, T. M.
One potential benefit of an ensemble prediction system (EPS) is its capacity to forecast its own forecast error through the ensemble spread-error relationship. In practice, an EPS is often quite limited in its ability to represent the variable expectation of forecast error through the variable dispersion of the ensemble, and perhaps more fundamentally, in its ability to provide enough variability in the ensembles dispersion to make the skill-spread relationship even potentially useful (irrespective of whether the EPS is well-calibrated or not). In this paper we examine the ensemble skill-spread relationship of an ensemble constructed from the TIGGE (THORPEX Interactive Grand Global Ensemble) dataset of global forecasts and a combination of multi-model and post-processing approaches. Both of the multi-model and post-processing techniques are based on quantile regression (QR) under a step-wise forward selection framework leading to ensemble forecasts with both good reliability and sharpness. The methodology utilizes the ensemble's ability to self-diagnose forecast instability to produce calibrated forecasts with informative skill-spread relationships. A context for these concepts is provided by assessing the constructed ensemble in forecasting district-level humidity impacting the incidence of meningitis in the meningitis belt of Africa, and in forecasting flooding events in the Brahmaputra and Ganges basins of South Asia.
Probabilistic regional wind power forecasts based on calibrated Numerical Weather Forecast ensembles
NASA Astrophysics Data System (ADS)
Späth, Stephan; von Bremen, Lueder; Junk, Constantin; Heinemann, Detlev
With increasing shares of installed wind power in Germany, accurate forecasts of wind speed and power get increasingly important for the grid integration of Renewable Energies. Applications like grid management and trading also benefit from uncertainty information. This uncertainty information can be provided by ensemble forecasts. These forecasts often exhibit systematic errors such as biases and spread deficiencies. The errors can be reduced by statistical post-processing. We use forecast data from the regional Numerical Weather Prediction model COSMO-DE EPS as input to regional wind power forecasts. In order to enhance the power forecast, we first calibrate the wind speed forecasts against the model analysis, so some of the model's systematic errors can be removed. Wind measurements at every grid point are usually not available and as we want to conduct grid zone forecasts, the model analysis is the best target for calibration. We use forecasts from the COSMO-DE EPS, a high-resolution ensemble prediction system with 20 forecast members. The model covers the region of Germany and surroundings with a vertical resolution of 50 model levels and a horizontal resolution of 0.025 degrees (approximately 2.8 km). The forecast range is 21 hours with model output available on an hourly basis. Thus, we use it for shortest-term wind power forecasts. The COSMO-DE EPS was originally designed with a focus on forecasts of convective precipitation. The COSMO-DE EPS wind speed forecasts at hub height were post-processed by nonhomogenous Gaussian regression (NGR; Thorarinsdottir and Gneiting, 2010), a calibration method that fits a truncated normal distribution to the ensemble wind speed forecasts. As calibration target, the model analysis was used. The calibration is able to remove some deficits of the COSMO-DE EPS. In contrast to the raw ensemble members, the calibrated ensemble members do not show anymore the strong correlations with each other and the spread-skill relationship
A global flash flood forecasting system
NASA Astrophysics Data System (ADS)
Baugh, Calum; Pappenberger, Florian; Wetterhall, Fredrik; Hewson, Tim; Zsoter, Ervin
The sudden and devastating nature of flash flood events means it is imperative to provide early warnings such as those derived from Numerical Weather Prediction (NWP) forecasts. Currently such systems exist on basin, national and continental scales in Europe, North America and Australia but rely on high resolution NWP forecasts or rainfall-radar nowcasting, neither of which have global coverage. To produce global flash flood forecasts this work investigates the possibility of using forecasts from a global NWP system. In particular we: (i) discuss how global NWP can be used for flash flood forecasting and discuss strengths and weaknesses; (ii) demonstrate how a robust evaluation can be performed given the rarity of the event; (iii) highlight the challenges and opportunities in communicating flash flood uncertainty to decision makers; and (iv) explore future developments which would significantly improve global flash flood forecasting. The proposed forecast system uses ensemble surface runoff forecasts from the ECMWF H-TESSEL land surface scheme. A flash flood index is generated using the ERIC (Enhanced Runoff Index based on Climatology) methodology [Raynaud et al., 2014]. This global methodology is applied to a series of flash floods across southern Europe. Results from the system are compared against warnings produced using the higher resolution COSMO-LEPS limited area model. The global system is evaluated by comparing forecasted warning locations against a flash flood database of media reports created in partnership with floodlist.com. To deal with the lack of objectivity in media reports we carefully assess the suitability of different skill scores and apply spatial uncertainty thresholds to the observations. To communicate the uncertainties of the flash flood system output we experiment with a dynamic region-growing algorithm. This automatically clusters regions of similar return period exceedence probabilities, thus presenting the at-risk areas at a spatial
Influenza Forecasting with Google Flu Trends
Dugas, Andrea F.; Jalalpour, Mehdi; Gel, Yulia; Levin, Scott; Torcaso, Fred; Igusa, Takeru; Rothman, Richard
Objective We sought to develop a practical influenza forecast model, based on real-time, geographically focused, and easy to access data, to provide individual medical centers with advanced warning of the number of influenza cases, thus allowing sufficient time to implement an intervention. Secondly, we evaluated how the addition of a real-time influenza surveillance system, Google Flu Trends, would impact the forecasting capabilities of this model. Introduction Each year, influenza results in increased Emergency Department crowding which can be mitigated through early detection linked to an appropriate response. Although current surveillance systems, such as Google Flu Trends, yield near real-time influenza surveillance, few demonstrate ability to forecast impending influenza cases. Methods Forecasting models designed to predict one week in advance were developed from weekly counts of confirmed influenza cases over seven seasons (2004 – 2011) divided into training and out-of-sample verification sets. Forecasting procedures using classical Box-Jenkins, generalized linear, and autoregressive methods were employed to develop the final model and assess the relative contribution of external variables such as, Google Flu Trends, meteorological data, and temporal information. Models were developed and evaluated through statistical measures of global deviance and log-likelihood ratio tests. An additional measure of forecast confidence, defined as the percentage of forecast values, during an influenza peak, that are within 7 influenza cases of the actual data, was examined to demonstrate practical utility of the model. Results A generalized autoregressive Poisson (GARMA) forecast model integrating previous influenza cases with Google Flu Trends information provided the most accurate influenza case predictions. Google Flu Trend data was the only source of external information providing significant forecast improvements (p = 0.00002). The final model, a GARMA intercept
The HFIP High Resolution Hurricane Forecast Test
NASA Astrophysics Data System (ADS)
Nance, L. B.; Bernardet, L.; Bao, S.; Brown, B.; Carson, L.; Fowler, T.; Halley Gotway, J.; Harrop, C.; Szoke, E.; Tollerud, E. I.; Wolff, J.; Yuan, H.
Tropical cyclones are a serious concern for the nation, causing significant risk to life, property and economic vitality. The National Oceanic and Atmospheric Administration (NOAA) National Weather Service has a mission of issuing tropical cyclone forecasts and warnings, aimed at protecting life and property and enhancing the national economy. In the last 10 years, the errors in hurricane track forecasts have been reduced by about 50% through improved model guidance, enhanced observations, and forecaster expertise. However, little progress has been made during this period toward reducing forecasted intensity errors. To address this shortcoming, NOAA established the Hurricane Forecast Improvement Project (HFIP) in 2007. HFIP is a 10-year plan to improve one to five day tropical cyclone forecasts, with a focus on rapid intensity change. Recent research suggests that prediction models with grid spacing less than 1 km in the inner core of the hurricane may provide a substantial improvement in intensity forecasts. The 2008-09 staging of the High Resolution Hurricane (HRH) Test focused on quantifying the impact of increased horizontal resolution in numerical models on hurricane intensity forecasts. The primary goal of this test was an evaluation of the effect of increasing horizontal resolution within a given model across a variety of storms with different intensity, location and structure. The test focused on 69 retrospectives cases from the 2005 and 2007 hurricane seasons. Six modeling groups participated in the HRH test utilizing a variety of models, including three configurations of the Weather Research and Forecasting (WRF) model, the operational GFDL model, the Navy's tropical cyclone model, and a model developed at the University of Wisconsin-Madison (UWM). The Development Testbed Center (DTC) was tasked with providing objective verification statistics for a variety of metrics. This presentation provides an overview of the HRH Test and a summary of the standard
Application of hydrologic forecast model.
Hua, Xu; Hengxin, Xue; Zhiguo, Chen
In order to overcome the shortcoming of the solution may be trapped into the local minimization in the traditional TSK (Takagi-Sugeno-Kang) fuzzy inference training, this paper attempts to consider the TSK fuzzy system modeling approach based on the visual system principle and the Weber law. This approach not only utilizes the strong capability of identifying objects of human eyes, but also considers the distribution structure of the training data set in parameter regulation. In order to overcome the shortcoming of it adopting the gradient learning algorithm with slow convergence rate, a novel visual TSK fuzzy system model based on evolutional learning is proposed by introducing the particle swarm optimization algorithm. The main advantage of this method lies in its very good optimization, very strong noise immunity and very good interpretability. The new method is applied to long-term hydrological forecasting examples. The simulation results show that the method is feasible and effective, the new method not only inherits the advantages of traditional visual TSK fuzzy models but also has the better global convergence and accuracy than the traditional model. PMID:22699326
Multispectral satellite training for inexperienced Navy forecasters
NASA Astrophysics Data System (ADS)
Kuciauskas, Arunas P.; Lee, Thomas F.; Durkee, Philip A.; Ledesma, Roy
Recent advancements of meteorology and oceanography (METOC) satellite products has resulted from a surge in computing resources and expanded communications via the Internet. Greater tactical demands in military operations are placed on Navy and Marine METOC personnel to provide better atmospheric depictions and forecasts in support of helicopter, fighter jet and ground troop operations, as was experienced in Operation Enduring Freedom and Operation Iraqi Freedom. Unfortunately, US military weather forecasters are often limited in their abilities to provide state of the art products and forecasts. One reason for these inefficiencies are that oftentimes, daily forecasting tasks are left to non-commissioned personnel (e.g., AG"s and ET"s) who receive little or no classroom training in this area, nor are there continuing education/training available. METOC forecast centers vary greatly and might not have access to the appropriate information base to answer ongoing questions. Additionally, the typical tour of duty at a particular forecast center is 2 years, resulting in a stressful environment to bring new forecasters up to speed in demanding forecast operations. The result is that the user is often confined to image looping and basic image enhancements to convey the general environmental conditions over the region of interest. To facilitate the learning process, the Naval Research Laboratory and the Naval Postgraduate School have developed a 3 day intensive laboratory and lecture course in satellite remote sensing, focusing on topics vital to military operations such as smoke and fire detection, coastal maritime layer analysis, snow, fog, haze, tropical cyclones, hazardous wind conditions, etc. A wealth of satellite data is provided from MODIS, AVHRR, DMSP and Geostationary satellite data. Background satellite remote sensing topics such as radiative transfer theory is also presented. This report presents a sample of the material used within the training.
Global Storm Surge Forecasting and Information System
NASA Astrophysics Data System (ADS)
Buckman, Lorraine; Verlaan, Martin; Weerts, Albrecht
The Global Storm Surge Forecasting and Information System is a first-of-its-kind operational forecasting system for storm surge prediction on a global scale, taking into account tidal and extra-tropical storm events in real time. The system, built and hosted by Deltares, provides predictions of water level and surge height up to 10 days in advance from numerical simulations and measurement data integrated within an operational IT environment. The Delft-FEWS software provides the operational environment in which wind forecasts and measurement data are collected and processed, and serves as a platform from which to run the numerical model. The global Delft3D model is built on a spherical, flexible mesh with a resolution around 5 km in near-shore coastal waters and an offshore resolution of 50 km to provide detailed information at the coast while limiting the computational time required. By using a spherical grid, the model requires no external boundary conditions. Numerical global wind forecasts are used as forcing for the model, with plans to incorporate regional meteorological forecasts to better capture smaller, tropical storms using the Wind Enhanced Scheme for generation of tropical winds (WES). The system will be automated to collect regional wind forecasts and storm warning bulletins which are incorporated directly into the model calculations. The forecasting system provides real-time water level and surge information in areas that currently lack local storm surge prediction capability. This information is critical for coastal communities in planning their flood strategy and during disaster response. The system is also designed to supply boundary conditions for coupling finer-scale regional models. The Global Storm Surge Forecasting and Information System is run within the Deltares iD-Lab initiative aiming at collaboration with universities, consultants and interested organizations. The results of the system will be made available via standards such as net
Influenza Forecasting with Google Flu Trends
Dugas, Andrea Freyer; Jalalpour, Mehdi; Gel, Yulia; Levin, Scott; Torcaso, Fred; Igusa, Takeru; Rothman, Richard E.
Background We developed a practical influenza forecast model based on real-time, geographically focused, and easy to access data, designed to provide individual medical centers with advanced warning of the expected number of influenza cases, thus allowing for sufficient time to implement interventions. Secondly, we evaluated the effects of incorporating a real-time influenza surveillance system, Google Flu Trends, and meteorological and temporal information on forecast accuracy. Methods Forecast models designed to predict one week in advance were developed from weekly counts of confirmed influenza cases over seven seasons (2004–2011) divided into seven training and out-of-sample verification sets. Forecasting procedures using classical Box-Jenkins, generalized linear models (GLM), and generalized linear autoregressive moving average (GARMA) methods were employed to develop the final model and assess the relative contribution of external variables such as, Google Flu Trends, meteorological data, and temporal information. Results A GARMA(3,0) forecast model with Negative Binomial distribution integrating Google Flu Trends information provided the most accurate influenza case predictions. The model, on the average, predicts weekly influenza cases during 7 out-of-sample outbreaks within 7 cases for 83% of estimates. Google Flu Trend data was the only source of external information to provide statistically significant forecast improvements over the base model in four of the seven out-of-sample verification sets. Overall, the p-value of adding this external information to the model is 0.0005. The other exogenous variables did not yield a statistically significant improvement in any of the verification sets. Conclusions Integer-valued autoregression of influenza cases provides a strong base forecast model, which is enhanced by the addition of Google Flu Trends confirming the predictive capabilities of search query based syndromic surveillance. This accessible and
Spatiotemporal drought forecasting using nonlinear models
NASA Astrophysics Data System (ADS)
Vasiliades, Lampros; Loukas, Athanasios
Spatiotemporal data mining is the extraction of unknown and implicit knowledge, structures, spatiotemporal relationships, or patterns not explicitly stored in spatiotemporal databases. As one of data mining techniques, forecasting is widely used to predict the unknown future based upon the patterns hidden in the current and past data. In order to achieve spatiotemporal forecasting, some mature analysis tools, e.g., time series and spatial statistics are extended to the spatial dimension and the temporal dimension, respectively. Drought forecasting plays an important role in the planning and management of natural resources and water resource systems in a river basin. Early and timelines forecasting of a drought event can help to take proactive measures and set out drought mitigation strategies to alleviate the impacts of drought. Despite the widespread application of nonlinear mathematical models, comparative studies on spatiotemporal drought forecasting using different models are still a huge task for modellers. This study uses a promising approach, the Gamma Test (GT), to select the input variables and the training data length, so that the trial and error workload could be greatly reduced. The GT enables to quickly evaluate and estimate the best mean squared error that can be achieved by a smooth model on any unseen data for a given selection of inputs, prior to model construction. The GT is applied to forecast droughts using monthly Standardized Precipitation Index (SPI) timeseries at multiple timescales in several precipitation stations at Pinios river basin in Thessaly region, Greece. Several nonlinear models have been developed efficiently, with the aid of the GT, for 1-month up to 12-month ahead forecasting. Several temporal and spatial statistical indices were considered for the performance evaluation of the models. The predicted results show reasonably good agreement with the actual data for short lead times, whereas the forecasting accuracy decreases with
Forecasting Tools Point to Fishing Hotspots
NASA Technical Reports Server (NTRS)
Private weather forecaster WorldWinds Inc. of Slidell, Louisiana has employed satellite-gathered oceanic data from Marshall Space Flight Center to create a service that is every fishing enthusiast s dream. The company's FishBytes system uses information about sea surface temperature and chlorophyll levels to forecast favorable conditions for certain fish populations. Transmitting the data to satellite radio subscribers, FishBytes provides maps that guide anglers to the areas they are most likely to make their favorite catch.
Nambe Pueblo Water Budget and Forecasting model.
Brainard, James Robert
This report documents The Nambe Pueblo Water Budget and Water Forecasting model. The model has been constructed using Powersim Studio (PS), a software package designed to investigate complex systems where flows and accumulations are central to the system. Here PS has been used as a platform for modeling various aspects of Nambe Pueblo's current and future water use. The model contains three major components, the Water Forecast Component, Irrigation Scheduling Component, and the Reservoir Model Component. In each of the components, the user can change variables to investigate the impacts of water management scenarios on future water use. The Water Forecast Component includes forecasting for industrial, commercial, and livestock use. Domestic demand is also forecasted based on user specified current population, population growth rates, and per capita water consumption. Irrigation efficiencies are quantified in the Irrigated Agriculture component using critical information concerning diversion rates, acreages, ditch dimensions and seepage rates. Results from this section are used in the Water Demand Forecast, Irrigation Scheduling, and the Reservoir Model components. The Reservoir Component contains two sections, (1) Storage and Inflow Accumulations by Categories and (2) Release, Diversion and Shortages. Results from both sections are derived from the calibrated Nambe Reservoir model where historic, pre-dam or above dam USGS stream flow data is fed into the model and releases are calculated.
Status and Future of Dust Storm Forecasting
NASA Astrophysics Data System (ADS)
Westphal, D. L.
In recent years, increased attention has been given to the large amounts of airborne dust derived from the deserts and desertified areas of the world and transported over scales ranging from local to global. This dust can have positive and negative impacts on human activities and the environment, including modifying cloud formation, fertilizing the ocean, degrading air quality, reducing visibility, transporting pathogens, and inducing respiratory problems. The atmospheric radiative forcing by the dust has implications for global climate change and presently is one of the largest unknowns in climate models. These uncertainties have lead to much of the funding for research into the sources, properties, and fate of atmospheric dust. As a result of advances in numerical weather prediction over the past decades and the recent climate research, we are now in a position to produce operational dust storm forecasts. International organizations and national agencies are developing programs for dust forecasting. The approaches and applications of dust detection and forecasting are as varied as the nations that are developing the models. The basic components of a dust forecasting system include atmospheric forcing, dust production, and dust microphysics. The forecasting applications include air and auto traffic safety, shipping, health, national security, climate and weather. This presentation will summarize the methods of dust storm forecasting and illustrate the various applications. The major remaining uncertainties (e.g. sources and initialization) will be discussed as well as approaches for solving those problems.
Solar Energy Forecast System Development and Implementation
NASA Astrophysics Data System (ADS)
Jascourt, S. D.; Kirk-Davidoff, D. B.; Cassidy, C.
Forecast systems for predicting real-time solar energy generation are being developed along similar lines to those of more established wind forecast systems, but the challenges and constraints are different. Clouds and aerosols play a large role, and for tilted photovoltaic panels and solar concentrating plants, the direct beam irradiance, which typically has much larger forecast errors than global horizontal irradiance, must be utilized. At MDA Information Systems, we are developing a forecast system based on first principles, with the well-validated REST2 clear sky model (Gueymard, 2008) at its backbone. In tuning the model and addressing aerosol scattering and surface albedo, etc., we relied upon the wealth of public data sources including AERONET (aerosol optical depth at different wavelengths), Suominet (GPS integrated water vapor), NREL MIDC solar monitoring stations, SURFRAD (includes upwelling shortwave), and MODIS (albedo in different wavelength bands), among others. The forecast itself utilizes a blend of NWP model output, which must be brought down to finer time resolution based on the diurnal cycle rather than simple interpolation. Many models currently do not output the direct beam irradiance, and one that does appears to have a bias relative to its global horizontal irradiance, with equally good performance attained by utilizing REST2 and the model global radiation to estimate the direct component. We will present a detailed assessment of various NWP solar energy products, evaluating forecast skill at a range of photovoltaic installations.
Weather Forecaster Understanding of Climate Models
NASA Astrophysics Data System (ADS)
Bol, A.; Kiehl, J. T.; Abshire, W. E.
Weather forecasters, particularly those in broadcasting, are the primary conduit to the public for information on climate and climate change. However, many weather forecasters remain skeptical of model-based climate projections. To address this issue, The COMET Program developed an hour-long online lesson of how climate models work, targeting an audience of weather forecasters. The module draws on forecasters' pre-existing knowledge of weather, climate, and numerical weather prediction (NWP) models. In order to measure learning outcomes, quizzes were given before and after the lesson. Preliminary results show large learning gains. For all people that took both pre and post-tests (n=238), scores improved from 48% to 80%. Similar pre/post improvement occurred for National Weather Service employees (51% to 87%, n=22 ) and college faculty (50% to 90%, n=7). We believe these results indicate a fundamental misunderstanding among many weather forecasters of (1) the difference between weather and climate models, (2) how researchers use climate models, and (3) how they interpret model results. The quiz results indicate that efforts to educate the public about climate change need to include weather forecasters, a vital link between the research community and the general public.
Nonlinear forecasting of intertidal shoreface evolution
NASA Astrophysics Data System (ADS)
Grimes, D. J.; Cortale, N.; Baker, K.; McNamara, D. E.
Natural systems dominated by sediment transport are notoriously difficult to forecast. This is particularly true along the ocean coastline, a region that draws considerable human attention as economic investment and infrastructure are threatened by both persistent, long-term and acute, event driven processes (i.e., sea level rise and storm damage, respectively). Forecasting the coastline's evolution over intermediate time (daily) and space (tens of meters) scales is hindered by the complexity of sediment transport and hydrodynamics, and limited access to the detailed local forcing that drives fast scale processes. Modern remote sensing systems provide an efficient, economical means to collect data within these regions. A solar-powered digital camera installation is used to capture the coast's evolution, and machine learning algorithms are implemented to extract the shoreline and estimate the daily mean intertidal coastal profile. Methods in nonlinear time series forecasting and genetic programming applied to these data corroborate that coastal morphology at these scales is predominately driven by nonlinear internal dynamics, which partially mask external forcing signatures. Results indicate that these forecasting techniques achieve nontrivial predictive skill for spatiotemporal forecast of the upper coastline profile (as much as 43% of variance in data explained for one day predictions). This analysis provides evidence that societally relevant coastline forecasts can be achieved without knowing the forcing environment or the underlying dynamical equations that govern coastline evolution.
Nonlinear forecasting of intertidal shoreface evolution.
Grimes, D J; Cortale, N; Baker, K; McNamara, D E
Natural systems dominated by sediment transport are notoriously difficult to forecast. This is particularly true along the ocean coastline, a region that draws considerable human attention as economic investment and infrastructure are threatened by both persistent, long-term and acute, event driven processes (i.e., sea level rise and storm damage, respectively). Forecasting the coastline's evolution over intermediate time (daily) and space (tens of meters) scales is hindered by the complexity of sediment transport and hydrodynamics, and limited access to the detailed local forcing that drives fast scale processes. Modern remote sensing systems provide an efficient, economical means to collect data within these regions. A solar-powered digital camera installation is used to capture the coast's evolution, and machine learning algorithms are implemented to extract the shoreline and estimate the daily mean intertidal coastal profile. Methods in nonlinear time series forecasting and genetic programming applied to these data corroborate that coastal morphology at these scales is predominately driven by nonlinear internal dynamics, which partially mask external forcing signatures. Results indicate that these forecasting techniques achieve nontrivial predictive skill for spatiotemporal forecast of the upper coastline profile (as much as 43% of variance in data explained for one day predictions). This analysis provides evidence that societally relevant coastline forecasts can be achieved without knowing the forcing environment or the underlying dynamical equations that govern coastline evolution. PMID:26520082
Enhancing model based forecasting of geomagnetic storms
NASA Astrophysics Data System (ADS)
Webb, Alla G.
Modern society is increasingly dependent on the smooth operation of large scale technology supporting Earth based activities such as communication, electricity distribution, and navigation. This technology is potentially threatened by global geomagnetic storms, which are caused by the impact of plasma ejected from the Sun upon the protective magnetic field that surrounds the Earth. Forecasting the timing and magnitude of these geomagnetic storms is part of the emerging discipline of space weather. The most severe geomagnetic storms are caused by magnetic clouds, whose properties and characteristics are important variables in space weather forecasting systems. The methodology presented here is the development of a new statistical approach to characterize the physical properties (variables) of the magnetic clouds and to examine the extent to which theoretical models can be used in describing both of these physical properties, as well as their evolution in space and time. Since space weather forecasting is a complex system, a systems engineering approach is used to perform analysis, validation, and verification of the magnetic cloud models (subsystem of the forecasting system) using a model-based methodology. This research demonstrates that in order to validate magnetic cloud models, it is important to categorize the data by physical parameters such as velocity and distance travelled. This understanding will improve the modeling accuracy of magnetic clouds in space weather forecasting systems and hence increase forecasting accuracy of geomagnetic storms and their impact on earth systems.
Traffic flow forecasting: Comparison of modeling approaches
Smith, B.L.; Demetsky, M.J.
The capability to forecast traffic volume in an operational setting has been identified as a critical need for intelligent transportation systems (ITS). In particular, traffic volume forecasts will support proactive, dynamic traffic control. However, previous attempts to develop traffic volume forecasting models have met with limited success. This research effort focused on developing traffic volume forecasting models for two sites on Northern Virginia`s Capital Beltway. Four models were developed and tested for the freeway traffic flow forecasting problem, which is defined as estimating traffic flow 15 min into the future. They were the historical average, time-series, neural network, and nonparametric regression models. The nonparametric regression model significantly outperformed the other models. A Wilcoxon signed-rank test revealed that the nonparametric regression model experienced significantly lower errors than the other models. In addition, the nonparametric regression model was easy to implement, and proved to be portable, performing well at two distinct sites. Based on its success, research is ongoing to refine the nonparametric regression model and to extend it to produce multiple interval forecasts.
Why Don't We Learn to Accurately Forecast Feelings? How Misremembering Our Predictions Blinds Us to Past Forecasting Errors
ERIC Educational Resources Information Center
Meyvis, Tom; Ratner, Rebecca K.; Levav, Jonathan
Why do affective forecasting errors persist in the face of repeated disconfirming evidence? Five studies demonstrate that people misremember their forecasts as consistent with their experience and thus fail to perceive the extent of their forecasting error. As a result, people do not learn from past forecasting errors and fail to adjust subsequent…
Monitoring and seasonal forecasting of meteorological droughts
NASA Astrophysics Data System (ADS)
Dutra, Emanuel; Pozzi, Will; Wetterhall, Fredrik; Di Giuseppe, Francesca; Magnusson, Linus; Naumann, Gustavo; Barbosa, Paulo; Vogt, Jurgen; Pappenberger, Florian
Near-real time drought monitoring can provide decision makers valuable information for use in several areas, such as water resources management, or international aid. Unfortunately, a major constraint in current drought outlooks is the lack of reliable monitoring capability for observed precipitation globally in near-real time. Furthermore, drought monitoring systems requires a long record of past observations to provide mean climatological conditions. We address these constraints by developing a novel drought monitoring approach in which monthly mean precipitation is derived from short-range using ECMWF probabilistic forecasts and then merged with the long term precipitation climatology of the Global Precipitation Climatology Centre (GPCC) dataset. Merging the two makes available a real-time global precipitation product out of which the Standardized Precipitation Index (SPI) can be estimated and used for global or regional drought monitoring work. This approach provides stability in that by-passes problems of latency (lags) in having local rain-gauge measurements available in real time or lags in satellite precipitation products. Seasonal drought forecasts can also be prepared using the common methodology and based upon two data sources used to provide initial conditions (GPCC and the ECMWF ERA-Interim reanalysis (ERAI) combined with either the current ECMWF seasonal forecast or a climatology based upon ensemble forecasts. Verification of the forecasts as a function of lead time revealed a reduced impact on skill for: (i) long lead times using different initial conditions, and (ii) short lead times using different precipitation forecasts. The memory effect of initial conditions was found to be 1 month lead time for the SPI-3, 3 to 4 months for the SPI-6 and 5 months for the SPI-12. Results show that dynamical forecasts of precipitation provide added value, a skill similar to or better than climatological forecasts. In some cases, particularly for long SPI time
Pollen Forecast and Dispersion Modelling
NASA Astrophysics Data System (ADS)
Costantini, Monica; Di Giuseppe, Fabio; Medaglia, Carlo Maria; Travaglini, Alessandro; Tocci, Raffaella; Brighetti, M. Antonia; Petitta, Marcello
The aim of this study is monitoring, mapping and forecast of pollen distribution for the city of Rome using in-situ measurements of 10 species of common allergenic pollens and measurements of PM10. The production of daily concentration maps, associated to a mobile phone app, are innovative compared to existing dedicated services to people who suffer from respiratory allergies. The dispersal pollen is one of the most well-known causes of allergic disease that is manifested by disorders of the respiratory functions. Allergies are the third leading cause of chronic disease and it is estimated that tens millions of people in Italy suffer from it. Recent works reveal that during the last few years there was a progressive increase of affected subjects, especially in urban areas. This situation may depend: on the ability to transport of pollutants, on the ability to react between pollutants and pollen and from a combination of other irritants, existing in densely populated and polluted urban areas. The methodology used to produce maps is based on in-situ measurements time series relative to 2012, obtained from networks of air quality and pollen stations in the metropolitan area of Rome. The monitoring station aerobiological of University of Rome "Tor Vergata" is located at the Department of Biology. The instrument used to pollen monitoring is a volumetric sampler type Hirst (Hirst 1952), Model 2000 VPPS Lanzoni; the data acquisition is carried out as reported in Standard UNI 11008:2004 - "Qualità dell'aria - Metodo di campionamento e conteggio dei granuli pollinici e delle spore fungine aerodisperse" - the protocol that describes the procedure for measuring of the concentration of pollen grains and fungal spores dispersed into the atmosphere, and reported in the "Manuale di gestione e qualità della R.I.M.A" (Travaglini et. al. 2009). All 10 allergenic pollen are monitored since 1996. At Tor Vergata university is also operating a meteorological station (SP2000, CAE
The Invasive Species Forecasting System
NASA Technical Reports Server (NTRS)
Schnase, John; Most, Neal; Gill, Roger; Ma, Peter
The Invasive Species Forecasting System (ISFS) provides computational support for the generic work processes found in many regional-scale ecosystem modeling applications. Decision support tools built using ISFS allow a user to load point occurrence field sample data for a plant species of interest and quickly generate habitat suitability maps for geographic regions of management concern, such as a national park, monument, forest, or refuge. This type of decision product helps resource managers plan invasive species protection, monitoring, and control strategies for the lands they manage. Until now, scientists and resource managers have lacked the data-assembly and computing capabilities to produce these maps quickly and cost efficiently. ISFS focuses on regional-scale habitat suitability modeling for invasive terrestrial plants. ISFS s component architecture emphasizes simplicity and adaptability. Its core services can be easily adapted to produce model-based decision support tools tailored to particular parks, monuments, forests, refuges, and related management units. ISFS can be used to build standalone run-time tools that require no connection to the Internet, as well as fully Internet-based decision support applications. ISFS provides the core data structures, operating system interfaces, network interfaces, and inter-component constraints comprising the canonical workflow for habitat suitability modeling. The predictors, analysis methods, and geographic extents involved in any particular model run are elements of the user space and arbitrarily configurable by the user. ISFS provides small, lightweight, readily hardened core components of general utility. These components can be adapted to unanticipated uses, are tailorable, and require at most a loosely coupled, nonproprietary connection to the Web. Users can invoke capabilities from a command line; programmers can integrate ISFS's core components into more complex systems and services. Taken together, these
Total probabilities of ensemble runoff forecasts
NASA Astrophysics Data System (ADS)
Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian
Ensemble forecasting has for a long time been used as a method in meteorological modelling to indicate the uncertainty of the forecasts. However, as the ensembles often exhibit both bias and dispersion errors, it is necessary to calibrate and post-process them. Two of the most common methods for this are Bayesian Model Averaging (Raftery et al., 2005) and Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). There are also methods for regionalizing these methods (Berrocal et al., 2007) and for incorporating the correlation between lead times (Hemri et al., 2013). Engeland and Steinsland Engeland and Steinsland (2014) developed a framework which can estimate post-processing parameters which are different in space and time, but still can give a spatially and temporally consistent output. However, their method is computationally complex for our larger number of stations, and cannot directly be regionalized in the way we would like, so we suggest a different path below. The target of our work is to create a mean forecast with uncertainty bounds for a large number of locations in the framework of the European Flood Awareness System (EFAS - http://www.efas.eu) We are therefore more interested in improving the forecast skill for high-flows rather than the forecast skill of lower runoff levels. EFAS uses a combination of ensemble forecasts and deterministic forecasts from different forecasters to force a distributed hydrologic model and to compute runoff ensembles for each river pixel within the model domain. Instead of showing the mean and the variability of each forecast ensemble individually, we will now post-process all model outputs to find a total probability, the post-processed mean and uncertainty of all ensembles. The post-processing parameters are first calibrated for each calibration location, but assuring that they have some spatial correlation, by adding a spatial penalty in the calibration process. This can in some cases have a slight negative
Ramp Forecasting Performance from Improved Short-Term Wind Power Forecasting: Preprint
Zhang, J.; Florita, A.; Hodge, B. M.; Freedman, J.
The variable and uncertain nature of wind generation presents a new concern to power system operators. One of the biggest concerns associated with integrating a large amount of wind power into the grid is the ability to handle large ramps in wind power output. Large ramps can significantly influence system economics and reliability, on which power system operators place primary emphasis. The Wind Forecasting Improvement Project (WFIP) was performed to improve wind power forecasts and determine the value of these improvements to grid operators. This paper evaluates the performance of improved short-term wind power ramp forecasting. The study is performed for the Electric Reliability Council of Texas (ERCOT) by comparing the experimental WFIP forecast to the current short-term wind power forecast (STWPF). Four types of significant wind power ramps are employed in the study; these are based on the power change magnitude, direction, and duration. The swinging door algorithm is adopted to extract ramp events from actual and forecasted wind power time series. The results show that the experimental short-term wind power forecasts improve the accuracy of the wind power ramp forecasting, especially during the summer.
Uncertainty Forecasts Improve Weather-Related Decisions and Attenuate the Effects of Forecast Error
ERIC Educational Resources Information Center
Joslyn, Susan L.; LeClerc, Jared E.
Although uncertainty is inherent in weather forecasts, explicit numeric uncertainty estimates are rarely included in public forecasts for fear that they will be misunderstood. Of particular concern are situations in which precautionary action is required at low probabilities, often the case with severe events. At present, a categorical weather…
More Intense Experiences, Less Intense Forecasts: Why People Overweight Probability Specifications in Affective Forecasts
Buechel, Eva C.; Zhang, Jiao; Morewedge, Carey K.; Vosgerau, Joachim
We propose that affective forecasters overestimate the extent to which experienced hedonic responses to an outcome are influenced by the probability of its occurrence. The experience of an outcome (e.g., winning a gamble) is typically more affectively intense than the simulation of that outcome (e.g., imagining winning a gamble) upon which the affective forecast for it is based. We suggest that, as a result, experiencers allocate a larger share of their attention toward the outcome (e.g., winning the gamble) and less to its probability specifications than do affective forecasters. Consequently, hedonic responses to an outcome are less sensitive to its probability specifications than are affective forecasts for that outcome. The results of 6 experiments provide support for our theory. Affective forecasters overestimated how sensitive experiencers would be to the probability of positive and negative outcomes (Experiments 1 and 2). Consistent with our attentional account, differences in sensitivity to probability specifications disappeared when the attention of forecasters was diverted from probability specifications (Experiment 3) or when the attention of experiencers was drawn toward probability specifications (Experiment 4). Finally, differences in sensitivity to probability specifications between forecasters and experiencers were diminished when the forecasted outcome was more affectively intense (Experiments 5 and 6). PMID:24128184
Potential for malaria seasonal forecasting in Africa
NASA Astrophysics Data System (ADS)
Tompkins, Adrian; Di Giuseppe, Francesca; Colon-Gonzalez, Felipe; Namanya, Didas; Friday, Agabe
As monthly and seasonal dynamical prediction systems have improved their skill in the tropics over recent years, there is now the potential to use these forecasts to drive dynamical malaria modelling systems to provide early warnings in epidemic and meso-endemic regions. We outline a new pilot operational system that has been developed at ECMWF and ICTP. It uses a precipitation bias correction methodology to seamlessly join the monthly ensemble prediction system (EPS) and seasonal (system 4) forecast systems of ECMWF together. The resulting temperature and rainfall forecasts for Africa are then used to drive the recently developed ICTP malaria model known as VECTRI. The resulting coupled system of ECMWF climate forecasts and VECTRI thus produces predictions of malaria prevalence rates and transmission intensity across Africa. The forecasts are filtered to highlight the regions and months in which the system has particular value due to high year to year variability. In addition to epidemic areas, these also include meso and hyper-endemic regions which undergo considerable variability in the onset months. We demonstrate the limits of the forecast skill as a function of lead-time, showing that for many areas the dynamical system can add one to two months additional warning time to a system based on environmental monitoring. We then evaluate the past forecasts against district level case data in Uganda and show that when interventions can be discounted, the system can show significant skill at predicting interannual variability in transmission intensity up to 3 or 4 months ahead at the district scale. The prospects for a operational implementation will be briefly discussed.
Forecasting volcanic eruptions and other material failure phenomena: An evaluation of the failure forecast method
NASA Astrophysics Data System (ADS)
Bell, Andrew F.; Naylor, Mark; Heap, Michael J.; Main, Ian G.
Power-law accelerations in the mean rate of strain, earthquakes and other precursors have been widely reported prior to material failure phenomena, including volcanic eruptions, landslides and laboratory deformation experiments, as predicted by several theoretical models. The Failure Forecast Method (FFM), which linearizes the power-law trend, has been routinely used to forecast the failure time in retrospective analyses; however, its performance has never been formally evaluated. Here we use synthetic and real data, recorded in laboratory brittle creep experiments and at volcanoes, to show that the assumptions of the FFM are inconsistent with the error structure of the data, leading to biased and imprecise forecasts. We show that a Generalized Linear Model method provides higher-quality forecasts that converge more accurately to the eventual failure time, accounting for the appropriate error distributions. This approach should be employed in place of the FFM to provide reliable quantitative forecasts and estimate their associated uncertainties.
An alternate approach to ensemble ENSO forecast spread: Application to the 2014 forecast
NASA Astrophysics Data System (ADS)
Larson, Sarah M.; Kirtman, Ben P.
Evaluating the 2014 El Niño forecast as a "bust" may be tapping into a bigger issue, namely that forecast "overconfidence" from single-model ensembles could affect the retrospective assessment of El Niño-Southern Oscillation (ENSO) predictions. The present study proposes a new approach to quantifying an "expected" spread and uncertainty from noise-driven processes and supplementing these measures with actual ENSO forecasts. Expanding on a previously developed coupled model framework that isolates noise-driven ENSO-like errors, an experimental design is implemented to generate an expected December Niño-3.4 spread from March initial condition sea surface temperature errors that have similar structure to the 2014 and 2015 observed. Results reveal that the 2014 ENSO forecast falls within the expected uncertainty generated by ENSO-independent, forecast-independent, noise-driven errors.
Optimizing Tsunami Forecast Model Accuracy
NASA Astrophysics Data System (ADS)
Whitmore, P.; Nyland, D. L.; Huang, P. Y.
Recent tsunamis provide a means to determine the accuracy that can be expected of real-time tsunami forecast models. Forecast accuracy using two different tsunami forecast models are compared for seven events since 2006 based on both real-time application and optimized, after-the-fact "forecasts". Lessons learned by comparing the forecast accuracy determined during an event to modified applications of the models after-the-fact provide improved methods for real-time forecasting for future events. Variables such as source definition, data assimilation, and model scaling factors are examined to optimize forecast accuracy. Forecast accuracy is also compared for direct forward modeling based on earthquake source parameters versus accuracy obtained by assimilating sea level data into the forecast model. Results show that including assimilated sea level data into the models increases accuracy by approximately 15% for the events examined.
Skill of global hydrological forecasting system FEWS GLOWASIS using climatic ESP forecasts
NASA Astrophysics Data System (ADS)
Weerts, A. H.; Candogan, N.; Winsemius, H. C.; van Beek, R.; Westerhoff, R.
Forecasting of water availability and scarcity is a prerequisite for the management of hydropower reservoirs, basin-scale management of water resources, agriculture and disaster relief. The EU 7th Framework Program project Global Water Scarcity Information Service (GLOWASIS) aims to pre-validate a service that provides real-time global-scale information on water scarcity. In this contribution, we demonstrate what skill (compared to a climatology) may be reached with a global seasonal ensemble forecasting system consisting of: a) a global hydrological model PCR-GLOBWB; b) an updating procedure for initial forecasting states, based on the best-guess global rainfall information. As best guess, a combination of ERA-Interim Reanalysis rainfall and Global Precipitation Climatology Project (GPCP) observations is being used; c) a forecast based on Ensemble Streamflow Prediction (ESP)procedure and reverse ESP procedure (Wood and Lettenmaier, 2008). In the ESP procedure, a meteorological forecast ensemble is generated based on past years of observation series (i.e. climatological observations). As observations, the combination of ERA-Interim and GPCP is used. In reverse ESP, an ensemble is generated by using a set of initial states from a large sample of updates at the specific month of interest, and forecasts are produced using one observed set. This analysis allows us to measure how much skill may be expected from hydrological inertia and climatology alone, leaving aside for the moment potential skill improvement by using seasonal meteorological forecasts. In future work, we will measure how much skill improvement compared to the forecasts mentioned above may be reached, when ECMWF Seasonal forecasts are used. This will allow us to measure the contributions to skill of each component (initial state inertia, hydrology and meteorological inputs) of the forecast system.
Seasonal Water Balance Forecasts for Drought Early Warning in Ethiopia
NASA Astrophysics Data System (ADS)
Spirig, Christoph; Bhend, Jonas; Liniger, Mark
Droughts severely impact Ethiopian agricultural production. Successful early warning for drought conditions in the upcoming harvest season therefore contributes to better managing food shortages arising from adverse climatic conditions. So far, however, meteorological seasonal forecasts have not been used in Ethiopia's national food security early warning system (i.e. the LEAP platform). Here we analyse the forecast quality of seasonal forecasts of total rainfall and of the meteorological water balance as a proxy for plant available water. We analyse forecast skill of June to September rainfall and water balance from dynamical seasonal forecast systems, the ECMWF System4 and EC-EARTH global forecasting systems. Rainfall forecasts outperform forecasts assuming a stationary climate mainly in north-eastern Ethiopia - an area that is particularly vulnerable to droughts. Forecasts of the water balance index seem to be even more skilful and thus more useful than pure rainfall forecasts. The results vary though for different lead times and skill measures employed. We further explore the potential added value of dynamically downscaling the forecasts through several dynamical regional climate models made available through the EU FP7 project EUPORIAS. Preliminary results suggest that dynamically downscaled seasonal forecasts are not significantly better compared with seasonal forecasts from the global models. We conclude that seasonal forecasts of a simple climate index such as the water balance have the potential to benefit drought early warning in Ethiopia, both due to its positive predictive skill and higher usefulness than seasonal mean quantities.
A combined forecasting approach based on fuzzy soft sets
NASA Astrophysics Data System (ADS)
Xiao, Zhi; Gong, Ke; Zou, Yan
Forecasting the export and import volume in international trade is the prerequisite of a government's policy-making and guidance for a healthier international trade development. However, an individual forecast may not always perform satisfactorily, while combination of forecasts may result in a better forecast than component forecasts. We believe the component forecasts employed in combined forecasts are a description of the actual time series, which is fuzzy. This paper attempts to use forecasting accuracy as the criterion of fuzzy membership function, and proposes a combined forecasting approach based on fuzzy soft sets. This paper also examines the method with data of international trade from 1993 to 2006 in the Chongqing Municipality of China and compares it with a combined forecasting approach based on rough sets and each individual forecast. The experimental results show that the combined approach provided in this paper improves the forecasting performance of each individual forecast and is free from a rough sets approach's restrictions as well. It is a promising forecasting approach and a new application of soft sets theory.
Evaluation of ensemble forecast uncertainty using a new proper score: application to medium-range and seasonal forecasts
NASA Astrophysics Data System (ADS)
Christensen, Hannah; Moroz, Irene; Palmer, Tim
Forecast verification is important across scientific disciplines as it provides a framework for evaluating the performance of a forecasting system. In the atmospheric sciences, probabilistic skill scores are often used for verification as they provide a way of unambiguously ranking the performance of different probabilistic forecasts. In order to be useful, a skill score must be proper -- it must encourage honesty in the forecaster, and reward forecasts which are reliable and which have good resolution. A new score, the Error-spread Score (ES), is proposed which is particularly suitable for evaluation of ensemble forecasts. It is formulated with respect to the moments of the forecast. The ES is confirmed to be a proper score, and is therefore sensitive to both resolution and reliability. The ES is tested on forecasts made using the Lorenz '96 system, and found to be useful for summarising the skill of the forecasts. The European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EPS) is evaluated using the ES. Its performance is compared to a perfect statistical probabilistic forecast -- the ECMWF high resolution deterministic forecast dressed with the observed error distribution. This generates a forecast that is perfectly reliable if considered over all time, but which does not vary from day to day with the predictability of the atmospheric flow. The ES distinguishes between the dynamically reliable EPS forecasts and the statically reliable dressed deterministic forecasts. Other skill scores are tested and found to be comparatively insensitive to this desirable forecast quality. The ES is used to evaluate seasonal range ensemble forecasts made with the ECMWF System 4. The ensemble forecasts are found to be skilful when compared with climatological or persistence forecasts, though this skill is dependent on region and time of year.
Ensemble Exigent Forecasting of Critical Weather Events
NASA Astrophysics Data System (ADS)
Hoffman, R. N.; Gombos, D.
To improve the forecasting of and society's preparedness for "worst-case" weather damage scenarios, we have developed ensemble exigent analysis. Exigent analysis determines worst cast scenarios and associated probability quantiles from the joint spatial properties of multivariate damaging weather events. Using the ensemble-estimated forecast covariance, we (1) identify the forecast exigent analysis perturbation (ExAP) and (2) find the contemporaneous and antecedent meteorological conditions that are most likely to coexist with or to evolve into the ExAP at the forecast time. Here we focus on the first objective, the ExAP identification problem. The ExAP is the perturbation wrt to the ensemble mean at the forecast time that maximizes the damage in the subspace of the ensemble with respect to a user-defined damage metric (i.e. maximizes the sum of the damage perturbation over the domain of interest) and to a user-specified ensemble probability quantile (EPQ) defined in terms of the Mahalanobis distance of the perturbation to the ensemble mean. Making use of a universal relationship (for Gaussian ensembles) between the quantile of the damage functional and the EPQ, we explain the ExAP using topological arguments. Then, we formally define the ExAP by making use of the ensemble-estimated covariance of the damage ensemble in a Lagrangian minimization technique according to an exigent analysis theorem. Two case studies with varying complexities and expected accuracies are used to illustrate ensemble exigent analysis. The first case study employs the gridded forecast number of heating degree days (HDD) to analyze forecast heating demand over a large portion of the United Sates for a cold event on 9 January 2010. The second case uses ensemble forecasts of 2-meter temperature and estimates of the spatial distribution of citrus trees to define the damage functional as the percentage of Florida citrus trees damaged by the 11 January 2010 Florida freeze event. The ExAP of this
Timetable of an operational flood forecasting system
NASA Astrophysics Data System (ADS)
Liechti, Katharina; Jaun, Simon; Zappa, Massimiliano
At present a new underground part of Zurich main station is under construction. For this purpose the runoff capacity of river Sihl, which is passing beneath the main station, is reduced by 40%. If a flood is to occur the construction site is evacuated and gates can be opened for full runoff capacity to prevent bigger damages. However, flooding the construction site, even if it is controlled, is coupled with costs and retardation. The evacuation of the construction site at Zurich main station takes about 2 to 4 hours and opening the gates takes another 1 to 2 hours each. In the upper part of the 336 km2 Sihl catchment the Sihl lake, a reservoir lake, is situated. It belongs and is used by the Swiss Railway Company for hydropower production. This lake can act as a retention basin for about 46% of the Sihl catchment. Lowering the lake level to gain retention capacity, and therewith safety, is coupled with direct loss for the Railway Company. To calculate the needed retention volume and the water to be released facing unfavourable weather conditions, forecasts with a minimum lead time of 2 to 3 days are needed. Since the catchment is rather small, this can only be realised by the use of meteorological forecast data. Thus the management of the construction site depends on accurate forecasts to base their decisions on. Therefore an operational hydrological ensemble prediction system (HEPS) was introduced in September 2008 by the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). It delivers daily discharge forecasts with a time horizon of 5 days. The meteorological forecasts are provided by MeteoSwiss and stem from the operational limited-area COSMO-LEPS which downscales the ECMWF ensemble prediction system to a spatial resolution of 7 km. Additional meteorological data for model calibration and initialisation (air temperature, precipitation, water vapour pressure, global radiation, wind speed and sunshine duration) and radar data are also provided by
Operational seasonal forecasting of crop performance.
Stone, Roger C; Meinke, Holger
Integrated, interdisciplinary crop performance forecasting systems, linked with appropriate decision and discussion support tools, could substantially improve operational decision making in agricultural management. Recent developments in connecting numerical weather prediction models and general circulation models with quantitative crop growth models offer the potential for development of integrated systems that incorporate components of long-term climate change. However, operational seasonal forecasting systems have little or no value unless they are able to change key management decisions. Changed decision making through incorporation of seasonal forecasting ultimately has to demonstrate improved long-term performance of the cropping enterprise. Simulation analyses conducted on specific production scenarios are especially useful in improving decisions, particularly if this is done in conjunction with development of decision-support systems and associated facilitated discussion groups. Improved management of the overall crop production system requires an interdisciplinary approach, where climate scientists, agricultural scientists and extension specialists are intimately linked with crop production managers in the development of targeted seasonal forecast systems. The same principle applies in developing improved operational management systems for commodity trading organizations, milling companies and agricultural marketing organizations. Application of seasonal forecast systems across the whole value chain in agricultural production offers considerable benefits in improving overall operational management of agricultural production. PMID:16433097
Heterogeneity: The key to failure forecasting
NASA Astrophysics Data System (ADS)
Vasseur, Jérémie; Wadsworth, Fabian B.; Lavallée, Yan; Bell, Andrew F.; Main, Ian G.; Dingwell, Donald B.
Elastic waves are generated when brittle materials are subjected to increasing strain. Their number and energy increase non-linearly, ending in a system-sized catastrophic failure event. Accelerating rates of geophysical signals (e.g., seismicity and deformation) preceding large-scale dynamic failure can serve as proxies for damage accumulation in the Failure Forecast Method (FFM). Here we test the hypothesis that the style and mechanisms of deformation, and the accuracy of the FFM, are both tightly controlled by the degree of microstructural heterogeneity of the material under stress. We generate a suite of synthetic samples with variable heterogeneity, controlled by the gas volume fraction. We experimentally demonstrate that the accuracy of failure prediction increases drastically with the degree of material heterogeneity. These results have significant implications in a broad range of material-based disciplines for which failure forecasting is of central importance. In particular, the FFM has been used with only variable success to forecast failure scenarios both in the field (volcanic eruptions and landslides) and in the laboratory (rock and magma failure). Our results show that this variability may be explained, and the reliability and accuracy of forecast quantified significantly improved, by accounting for material heterogeneity as a first-order control on forecasting power.
Layered Ensemble Architecture for Time Series Forecasting.
Rahman, Md Mustafizur; Islam, Md Monirul; Murase, Kazuyuki; Yao, Xin
Time series forecasting (TSF) has been widely used in many application areas such as science, engineering, and finance. The phenomena generating time series are usually unknown and information available for forecasting is only limited to the past values of the series. It is, therefore, necessary to use an appropriate number of past values, termed lag, for forecasting. This paper proposes a layered ensemble architecture (LEA) for TSF problems. Our LEA consists of two layers, each of which uses an ensemble of multilayer perceptron (MLP) networks. While the first ensemble layer tries to find an appropriate lag, the second ensemble layer employs the obtained lag for forecasting. Unlike most previous work on TSF, the proposed architecture considers both accuracy and diversity of the individual networks in constructing an ensemble. LEA trains different networks in the ensemble by using different training sets with an aim of maintaining diversity among the networks. However, it uses the appropriate lag and combines the best trained networks to construct the ensemble. This indicates LEAs emphasis on accuracy of the networks. The proposed architecture has been tested extensively on time series data of neural network (NN)3 and NN5 competitions. It has also been tested on several standard benchmark time series data. In terms of forecasting accuracy, our experimental results have revealed clearly that LEA is better than other ensemble and nonensemble methods. PMID:25751882
Worst case forecasting of Hurricane Irene (2011)
NASA Astrophysics Data System (ADS)
Hoffman, R. N.; Gombos, D.; Woods, B. K.
Worst case scenarios for wind damage from Hurricane Irene are estimated from an ensemble of surface wind speed forecasts. Damage at any point is modeled by applying a simple damage function to census data estimates of property values. The forecast damage ensemble provides an estimate of the covariance structure of the damage. Under the assumption that the damage is multivariate Gaussian (mG), the damage covariance defines a high dimensional ellipsoidal surface for any probability quantile. The damage maximizing point on that ellipsoid, i.e., the ``exigent'' scenario, is found by the method of Lagrangian multipliers according to the Exigent Analysis Theorem (EAT). We will present the evolution of the exigent scenario as calculated at different forecast initial times and compare these forecast worst case estimates to the actual damage. We will also explore methods to quantify deviations from the mG assumption and their impact on our analysis. The EAT also provides the least damaging (or best case) scenario and this enables us to present the relative uncertainty of the damage forecasts and how this uncertainty evolves in terms of worst case minus best case damage maps.
Drought Forecasting System of the Netherlands
NASA Astrophysics Data System (ADS)
Weerts, A. H.; Berendrecht, W. L.; Veldhuizen, A.; Goorden, N.; Vernimmen, R.; Lourens, A.; Prinsen, G.; Mulder, M.; Kroon, T.; Stam, J.
During periods of droughts the National Coordinating Committee for Water Distribution of the Netherlands has to decide how the available surface water is used and allocated between different users (agriculture, navigation, industry etc). To support this decision making, real-time information is needed about the availability of surface water, groundwater levels, saturation of the root zone, etc. This real-time information must give insight into the current state of the system as well as into its state in the near future (i.e. 10 days ahead). For this purpose, the National Hydrological Instrument (NHI), running on a daily time step and consisting of a nationwide distribution model and surface water model coupled with a MODFLOW-METASWAP model of the saturated-unsaturated zone of the whole of the Netherlands, driven by measured and forecasted precipitation and evaporation (ECMWF-DET and -EPS), is used to obtain insight into the actual and forecasted states of the surface, ground and soil water in the Netherlands. The tool also gives insight in the actual and forecasted water demands by the different actors. The whole system is operationalised within Delft-FEWS, an operational forecasting system to manage data and models in a real time environment. The surface water and groundwater models can be compared with surface water measurements (discharges and water levels) and groundwater level measurements in real-time. ECMWF reforecasts will be used to gain insight in the performance of the drought forecasting system.
Multivariate streamflow forecasting using independent component analysis
NASA Astrophysics Data System (ADS)
Westra, Seth; Sharma, Ashish; Brown, Casey; Lall, Upmanu
Seasonal forecasting of streamflow provides many benefits to society, by improving our ability to plan and adapt to changing water supplies. A common approach to developing these forecasts is to use statistical methods that link a set of predictors representing climate state as it relates to historical streamflow, and then using this model to project streamflow one or more seasons in advance based on current or a projected climate state. We present an approach for forecasting multivariate time series using independent component analysis (ICA) to transform the multivariate data to a set of univariate time series that are mutually independent, thereby allowing for the much broader class of univariate models to provide seasonal forecasts for each transformed series. Uncertainty is incorporated by bootstrapping the error component of each univariate model so that the probability distribution of the errors is maintained. Although all analyses are performed on univariate time series, the spatial dependence of the streamflow is captured by applying the inverse ICA transform to the predicted univariate series. We demonstrate the technique on a multivariate streamflow data set in Colombia, South America, by comparing the results to a range of other commonly used forecasting methods. The results show that the ICA-based technique is significantly better at representing spatial dependence, while not resulting in any loss of ability in capturing temporal dependence. As such, the ICA-based technique would be expected to yield considerable advantages when used in a probabilistic setting to manage large reservoir systems with multiple inflows or data collection points.
Heterogeneity: The key to failure forecasting.
Vasseur, Jérémie; Wadsworth, Fabian B; Lavallée, Yan; Bell, Andrew F; Main, Ian G; Dingwell, Donald B
Elastic waves are generated when brittle materials are subjected to increasing strain. Their number and energy increase non-linearly, ending in a system-sized catastrophic failure event. Accelerating rates of geophysical signals (e.g., seismicity and deformation) preceding large-scale dynamic failure can serve as proxies for damage accumulation in the Failure Forecast Method (FFM). Here we test the hypothesis that the style and mechanisms of deformation, and the accuracy of the FFM, are both tightly controlled by the degree of microstructural heterogeneity of the material under stress. We generate a suite of synthetic samples with variable heterogeneity, controlled by the gas volume fraction. We experimentally demonstrate that the accuracy of failure prediction increases drastically with the degree of material heterogeneity. These results have significant implications in a broad range of material-based disciplines for which failure forecasting is of central importance. In particular, the FFM has been used with only variable success to forecast failure scenarios both in the field (volcanic eruptions and landslides) and in the laboratory (rock and magma failure). Our results show that this variability may be explained, and the reliability and accuracy of forecast quantified significantly improved, by accounting for material heterogeneity as a first-order control on forecasting power. PMID:26307196
Heterogeneity: The key to failure forecasting
Vasseur, Jérémie; Wadsworth, Fabian B.; Lavallée, Yan; Bell, Andrew F.; Main, Ian G.; Dingwell, Donald B.
Elastic waves are generated when brittle materials are subjected to increasing strain. Their number and energy increase non-linearly, ending in a system-sized catastrophic failure event. Accelerating rates of geophysical signals (e.g., seismicity and deformation) preceding large-scale dynamic failure can serve as proxies for damage accumulation in the Failure Forecast Method (FFM). Here we test the hypothesis that the style and mechanisms of deformation, and the accuracy of the FFM, are both tightly controlled by the degree of microstructural heterogeneity of the material under stress. We generate a suite of synthetic samples with variable heterogeneity, controlled by the gas volume fraction. We experimentally demonstrate that the accuracy of failure prediction increases drastically with the degree of material heterogeneity. These results have significant implications in a broad range of material-based disciplines for which failure forecasting is of central importance. In particular, the FFM has been used with only variable success to forecast failure scenarios both in the field (volcanic eruptions and landslides) and in the laboratory (rock and magma failure). Our results show that this variability may be explained, and the reliability and accuracy of forecast quantified significantly improved, by accounting for material heterogeneity as a first-order control on forecasting power. PMID:26307196
Does money matter in inflation forecasting?
NASA Astrophysics Data System (ADS)
Binner, J. M.; Tino, P.; Tepper, J.; Anderson, R.; Jones, B.; Kendall, G.
This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two nonlinear techniques, namely, recurrent neural networks and kernel recursive least squares regression-techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naïve random walk model. The best models were nonlinear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation. Beyond its economic findings, our study is in the tradition of physicists' long-standing interest in the interconnections among statistical mechanics, neural networks, and related nonparametric statistical methods, and suggests potential avenues of extension for such studies.
Coastal ocean forecasting systems in Europe
NASA Astrophysics Data System (ADS)
Dugan, John
During my tour as the liaison oceanographer at the Office of Naval Research's European branch, I conducted a focused study of coastal ocean forecasting systems. This study is of direct interest to ONR because of an increased interest in the coastal zone and to the civilian U.S. oceanographic community because of numerous problems in the coastal zone that could be alleviated with an operational forecasting system. The Europeans have a long history of excellent research and developmental work in this area. The Europeans' distinguished history in coastal ocean forecasting is due in part to their strong dependence on the sea. However, the original motivation for these systems was the recognition early in this century that weather conditions were responsible for damaging storm surges around the periphery of the North Sea and that science could predict these catastrophic floods. Forecasting systems called tide-surge prediction systems, which provide warnings of impending flood conditions, were designed and constructed and are operational in the various meteorological centers of the nations surrounding the North Sea. Over time, the services have been extended to provide forecasts of ocean waves, water depth for navigation, and currents for a large customer base. These systems now are being extended further into the three-dimensional domain that is required for management of problems associated with water quality, pollution, and aquaculture and fisheries interests.
In Brief: Atlantic seasonal hurricane forecast
NASA Astrophysics Data System (ADS)
Showstack, Randy
Two hurricane forecasters are predicting that 2008 will be an above-average Atlantic basin tropical cyclone season with an above-average probability of a major hurricane making landfall in the United States. During 2008, there could be about seven hurricanes (the annual average is 5.9) and 13 named storms (the average is 9.6), according to a 7 December report by Philip Klotzbach, research scientist at Colorado State University in Fort Collins, and William Gray, university professor emeritus of atmospheric sciences. The forecasters indicate that they believe the Atlantic basin is in an active hurricane cycle that is associated with a strong thermohaline circulation and an active phase of the Atlantic Multidecadal Oscillation. The report notes that, ``real-time operational early December forecasts have not shown forecast skill over climatology during this 16-year period [1992-2007]. This has occurred despite the fact that the skill over the hindcast period...showed appreciable skill.'' For more information, visit the Web site: http://hurricane.atmos.colostate.edu/Forecasts/2007/dec2007/dec2007.pdf.
Monitoring and forecasting Etna volcanic plumes
NASA Astrophysics Data System (ADS)
Scollo, S.; Prestifilippo, M.; Spata, G.; D'Agostino, M.; Coltelli, M.
In this paper we describe the results of a project ongoing at the Istituto Nazionale di Geofisica e Vulcanologia (INGV). The objective is to develop and implement a system for monitoring and forecasting volcanic plumes of Etna. Monitoring is based at present by multispectral infrared measurements from the Spin Enhanced Visible and Infrared Imager on board the Meteosat Second Generation geosynchronous satellite, visual and thermal cameras, and three radar disdrometers able to detect ash dispersal and fallout. Forecasting is performed by using automatic procedures for: i) downloading weather forecast data from meteorological mesoscale models; ii) running models of tephra dispersal, iii) plotting hazard maps of volcanic ash dispersal and deposition for certain scenarios and, iv) publishing the results on a web-site dedicated to the Italian Civil Protection. Simulations are based on eruptive scenarios obtained by analysing field data collected after the end of recent Etna eruptions. Forecasting is, hence, supported by plume observations carried out by the monitoring system. The system was tested on some explosive events occurred during 2006 and 2007 successfully. The potentiality use of monitoring and forecasting Etna volcanic plumes, in a way to prevent threats to aviation from volcanic ash, is finally discussed.
Forecasting the Emergency Department Patients Flow.
Afilal, Mohamed; Yalaoui, Farouk; Dugardin, Frédéric; Amodeo, Lionel; Laplanche, David; Blua, Philippe
Emergency department (ED) have become the patient's main point of entrance in modern hospitals causing it frequent overcrowding, thus hospital managers are increasingly paying attention to the ED in order to provide better quality service for patients. One of the key elements for a good management strategy is demand forecasting. In this case, forecasting patients flow, which will help decision makers to optimize human (doctors, nurses…) and material(beds, boxs…) resources allocation. The main interest of this research is forecasting daily attendance at an emergency department. The study was conducted on the Emergency Department of Troyes city hospital center, France, in which we propose a new practical ED patients classification that consolidate the CCMU and GEMSA categories into one category and innovative time-series based models to forecast long and short term daily attendance. The models we developed for this case study shows very good performances (up to 91,24 % for the annual Total flow forecast) and robustness to epidemic periods. PMID:27272135
Forecasts of solar and geomagnetic activity
NASA Technical Reports Server (NTRS)
Joselyn, Joann
Forecasts of solar and geomagnetic activity are critical since these quantities are such important inputs to the thermospheric density models. At this time in the history of solar science there is no way to make such a forecast from first principles. Physical theory applied to the Sun is developing rapidly, but is still primitive. Techniques used for forecasting depend upon the observations over about 130 years, which is only twelve solar cycles. It has been noted that even-numbered cycles systematically tend to be smaller than the odd-numbered ones by about 20 percent. Another observation is that for the last 12 cycle pairs, an even-numbered sunspot cycle looks rather like the next odd-numbered cycle, but with the top cut off. These observations are examples of approximate periodicities that forecasters try to use to achieve some insight into the nature of an upcoming cycle. Another new and useful forecasting aid is a correlation that has been noted between geomagnetic indices and the size of the next solar cycle. Some best estimates are given concerning both activities.
Forecast Mekong 2012: Building scientific capacity
Stefanov, James E.
In 2009, U.S. Secretary of State Hillary R. Clinton joined the Foreign Ministers of Cambodia, Laos, Thailand, and Vietnam in launching the Lower Mekong Initiative to enhance U.S. engagement with the countries of the Lower Mekong River Basin in the areas of environment, health, education, and infrastructure. The U.S. Geological Survey Forecast Mekong supports the Lower Mekong Initiative through a variety of activities. The principal objectives of Forecast Mekong include the following: * Build scientific capacity in the Lower Mekong Basin and promote cooperation and collaboration among scientists working in the region. * Provide data, information, and scientific models to help resource managers there make informed decisions. * Produce forecasting and visualization tools to support basin planning, including climate change adaptation. The focus of this product is Forecast Mekong accomplishments and current activities related to the development of scientific capacity at organizations and institutions in the region. Building on accomplishments in 2010 and 2011, Forecast Mekong continues to enhance scientific capacity in the Lower Mekong Basin with a suite of activities in 2012.
Space weather forecasting: Past, Present, Future
NASA Astrophysics Data System (ADS)
Lanzerotti, L. J.
There have been revolutionary advances in electrical technologies over the last 160 years. The historical record demonstrates that space weather processes have often provided surprises in the implementation and operation of many of these technologies. The historical record also demonstrates that as the complexity of systems increase, including their interconnectedness and interoperability, they can become more susceptible to space weather effects. An engineering goal, beginning during the decades following the 1859 Carrington event, has been to attempt to forecast solar-produced disturbances that could affect technical systems, be they long grounded conductor-based or radio-based or required for exploration, or the increasingly complex systems immersed in the space environment itself. Forecasting of space weather events involves both frontier measurements and models to address engineering requirements, and industrial and governmental policies that encourage and permit creativity and entrepreneurship. While analogies of space weather forecasting to terrestrial weather forecasting are frequently made, and while many of the analogies are valid, there are also important differences. This presentation will provide some historical perspectives on the forecast problem, a personal assessment of current status of several areas including important policy issues, and a look into the not-too-distant future.
Towards custom made seasonal/decadal forecasting
NASA Astrophysics Data System (ADS)
Mahlstein, Irina; Spirig, Christoph; Liniger, Mark
Climate indices offer the possibility to deliver information to the end user that can be easily applied to their field of work. For instance, a 3-monthly mean average temperature does not say much about the Heating Degree Days of a season, or how many frost days there are to be expected. Hence, delivering aggregated climate information can be more useful to the consumer than just raw data. In order to ensure that the end-users actually get what they need, the providers need to know what exactly they need to deliver. Hence, the specific user-needs have to be identified. In the framework of EUPORIAS, interviews with the end-user were conducted in order to learn more about the types of information that are needed. But also to investigate what knowledge exists among the users about seasonal/decadal forecasting and in what way uncertainties are taken into account. It is important that we gain better knowledge of how forecasts/predictions are applied by the end-user to their specific situation and business. EUPORIAS, which is embedded in the framework of EU FP7, aims exactly to improve that knowledge and deliver very specific forecasts that are custom made. Here we present examples of seasonal forecasts and their skill of several climate impact indices with direct relevance for specific economic sectors, such as energy. The results are compared to the visualization of conventional depiction of seasonal forecasts, such as 3 monthly average temperature tercile probabilities and the differences are highlighted.
Operational seasonal forecasting of crop performance
Stone, Roger C; Meinke, Holger
Integrated, interdisciplinary crop performance forecasting systems, linked with appropriate decision and discussion support tools, could substantially improve operational decision making in agricultural management. Recent developments in connecting numerical weather prediction models and general circulation models with quantitative crop growth models offer the potential for development of integrated systems that incorporate components of long-term climate change. However, operational seasonal forecasting systems have little or no value unless they are able to change key management decisions. Changed decision making through incorporation of seasonal forecasting ultimately has to demonstrate improved long-term performance of the cropping enterprise. Simulation analyses conducted on specific production scenarios are especially useful in improving decisions, particularly if this is done in conjunction with development of decision-support systems and associated facilitated discussion groups. Improved management of the overall crop production system requires an interdisciplinary approach, where climate scientists, agricultural scientists and extension specialists are intimately linked with crop production managers in the development of targeted seasonal forecast systems. The same principle applies in developing improved operational management systems for commodity trading organizations, milling companies and agricultural marketing organizations. Application of seasonal forecast systems across the whole value chain in agricultural production offers considerable benefits in improving overall operational management of agricultural production. PMID:16433097
Seasonal Climate Forecasts and Adoption by Agriculture
NASA Astrophysics Data System (ADS)
Garbrecht, Jurgen; Meinke, Holger; Sivakumar, Mannava V. K.; Motha, Raymond P.; Salinger, Michael J.
Recent advances in atmospheric and ocean sciences and a better understanding of the global climate have led to skillful climate forecasts at seasonal to interannual timescales, even in midlatitudes. These scientific advances and forecasting capabilities have opened the door to practical applications that benefit society. The benefits include the reduction of weather/climate related risks and vulnerability, increased economic opportunities, enhanced food security, mitigation of adverse climate impacts, protection of environmental quality, and so forth. Agriculture in particular can benefit substantially from accurate long-lead seasonal climate forecasts. Indeed, agricultural production very much depends on weather, climate, and water availability, and unexpected departures from anticipated climate conditions can thwart the best laid management plans. Timely climate forecasts offer means to reduce losses in drought years, increase profitability in good years, deal more effectively with climate variability, and choose from targeted risk-management strategies. In addition to benefiting farmers, forecasts can also help marketing systems and downstream users prepare for anticipated production outcomes and associated consequences.
Verification of Space Weather Forecasts using Terrestrial Weather Approaches
NASA Astrophysics Data System (ADS)
Henley, E.; Murray, S.; Pope, E.; Stephenson, D.; Sharpe, M.; Bingham, S.; Jackson, D.
The Met Office Space Weather Operations Centre (MOSWOC) provides a range of 24/7 operational space weather forecasts, alerts, and warnings, which provide valuable information on space weather that can degrade electricity grids, radio communications, and satellite electronics. Forecasts issued include arrival times of coronal mass ejections (CMEs), and probabilistic forecasts for flares, geomagnetic storm indices, and energetic particle fluxes and fluences. These forecasts are produced twice daily using a combination of output from models such as Enlil, near-real-time observations, and forecaster experience. Verification of forecasts is crucial for users, researchers, and forecasters to understand the strengths and limitations of forecasters, and to assess forecaster added value. To this end, the Met Office (in collaboration with Exeter University) has been adapting verification techniques from terrestrial weather, and has been working closely with the International Space Environment Service (ISES) to standardise verification procedures. We will present the results of part of this work, analysing forecast and observed CME arrival times, assessing skill using 2x2 contingency tables. These MOSWOC forecasts can be objectively compared to those produced by the NASA Community Coordinated Modelling Center - a useful benchmark. This approach cannot be taken for the other forecasts, as they are probabilistic and categorical (e.g., geomagnetic storm forecasts give probabilities of exceeding levels from minor to extreme). We will present appropriate verification techniques being developed to address these forecasts, such as rank probability skill score, and comparing forecasts against climatology and persistence benchmarks. As part of this, we will outline the use of discrete time Markov chains to assess and improve the performance of our geomagnetic storm forecasts. We will also discuss work to adapt a terrestrial verification visualisation system to space weather, to help
Tropical Cyclone Intensity Forecast Error Predictions and Their Applications
NASA Astrophysics Data System (ADS)
Bhatia, Kieran T.
This dissertation aims to improve tropical cyclone (TC) intensity forecasts by exploring the connection between intensity forecast error and parameters representing initial condition uncertainty, atmospheric flow stability, TC strength, and the large-scale environment surrounding a TC. After assessing which of these parameters have robust relationships with error, a set of predictors are selected to develop a priori estimates of intensity forecast accuracy for Atlantic basin TCs. The applications of these forecasts are then discussed, including a multimodel ensemble that unequally weights different intensity models according to the situation. The ultimate goal is to produce skillful forecasts of TC intensity error and use their output to enhance intensity forecasts.
Convective Weather Forecast Accuracy Analysis at Center and Sector Levels
NASA Technical Reports Server (NTRS)
Wang, Yao; Sridhar, Banavar
This paper presents a detailed convective forecast accuracy analysis at center and sector levels. The study is aimed to provide more meaningful forecast verification measures to aviation community, as well as to obtain useful information leading to the improvements in the weather translation capacity models. In general, the vast majority of forecast verification efforts over past decades have been on the calculation of traditional standard verification measure scores over forecast and observation data analyses onto grids. These verification measures based on the binary classification have been applied in quality assurance of weather forecast products at the national level for many years. Our research focuses on the forecast at the center and sector levels. We calculate the standard forecast verification measure scores for en-route air traffic centers and sectors first, followed by conducting the forecast validation analysis and related verification measures for weather intensities and locations at centers and sectors levels. An approach to improve the prediction of sector weather coverage by multiple sector forecasts is then developed. The weather severe intensity assessment was carried out by using the correlations between forecast and actual weather observation airspace coverage. The weather forecast accuracy on horizontal location was assessed by examining the forecast errors. The improvement in prediction of weather coverage was determined by the correlation between actual sector weather coverage and prediction. observed and forecasted Convective Weather Avoidance Model (CWAM) data collected from June to September in 2007. CWAM zero-minute forecast data with aircraft avoidance probability of 60% and 80% are used as the actual weather observation. All forecast measurements are based on 30-minute, 60- minute, 90-minute, and 120-minute forecasts with the same avoidance probabilities. The forecast accuracy analysis for times under one-hour showed that the errors in
The forecast model of relationship commitment.
Lemay, Edward P
Four studies tested the forecast model of relationship commitment, which posits that forecasts of future relationship satisfaction determine relationship commitment and prorelationship behavior in romantic relationships independently of other known predictors and partially explain the effects of these other predictors. This model was supported in 2 cross-sectional studies, a daily report study, and a study using behavioral observation, informant, and longitudinal methods. Across these studies, forecasts of future relationship satisfaction predicted relationship commitment and prorelationship behavior during relationship conflict and partially explained the effects of relationship satisfaction, quality of alternatives, and investment size. These results suggest that representations of the future have a prominent role in interpersonal processes. (PsycINFO Database Record PMID:27183320
Forecasting differences in life expectancy by education.
van Baal, Pieter; Peters, Frederik; Mackenbach, Johan; Nusselder, Wilma
Forecasts of life expectancy (LE) have fuelled debates about the sustainability and dependability of pension and healthcare systems. Of relevance to these debates are inequalities in LE by education. In this paper, we present a method of forecasting LE for different educational groups within a population. As a basic framework we use the Li-Lee model that was developed to forecast mortality coherently for different groups. We adapted this model to distinguish between overall, sex-specific, and education-specific trends in mortality, and extrapolated these time trends in a flexible manner. We illustrate our method for the population aged 65 and over in the Netherlands, using several data sources and spanning different periods. The results suggest that LE is likely to increase for all educational groups, but that differences in LE between educational groups will widen. Sensitivity analyses illustrate the advantages of our proposed method. PMID:27052447
Simulation forecasts complex flow streams from Ekofisk
Arnes, F.C.; Lillejord, H.
A commercial steady-state process flowsheet simulation program serves as the basis for a rigorous calculation model for predicting produced flow rates from the Ekofisk complex in the Norwegian sector of the North Sea. The complex is the center of an extensive gathering system that collects oil and gas streams from several producing fields. Prior to running a production forecast, the simulation model is initiated by matching several years of production. Once the simulation model matches historical production data within acceptable limits, it then is driven by production forecasts from reservoir simulations to develop long-term forecasts of gas, NGL, and oil production. The paper describes the Ekofisk field, the process simulation, implementation of the model, and problems encountered.
Real-time forecasts of dengue epidemics
NASA Astrophysics Data System (ADS)
Yamana, T. K.; Shaman, J. L.
Dengue is a mosquito-borne viral disease prevalent in the tropics and subtropics, with an estimated 2.5 billion people at risk of transmission. In many areas with endemic dengue, disease transmission is seasonal but prone to high inter-annual variability with occasional severe epidemics. Predicting and preparing for periods of higher than average transmission is a significant public health challenge. Here we present a model of dengue transmission and a framework for optimizing model simulations with real-time observational data of dengue cases and environmental variables in order to generate ensemble-based forecasts of the timing and severity of disease outbreaks. The model-inference system is validated using synthetic data and dengue outbreak records. Retrospective forecasts are generated for a number of locations and the accuracy of these forecasts is quantified.
Affective forecasting and the Big Five
Hoerger, Michael; Quirk, Stuart W.
Recent studies on affective forecasting clarify that the emotional reactions people anticipate often differ markedly from those they actually experience in response to affective stimuli and events. However, core personality differences in affective forecasting have received limited attention, despite their potential relevance to choice behavior. In the present study, 226 college undergraduates rated their anticipated and experienced reactions to the emotionally-evocative event of Valentine's Day and completed a measure of the Big Five personality traits – neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness – and their facet scales. Neuroticism and extraversion were associated with baseline mood, experienced emotional reactions, and anticipated emotional reactions. The present findings hold implications for the study of individual differences in affective forecasting, personality theory, and interventions research. PMID:22021944
Thin-Slice Forecasts of Gubernatorial Elections
Benjamin, Daniel J.; Shapiro, Jesse M.
We showed 10-second, silent video clips of unfamiliar gubernatorial debates to a group of experimental participants and asked them to predict the election outcomes. The participants' predictions explain more than 20 percent of the variation in the actual two-party vote share across the 58 elections in our study, and their importance survives a range of controls, including state fixed effects. In a horse race of alternative forecasting models, participants' forecasts significantly outperform economic variables in predicting vote shares, and are comparable in predictive power to a measure of incumbency status. Participants' forecasts seem to rest on judgments of candidates' personal attributes (such as likeability), rather than inferences about candidates' policy positions. Though conclusive causal inference is not possible in our context, our findings may be seen as suggestive evidence of a causal effect of candidate appeal on election outcomes. PMID:20431718
A operational real time flood forecasting chain
NASA Astrophysics Data System (ADS)
Arena, N.; Cavallo, A.; Giannoni, F.; Turato, B.
Extreme floods forecast represent an important modeling challenge for which it is crucial to utilize the simplest model representations that capture the dominant controls of extreme flood response. For extreme floods, the spatio-temporal structure of rainfall and drainage network structure often play a fundamental role. The integrated meteo-hydrologic real time forecasting chain in use at the Hydrometorological Center of Liguria Region is presented with particular regard to a specific case study. The meteorological forecasts are performed through the use of traditional means as Numerical Weather Predictions models at different resolutions and an innovative tool for the now-casting prediction as the meteorological Radar. The elements of the hydrologic model are a Hortonian infiltration model and a GIUH-based network response model. The basin scales of interest range from approximately 50 - 1,000 km2. The case study is the November 23-26, 2002 event.
Market turning points forecasting using wavelet analysis
NASA Astrophysics Data System (ADS)
Bai, Limiao; Yan, Sen; Zheng, Xiaolian; Chen, Ben M.
Based on the system adaptation framework we previously proposed, a frequency domain based model is developed in this paper to forecast the major turning points of stock markets. This system adaptation framework has its internal model and adaptive filter to capture the slow and fast dynamics of the market, respectively. The residue of the internal model is found to contain rich information about the market cycles. In order to extract and restore its informative frequency components, we use wavelet multi-resolution analysis with time-varying parameters to decompose this internal residue. An empirical index is then proposed based on the recovered signals to forecast the market turning points. This index is successfully applied to US, UK and China markets, where all major turning points are well forecasted.
Comparison of Forecast and Observed Energetics
NASA Technical Reports Server (NTRS)
Baker, W. E.; Brin, Y.
An energetics analysis scheme was developed to compare the observed kinetic energy balance over North America with that derived from forecast cyclone case. It is found that: (1) the observed and predicted kinetic energy and eddy conversion are in good qualitative agreement, although the model eddy conversion tends to be 2 to 3 times stronger than the observed values. The eddy conversion which is stronger in the 12 h forecast than in observations and may be due to several factors is studied; (2) vertical profiles of kinetic energy generation and dissipation exhibit lower and upper tropospheric maxima in both the forecast and observations; and (3) a lag in the observational analysis with the maximum in the observed kinetic energy occurring at 0000 GMT 14 January over the same region as the maximum Eddy conversion 12 h earlier is noted.
Comparison of Forecast and Observed Energetics
NASA Technical Reports Server (NTRS)
Baker, W. E.; Brin, Y.
An energetics analysis scheme was developed to compare the observed kinetic energy balance over North America with that derived from forecast fields of the GLAS fourth order model for the 13 to 15 January 1979 cyclone case. It is found that: (1) the observed and predicted kinetic energy and eddy conversion are in good qualitative agreement, although the model eddy conversion tends to be 2 to 3 times stronger than the observed values. The eddy conversion which is stronger in the 12 h forecast than in observations and may be due to several factors is studied; (2) vertical profiles of kinetic energy generation and dissipation exhibit lower and upper tropospheric maxima in both the forecast and observations; (3) a lag in the observational analysis with the maximum in the observed kinetic energy occurring at 0000 GMT 14 January over the same region as the maximum ddy conversion 12 h earlier is noted.
Forecasting cyanobacteria dominance in Canadian temperate lakes.
Persaud, Anurani D; Paterson, Andrew M; Dillon, Peter J; Winter, Jennifer G; Palmer, Michelle; Somers, Keith M
Predictive models based on broad scale, spatial surveys typically identify nutrients and climate as the most important predictors of cyanobacteria abundance; however these models generally have low predictive power because at smaller geographic scales numerous other factors may be equally or more important. At the lake level, for example, the ability to forecast cyanobacteria dominance is of tremendous value to lake managers as they can use such models to communicate exposure risks associated with recreational and drinking water use, and possible exposure to algal toxins, in advance of bloom occurrence. We used detailed algal, limnological and meteorological data from two temperate lakes in south-central Ontario, Canada to determine the factors that are closely linked to cyanobacteria dominance, and to develop easy to use models to forecast cyanobacteria biovolume. For Brandy Lake (BL), the strongest and most parsimonious model for forecasting % cyanobacteria biovolume (% CB) included water column stability, hypolimnetic TP, and % cyanobacteria biovolume two weeks prior. For Three Mile Lake (TML), the best model for forecasting % CB included water column stability, hypolimnetic TP concentration, and 7-d mean wind speed. The models for forecasting % CB in BL and TML are fundamentally different in their lag periods (BL = lag 1 model and TML = lag 2 model) and in some predictor variables despite the close proximity of the study lakes. We speculate that three main factors (nutrient concentrations, water transparency and lake morphometry) may have contributed to differences in the models developed, and may account for variation observed in models derived from large spatial surveys. Our results illustrate that while forecast models can be developed to determine when cyanobacteria will dominate within two temperate lakes, the models require detailed, lake-specific calibration to be effective as risk-management tools. PMID:25585147
Including Tidal Effects in Tsunami Forecasting
NASA Astrophysics Data System (ADS)
Arcas, D.; Moore, C. W.; Spillane, M. C.; Bernard, E. N.
Recently a new tsunami forecast system SIFT (Short-term Inundation and Forecasting of Tsunamis) has been declared operational by the National Weather Service (NWS) Tsunami Warning Centers. The SIFT system assimilates real-time information from a network of observing systems deployed in the open ocean, to produce on-the-fly estimates of tsunami impact at specific coastal communities. These estimates are computed via the tsunami simulation code MOST (Method of Splitting Tsunami) and include forecast products such as tsunami arrival time, duration of the event, predicted tsunami currents, maximum sea surface elevation and expected inundation areas. These computations are performed under the assumption that the mean sea level remains constant at Mean High Water (MHW) during the entire tsunami event. This assumption produces conservative tsunami forecasts that tend to err on the side of caution with the possibility of substantial overestimates of the inundation areas. To avoid this problem and produce more accurate, operational tsunami forecasts, we investigate the interaction of tsunamis with a longer period water level variation due to tidal forcing, by comparing simulations of the 2011 Japan event at different at different locations with and without tidal effects. Our results demonstrate that while non-linear effects resulting from this interaction are minimal in water surface elevation, they can have a significant effect on inundation areas. Based on these findings we propose a simple, first-order correction to the standard MHW forecast, that can be performed on-the-fly by the SIFT system without the need for complex tidal models.
Real-time eruption forecasting using the material Failure Forecast Method with a Bayesian approach
NASA Astrophysics Data System (ADS)
Boué, A.; Lesage, P.; Cortés, G.; Valette, B.; Reyes-Dávila, G.
Many attempts for deterministic forecasting of eruptions and landslides have been performed using the material Failure Forecast Method (FFM). This method consists in adjusting an empirical power law on precursory patterns of seismicity or deformation. Until now, most of the studies have presented hindsight forecasts based on complete time series of precursors and do not evaluate the ability of the method for carrying out real-time forecasting with partial precursory sequences. In this study, we present a rigorous approach of the FFM designed for real-time applications on volcano-seismic precursors. We use a Bayesian approach based on the FFM theory and an automatic classification of seismic events. The probability distributions of the data deduced from the performance of this classification are used as input. As output, it provides the probability of the forecast time at each observation time before the eruption. The spread of the a posteriori probability density function of the prediction time and its stability with respect to the observation time are used as criteria to evaluate the reliability of the forecast. We test the method on precursory accelerations of long-period seismicity prior to vulcanian explosions at Volcán de Colima (Mexico). For explosions preceded by a single phase of seismic acceleration, we obtain accurate and reliable forecasts using approximately 80% of the whole precursory sequence. It is, however, more difficult to apply the method to multiple acceleration patterns.
Baseline and target values for regional and point PV power forecasts: Toward improved solar forecasting
Zhang, Jie; Hodge, Bri -Mathias; Lu, Siyuan; Hamann, Hendrik F.; Lehman, Brad; Simmons, Joseph; Campos, Edwin; Banunarayanan, Venkat; Black, Jon; Tedesco, John
Accurate solar photovoltaic (PV) power forecasting allows utilities to reliably utilize solar resources on their systems. However, to truly measure the improvements that any new solar forecasting methods provide, it is important to develop a methodology for determining baseline and target values for the accuracy of solar forecasting at different spatial and temporal scales. This paper aims at developing a framework to derive baseline and target values for a suite of generally applicable, value-based, and custom-designed solar forecasting metrics. The work was informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models in combination with a radiative transfer model. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of PV power output. The proposed reserve-based methodology is a reasonable and practical approach that can be used to assess the economic benefits gained from improvements in accuracy of solar forecasting. Lastly, the financial baseline and targets can be translated back to forecasting accuracy metrics and requirements, which will guide research on solar forecasting improvements toward the areas that are most beneficial to power systems operations.
Baseline and target values for regional and point PV power forecasts: Toward improved solar forecasting
Zhang, Jie; Hodge, Bri -Mathias; Lu, Siyuan; Hamann, Hendrik F.; Lehman, Brad; Simmons, Joseph; Campos, Edwin; Banunarayanan, Venkat; Black, Jon; Tedesco, John
Accurate solar photovoltaic (PV) power forecasting allows utilities to reliably utilize solar resources on their systems. However, to truly measure the improvements that any new solar forecasting methods provide, it is important to develop a methodology for determining baseline and target values for the accuracy of solar forecasting at different spatial and temporal scales. This paper aims at developing a framework to derive baseline and target values for a suite of generally applicable, value-based, and custom-designed solar forecasting metrics. The work was informed by close collaboration with utility and independent system operator partners. The baseline values are established based onmore » state-of-the-art numerical weather prediction models and persistence models in combination with a radiative transfer model. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of PV power output. The proposed reserve-based methodology is a reasonable and practical approach that can be used to assess the economic benefits gained from improvements in accuracy of solar forecasting. Lastly, the financial baseline and targets can be translated back to forecasting accuracy metrics and requirements, which will guide research on solar forecasting improvements toward the areas that are most beneficial to power systems operations.« less
Probabilistic forecasts for Decision Support at the North Central River Forecast Center
NASA Astrophysics Data System (ADS)
Restrepo, Pedro; Buan, Steven; Connelly, Brian; DeWeese, Michael; Diamond, Laura; Ellis, Larry; Goering, Dustin; Holz, Andrea; Husaby, James; Merrigan, Douglas; Palmer, Justin; Pokorny, Daniel; Reckel, Holly; Sites, William; Stockhaus, Scott; Thornburg, Jonathon; Wavrin, Robert.; Ziemer, Mark
The North Central River Forecast Center (NCRFC) of the US National Weather Service has the responsibility for issuing river forecasts at 426 points over an area of nearly 890,000 km2, covering the Upper Mississippi river basin, the US watersheds flowing to lakes Superior, Huron and Michigan, and rivers flowing from the US to the Hudson Bay in Canada. The NCRFC issues probabilistic outlook forecasts at all its forecast points starting on December. While focused primarily on the risks associated with flooding during the spring snow melt down, the RFC frequently issues probabilistic forecasts to deal with water resources operations during drought times. This presentation will focus on probabilistic forecasts issued to assess flooding risk at Red River of the North , to support navigation operations on the Mississippi river during drought conditions, and on support of reservoir operations for hydropower generation and recreation. The presentation will discuss the improvements over the current practice that will be possible to achieve once the NWS Hydrologic Ensemble Forecasting System is put into operations later this year.
The Forecast Interpretation Tool-a Monte Carlo technique for blending climatic distributions with probabilistic forecasts
Husak, G.J.; Michaelsen, J.; Kyriakidis, P.; Verdin, J.P.; Funk, C.; Galu, G.
Probabilistic forecasts are produced from a variety of outlets to help predict rainfall, and other meteorological events, for periods of 1 month or more. Such forecasts are expressed as probabilities of a rainfall event, e.g. being in the upper, middle, or lower third of the relevant distribution of rainfall in the region. The impact of these forecasts on the expectation for the event is not always clear or easily conveyed. This article proposes a technique based on Monte Carlo simulation for adjusting existing climatologic statistical parameters to match forecast information, resulting in new parameters defining the probability of events for the forecast interval. The resulting parameters are shown to approximate the forecasts with reasonable accuracy. To show the value of the technique as an application for seasonal rainfall, it is used with consensus forecast developed for the Greater Horn of Africa for the 2009 March-April-May season. An alternative, analytical approach is also proposed, and discussed in comparison to the first simulation-based technique. Copyright ?? 2010 Royal Meteorological Society.
An Improved Forecasting Method of Sunspot Maximum
NASA Astrophysics Data System (ADS)
Yin, Z.; Tian, L.; Han, Y.; Wang, B.; Han, Y.
It has been paid more and more attention for forecasting sunspot maximum of future solar cycle in recent decades, and a variety of forecasting methods have been studied. However, to make an accurate prediction is still very difficult due to the complexities of the characteristics of solar activity. Some authors summerized a variety of methods for the maximum predictions of 22nd, 23rd, 24th solar cycles, the incomplete statistics are 63, 54 and 75 cases respectively, results of the methods, which the difference between forecasting and observed values within the range of ±15%, are 27.0%, 25.9% and 24.3% respectively. Using the 13 points smoothed value of monthly sunspot numbers, we studied correlation between sunspot number rising rate of the first 24 months of the solar cycle and the coming cycle maximum, published forecasting result that the maximum value was 139.2 ± 18.8 for 23rd solar cycle (Han et al., 2000), and the observed value is 120.8, the error is about 15.2%. The present paper describes our improved forecasting methods. First, Vondrak smoothing method is used to deal with the monthly sunspot numbers. It is studied that the relationship between the rise rate of earlier months of sunspot numbers of this smoothed sequence and the coming maximum value in each solar cycles. The results show that the first 22, 23, 24 months rise rate of sunspot numbers are highly related with the coming maximum values, and simulated prediction of maximum for 22~24 cycles show that using the 22-month rise rate of three solar cycles, the maximum forecasting error is about 13.2%, using 23-month rise rate, the maximum error is about 11.2%, while using 24-month rise rate, the maximum error is only about 9.3%. The new method not only improves the forecasting accuracy but also can make the forecasting time in advance at least half a year than the common method using 13 points monthly smoothed value.
Medium range flood forecasts at global scale
NASA Astrophysics Data System (ADS)
Voisin, N.; Wood, A. W.; Lettenmaier, D. P.; Wood, E. F.
While weather and climate forecast methods have advanced greatly over the last two decades, this capability has yet to be evidenced in mitigation of water-related natural hazards (primarily floods and droughts), especially in the developing world. Examples abound of extreme property damage and loss of life due to floods in the underdeveloped world. For instance, more than 4.5 million people were affected by the July 2000 flooding of the Mekong River and its tributaries in Cambodia, Vietnam, Laos and Thailand. The February- March 2000 floods in the Limpopo River of Mozambique caused extreme disruption to that country's fledgling economy. Mitigation of these events through advance warning has typically been modest at best. Despite the above noted improvement in weather and climate forecasts, there is at present no system for forecasting of floods globally, notwithstanding that the potential clearly exists. We describe a methodology that is eventually intended to generate global flood predictions routinely. It draws heavily from the experimental North American Land Data Assimilation System (NLDAS) and the companion Global Land Data Assimilation System (GLDAS) for development of nowcasts, and the University of Washington Experimental Hydrologic Prediction System to develop ensemble hydrologic forecasts based on Numerical Weather Prediction (NWP) models which serve both as nowcasts (and hence reduce the need for in situ precipitation and other observations in parts of the world where surface networks are critically deficient) and provide forecasts for lead times as long as fifteen days. The heart of the hydrologic modeling system is the University of Washington/Princeton University Variable Infiltration Capacity (VIC) macroscale hydrology model. In the prototype (tested using retrospective data), VIC is driven globally up to the time of forecast with daily ERA40 precipitation (rescaled on a monthly basis to a station-based global climatology), ERA40 wind, and ERA40
2014 Gulf of Mexico Hypoxia Forecast
Scavia, Donald; Evans, Mary Anne; Obenour, Dan
The Gulf of Mexico annual summer hypoxia forecasts are based on average May total nitrogen loads from the Mississippi River basin for that year. The load estimate, recently released by USGS, is 4,761 metric tons per day. Based on that estimate, we predict the area of this summer's hypoxic zone to be 14,000 square kilometers (95% credible interval, 8,000 to 20,000) – an "average year". Our forecast hypoxic volume is 50 km3 (95% credible interval, 20 to 77).
Load forecasting using artificial neural networks
Pham, K.D.
Artificial neural networks, modeled after their biological counterpart, have been successfully applied in many diverse areas including speech and pattern recognition, remote sensing, electrical power engineering, robotics and stock market forecasting. The most commonly used neural networks are those that gained knowledge from experience. Experience is presented to the network in form of the training data. Once trained, the neural network can recognized data that it has not seen before. This paper will present a fundamental introduction to the manner in which neural networks work and how to use them in load forecasting.
2013 Gulf of Mexico Hypoxia Forecast
Scavia, Donald; Evans, Mary Anne; Obenour, Dan
The Gulf of Mexico annual summer hypoxia forecasts are based on average May total nitrogen loads from the Mississippi River basin for that year. The load estimate, recently released by USGS, is 7,316 metric tons per day. Based on that estimate, we predict the area of this summer's hypoxic zone to be 18,900 square kilometers (95% credible interval, 13,400 to 24,200), the 7th largest reported and about the size of New Jersey. Our forecast hypoxic volume is 74.5 km3 (95% credible interval, 51.5 to 97.0), also the 7th largest on record.
Requirements of Operational Verification of the NWSRFS-ESP Forecasts
NASA Astrophysics Data System (ADS)
Imam, B.; Werner, K.; Hartmann, H.; Sorooshian, S.; Pritchard, E.
Forecast verification is the process of determining the quality of forecasts. This requires the utilization of quality measures that summarize one or more aspects of the relationship between forecasts and observations. Technically, the three main objectives of forecast verification are (a) monitoring, (b) improving, and (c) comparing the quality of different forecasting systems. However, users of forecast verification results range from administrators, who want to know the value of investing in forecast system improvement to forecasters and modelers, who want to assess areas of improving their own predictions, to forecast users, who weigh their decision based not only on the forecast but also on the perceived quality of such forecast. Our discussions with several forecasters and hydrologists in charge at various River Forecast Centers (RFCs) indicated that operational hydrologists view verification in a broader sense than their counterparts within the meteorological community. Their view encompasses verification as a possible tool in determining whether a forecast is ready for issuance as an "official" product or that it needs more work. In addition to the common challenges associated with verification of monthly and seasonal probabilistic forecasts. which include determining and obtaining the appropriate size of "forecast-observation" pairs data set, operational verification also requires the consideration of verification strategies for short-term forecasts. Under such condition, the identification of conditional verification (i.e., similar conditions) samples, tracking model states, input, and output, relative to their climatology, and the establishment of links between the forecast issuance, verification, and simulation components of the forecast system become important. In this presentation, we address the impacts of such view on the potential requirements of an operational verification system for the Ensemble Streamflow Prediction (ESP) component of the
Incorporate Hydrologic Forecast for Real-Time Reservoir Operations
NASA Astrophysics Data System (ADS)
Zhao, T.; Cai, X.; Zhao, J.
Advances in weather forecasting, hydrologic modeling, and hydro-climatic teleconnection relationships have significantly improved streamflow forecast precision and lead-time. The advances provide great opportunities to improve the operation rules of water resources systems, for example, updating reservoir operation curves using long-term forecast, or even replacing operation rules by real-time optimization and simulation models utilizing various streamflow forecast products. However, incorporation of forecast for real-time optimization of reservoir operation needs more understanding of the forecast uncertainty (FU) evolution with forecast horizon (FH, the advance time of a forecast) and the complicating effect of FU and FH. Increasing horizon may provide more information for decision making in a long time framework but with increasing error and less reliable information. This presentation addresses the challenges on the use of hydrologic forecast for real-time reservoir operations through the following two particular studies: 1) Evaluating the effectiveness of the various hydrological forecast products for reservoir operation with an explicit simulation of dynamic evolution of uncertainties involved in those products. A hypothetical example shows that optimal reservoir operation varies with the hydrologic forecast products. The utility of the reservoir operation with ensemble or probabilistic streamflow forecast (with a probabilistic uncertainty distribution) is the highest compared to deterministic streamflow forecast (DSF) with the forecast uncertainty represented in the form of deterministic forecast errors and DSF-based probabilistic streamflow forecast with the forecast uncertainty represented by a conditional distribution of forecast uncertainty for a given DSF. 2) Identifying an effective forecast horizon (EFH) under a limited inflow forecast considering the complicating effect of FH and FU, as well as streamflow variability and reservoir characteristics
Drought Monitoring and Forecasting Using the Princeton/U Washington National Hydrologic Forecasting System
NASA Astrophysics Data System (ADS)
Wood, E. F.; Yuan, X.; Roundy, J. K.; Lettenmaier, D. P.; Mo, K. C.; Xia, Y.; Ek, M. B.
Extreme hydrologic events in the form of droughts or floods are a significant source of social and economic damage in many parts of the world. Having sufficient warning of extreme events allows managers to prepare for and reduce the severity of their impacts. A hydrologic forecast system can give seasonal predictions that can be used by mangers to make better decisions; however there is still much uncertainty associated with such a system. Therefore it is important to understand the forecast skill of the system before transitioning to operational usage. Seasonal reforecasts (1982 - 2010) from the NCEP Climate Forecast System (both version 1 (CFS) and version 2 (CFSv2), Climate Prediction Center (CPC) outlooks and the European Seasonal Interannual Prediction (EUROSIP) system, are assessed for forecasting skill in drought prediction across the U.S., both singularly and as a multi-model system The Princeton/U Washington national hydrologic monitoring and forecast system is being implemented at NCEP/EMC via their Climate Test Bed as the experimental hydrological forecast system to support U.S. operational drought prediction. Using our system, the seasonal forecasts are biased corrected, downscaled and used to drive the Variable Infiltration Capacity (VIC) land surface model to give seasonal forecasts of hydrologic variables with lead times of up to six months. Results are presented for a number of events, with particular focus on the Apalachicola-Chattahoochee-Flint (ACF) River Basin in the South Eastern United States, which has experienced a number of severe droughts in recent years and is a pilot study basin for the National Integrated Drought Information System (NIDIS). The performance of the VIC land surface model is evaluated using observational forcing when compared to observed streamflow. The effectiveness of the forecast system to predict streamflow and soil moisture is evaluated when compared with observed streamflow and modeled soil moisture driven by
Evolutionary Forecast Engines for Solar Meteorology
NASA Astrophysics Data System (ADS)
Coimbra, C. F.
A detailed comparison of non-stationary regression and stochastic learning methods based on k-Nearest Neighbor (kNN), Artificial Neural Networks (ANN) and Genetic Algorithm (GA) approaches is carried out in order to develop high-fidelity solar forecast engines for several time horizons of interest. A hybrid GA/ANN method emerges as the most robust stochastic learning candidate. The GA/ANN approach In general the following decisions need to be made when creating an ANN-based solar forecast model: the ANN architecture: number of layers, numbers of neurons per layer; the preprocessing scheme; the fraction and distribution between training and testing data, and the meteorological and radiometric inputs. ANNs are very well suited to handle multivariate forecasting models due to their overall flexibility and nonlinear pattern recognition abilities. However, the forecasting skill of ANNs depends on a new set of parameters to be optimized within the context of the forecast model, which is the selection of input variables that most directly impact the fidelity of the forecasts. In a data rich scenario where irradiation, meteorological, and cloud cover data are available, it is not always evident which variables to include in the model a priori. New variables can also arise from data preprocessing such as smoothing or spectral decomposition. One way to avoid time-consuming trial-and-error approaches that have limited chance to result in optimal ANN topology and input selection is to couple the ANN with some optimization algorithm that scans the solution space and "evolves" the ANN structure. Genetic Algorithms (GAs) are well suited for this task. Results and Discussion The models built upon the historical data of 2009 and 2010 are applied to the 2011 data without modifications or retraining. We consider 3 solar variability seasons or periods, which are subsets of the total error evaluation data set. The 3 periods are defined based on the solar variability study as: - a high
The report gives regional air emission forecasts from three Process Model Projection Technique (PROMPT) runs. These estimates illustrate a range of possible future emissions. PROMPT, one of a number of National Acid Precipitation Assessment Program emission forecasting models, pr...
Forecasting in foodservice: model development, testing, and evaluation.
Miller, J L; Thompson, P A; Orabella, M M
This study was designed to develop, test, and evaluate mathematical models appropriate for forecasting menu-item production demand in foodservice. Data were collected from residence and dining hall foodservices at Ohio State University. Objectives of the study were to collect, code, and analyze the data; develop and test models using actual operation data; and compare forecasting results with current methods in use. Customer count was forecast using deseasonalized simple exponential smoothing. Menu-item demand was forecast by multiplying the count forecast by a predicted preference statistic. Forecasting models were evaluated using mean squared error, mean absolute deviation, and mean absolute percentage error techniques. All models were more accurate than current methods. A broad spectrum of forecasting techniques could be used by foodservice managers with access to a personal computer and spread-sheet and database-management software. The findings indicate that mathematical forecasting techniques may be effective in foodservice operations to control costs, increase productivity, and maximize profits. PMID:2019699
Technological Forecasting with a Multiple Regression Analysis Approach.
ERIC Educational Resources Information Center
Luftig, Jeffrey T.; Norton, Willis P.
This article examines simple and multiple regression analysis as forecasting tools, and details the process by which multiple regression analysis may be used to increase the accuracy of the technology forecast. (CT)
Development of an oil spill forecast system for offshore China
NASA Astrophysics Data System (ADS)
Wang, Yonggang; Wei, Zexun; An, Wei
An oil spill forecast system for offshore China was developed based on Visual C++. The oil spill forecast system includes an ocean environmental forecast model and an oil spill model. The ocean environmental forecast model was designed to include timesaving methods, and comprised a parametrical wind wave forecast model and a sea surface current forecast model. The oil spill model was based on the "particle method" and fulfills the prediction of oil particle behavior by considering the drifting, evaporation and emulsification processes. A specific database was embedded into the oil spill forecast system, which contained fundamental information, such as the properties of oil, reserve of emergency equipment and distribution of marine petroleum platform. The oil spill forecast system was successfully applied as part of an oil spill emergency exercise, and provides an operational service in the Research and Development Center for Offshore Oil Safety and Environmental Technology.
Skill of regional and global model forecast over Indian region
NASA Astrophysics Data System (ADS)
Kumar, Prashant; Kishtawal, C. M.; Pal, P. K.
The global model analysis and forecast have a significant impact on the regional model predictions, as global model provides the initial and lateral boundary condition to regional model. This study addresses an important question whether the regional model can improve the short-range weather forecast as compared to the global model. The National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) and the Weather Research and Forecasting (WRF) model are used in this study to evaluate the performance of global and regional models over the Indian region. A 24-h temperature and specific humidity forecast from the NCEP GFS model show less error compared to WRF model forecast. Rainfall prediction is improved over the Indian landmass when WRF model is used for rainfall forecast. Moreover, the results showed that high-resolution global model analysis (GFS4) improved the regional model forecast as compared to low-resolution global model analysis (GFS3).
Verification of ensemble flow forecasts for the River Rhine
NASA Astrophysics Data System (ADS)
Renner, M.; Werner, M. G. F.; Rademacher, S.; Sprokkereef, E.
SummaryEnsemble stream flow predictions obtained by forcing rainfall-runoff models with probabilistic weather forecasting products are becoming more commonly used in operational flood forecasting applications. In this paper the performance of ensemble flow forecasts at various stations in the Rhine basin are studied by the means of probabilistic verification statistics. When compared to climatology positive skill scores are found at all river gauges for lead times of up to 9 days, thus proving the medium-range flow forecasts to be useful. A preliminary comparison between the low resolution ECMWF-EPS forecast and the high-resolution COSMO-LEPS forecast products shows that downscaling of global meteorological forecast products is recommended before use in forcing rainfall-runoff models in flow forecasting.
Development of an oil spill forecast system for offshore China
NASA Astrophysics Data System (ADS)
Wang, Yonggang; Wei, Zexun; An, Wei
An oil spill forecast system for offshore China was developed based on Visual C++. The oil spill forecast system includes an ocean environmental forecast model and an oil spill model. The ocean environmental forecast model was designed to include timesaving methods, and comprised a parametrical wind wave forecast model and a sea surface current forecast model. The oil spill model was based on the "particle method" and fulfills the prediction of oil particle behavior by considering the drifting, evaporation and emulsification processes. A specific database was embedded into the oil spill forecast system, which contained fundamental information, such as the properties of oil, reserve of emergency equipment and distribution of marine petroleum platform. The oil spill forecast system was successfully applied as part of an oil spill emergency exercise, and provides an operational service in the Research and Development Center for Offshore Oil Safety and Environmental Technology.
Evaluation of an operational streamflow forecasting system driven by ensemble precipitation forecasts : a case study for the Gatineau watershed
NASA Astrophysics Data System (ADS)
Boucher, M.-A.; Perreault, L.; Tremblay, D.; Gaudet, J.; Minville, M.; Anctil, F.
Among the various sources of uncertainty for hydrological forecasts, the uncertainty linked to meteorological inputs prevail. Precipitation is particularly difficult to forecast and observed values are often poor representation of the true precipitation field. In order to account for the uncertainty related to precipitation data, it can be interesting to produce ensemble streamflow forecasts by feeding a hydrological model with ensemble precipitation forecasts issued by atmospheric models. In this study, we use ensemble precipitation forecasts to drive Hydrotel, a distributed hydrological model. We concentrate on the Gatineau watershed, which serves as an experimental watershed for Hydro-Québec, the major hydropower producer in Quebec. The main goal of this study is to demonstrate that ensemble precipitation forecasts can improve streamflow forecasting for the watershed of interest. The ensemble precipitation forecasts were produced by Environnement Canada from march first of 2002 to december 31st of 2003. They were obtained using two atmospheric models, SEF (8 members plus the control deterministic forecast) and GEM (8 members). The corresponding deterministic precipitation forecast issued by SEF model is also used with Hydrotel in order to compare ensemble streamflow forecasts with their deterministic counterparts. The quality of the precipitation forecasts is first assessed, using the continuous ranked probability score (CRPS), the logarithmic score, rank histograms and reliability diagrams. The performance of the corresponding streamflow forecasts obtained at the end of the process is also evaluated using the same quality assessment tools.
An Algorithm Combining for Objective Prediction with Subjective Forecast Information
NASA Astrophysics Data System (ADS)
Choi, JunTae; Kim, SooHyun
As direct or post-processed output from numerical weather prediction (NWP) models has begun to show acceptable performance compared with the predictions of human forecasters, many national weather centers have become interested in automatic forecasting systems based on NWP products alone, without intervention from human forecasters. The Korea Meteorological Administration (KMA) is now developing an automatic forecasting system for dry variables. The forecasts are automatically generated from NWP predictions using a post processing model (MOS). However, MOS cannot always produce acceptable predictions, and sometimes its predictions are rejected by human forecasters. In such cases, a human forecaster should manually modify the prediction consistently at points surrounding their corrections, using some kind of smart tool to incorporate the forecaster's opinion. This study introduces an algorithm to revise MOS predictions by adding a forecaster's subjective forecast information at neighbouring points. A statistical relation between two forecast points - a neighbouring point and a dependent point - was derived for the difference between a MOS prediction and that of a human forecaster. If the MOS prediction at a neighbouring point is updated by a human forecaster, the value at a dependent point is modified using a statistical relationship based on linear regression, with parameters obtained from a one-year dataset of MOS predictions and official forecast data issued by KMA. The best sets of neighbouring points and dependent point are statistically selected. According to verification, the RMSE of temperature predictions produced by the new algorithm was slightly lower than that of the original MOS predictions, and close to the RMSE of subjective forecasts. For wind speed and relative humidity, the new algorithm outperformed human forecasters.
Wind power forecasting in U.S. Electricity markets
Botterud, Audun; Wang, Jianhui; Miranda, Vladimiro; Bessa, Ricardo J.
Wind power forecasting is becoming an important tool in electricity markets, but the use of these forecasts in market operations and among market participants is still at an early stage. The authors discuss the current use of wind power forecasting in U.S. ISO/RTO markets, and offer recommendations for how to make efficient use of the information in state-of-the-art forecasts. (author)
Risky Business: Development, Communication and Use of Hydroclimatic Forecasts
NASA Astrophysics Data System (ADS)
Lall, U.
Inter-seasonal and longer hydroclimatic forecasts have been made increasingly in the last two decades following the increase in ENSO activity since the early 1980s and the success in seasonal ENSO forecasting. Yet, the number of examples of systematic use of these forecasts and their incorporation into water systems operation continue to be few. This may be due in part to the limited skill in such forecasts over much of the world, but is also likely due to the limited evolution of methods and opportunities to "safely" use uncertain forecasts. There has been a trend to rely more on "physically based" rather than "physically informed" empirical forecasts, and this may in part explain the limited success in developing usable products in more locations. Given the limited skill, forecasters have tended to "dumb" down their forecasts - either formally or subjectively shrinking the forecasts towards climatology, or reducing them to tercile forecasts that serve to obscure the potential information in the forecast. Consequently, the potential utility of such forecasts for decision making is compromised. Water system operating rules are often designed to be robust in the face of historical climate variability, and consequently are adapted to the potential conditions that a forecast seeks to inform. In such situations, there is understandable reluctance by managers to use the forecasts as presented, except in special cases where an alternate course of action is pragmatically appealing in any case. In this talk, I review opportunities to present targeted forecasts for use with decision systems that directly address climate risk and the risk induced by unbiased yet uncertain forecasts, focusing especially on extreme events and water allocation in a competitive environment. Examples from Brazil and India covering surface and ground water conjunctive use strategies that could potentially be insured and lead to improvements over the traditional system operation and resource
Technology Transfer Automated Retrieval System (TEKTRAN)
Seasonal climate forecasts are issued by NOAA/CPC for average temperature and total precipitation over 3-month overlapping periods covering the coming year. Many crop and hydrologic models employ weather generators based on monthly statistics to produce stochastic realizations of daily weather (e.g...
Seismogenic lavas and explosive eruption forecasting.
Lavallée, Y; Meredith, P G; Dingwell, D B; Hess, K-U; Wassermann, J; Cordonnier, B; Gerik, A; Kruhl, J H
Volcanic dome-building episodes commonly exhibit acceleration in both effusive discharge rate and seismicity before explosive eruptions. This should enable the application of material failure forecasting methods to eruption forecasting. To date, such methods have been based exclusively on the seismicity of the country rock. It is clear, however, that the rheology and deformation rate of the lava ultimately dictate eruption style. The highly crystalline lavas involved in these eruptions are pseudoplastic fluids that exhibit a strong component of shear thinning as their deformation accelerates across the ductile to brittle transition. Thus, understanding the nature of the ductile-brittle transition in dome lavas may well hold the key to an accurate description of dome growth and stability. Here we present the results of rheological experiments with continuous microseismic monitoring, which reveal that dome lavas are seismogenic and that the character of the seismicity changes markedly across the ductile-brittle transition until complete brittle failure occurs at high strain rates. We conclude that magma seismicity, combined with failure forecasting methods, could potentially be applied successfully to dome-building eruptions for volcanic forecasting. PMID:18497822
The report gives national and regional air emissions forecasts from several sulfur oxide and nitrogen oxide (SOx and NOx) emissions control Process Model Projection Technique (PROMPT) test runs. PROMPT, one of a number of National Acid Precipitation Assessment Program emission fo...
Online short-term solar power forecasting
Bacher, Peder; Madsen, Henrik; Nielsen, Henrik Aalborg
This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model. (author)
Applications products of aviation forecast models
NASA Technical Reports Server (NTRS)
Garthner, John P.
A service called the Optimum Path Aircraft Routing System (OPARS) supplies products based on output data from the Naval Oceanographic Global Atmospheric Prediction System (NOGAPS), a model run on a Cyber-205 computer. Temperatures and winds are extracted from the surface to 100 mb, approximately 55,000 ft. Forecast winds are available in six-hour time steps.
Issues in midterm analysis and forecasting 1998
Issues in Midterm Analysis and Forecasting 1998 (Issues) presents a series of nine papers covering topics in analysis and modeling that underlie the Annual Energy Outlook 1998 (AEO98), as well as other significant issues in midterm energy markets. AEO98, DOE/EIA-0383(98), published in December 1997, presents national forecasts of energy production, demand, imports, and prices through the year 2020 for five cases -- a reference case and four additional cases that assume higher and lower economic growth and higher and lower world oil prices than in the reference case. The forecasts were prepared by the Energy Information Administration (EIA), using EIA`s National Energy Modeling System (NEMS). The papers included in Issues describe underlying analyses for the projections in AEO98 and the forthcoming Annual Energy Outlook 1999 and for other products of EIA`s Office of Integrated Analysis and Forecasting. Their purpose is to provide public access to analytical work done in preparation for the midterm projections and other unpublished analyses. Specific topics were chosen for their relevance to current energy issues or to highlight modeling activities in NEMS. 59 figs., 44 tabs.
The FOAM operational deep ocean forecasting system
NASA Astrophysics Data System (ADS)
Hines, A.; Barciela, R.; Bell, M.; Holland, P.; Martin, M.; McCulloch, M.; Storkey, D.
The Forecasting Ocean Assimilation Model (FOAM) has been developed at the Met Office to provide operational real-time forecasts of the deep ocean to the Royal Navy. The model is built around the ocean and sea-ice components of the Met Office's Unified Model (UM), which is also used in coupled ocean-ice-atmosphere climate prediction. FOAM is forced by 6-hourly surface fluxes from the Met Office's Numerical Weather Prediction (NWP) system, and assimilates in situ profile data, in situ and satellite SST data, satellite derived sea-ice concentration data, and satellite altimeter sea surface height data. The operational system consists of a 1 degree resolution global model and a 1/3 degree resolution model covering the North Atlantic and Arctic oceans. The model suite runs daily, delivering forecast products directly to a visualisation system at the Royal Navy. The operational system also includes automatic verification of analyses and forecasts. A 1/9 degree model of the North Atlantic is being run daily on a pre-operational basis as part of GODAE and MERSEA. Output from this model is available on the internet in real time.
Issues in midterm analysis and forecasting, 1996
This document consists of papers which cover topics in analysis and modeling that underlie the Annual Energy Outlook 1996. Topics include: The Potential Impact of Technological Progress on U.S. Energy Markets; The Outlook for U.S. Import Dependence; Fuel Economy, Vehicle Choice, and Changing Demographics, and Annual Energy Outlook Forecast Evaluation.
Local Air Quality Conditions and Forecasts
... Location Map Center Forecast AQI Current AQI Current Ozone Current PM AQI Loop Ozone Loop PM Loop Action Day Maps by Monitor ... Partners Kids Movies NAQ Conferences NOAA Older Adults Ozone Particle Pollution (PM2.5, PM10) Publications Publicaciones (En ...
Forecasting and Resource Allocation in Educational Administration
ERIC Educational Resources Information Center
Schaefer, Marguerite J.
An awareness of all the forces affecting higher education today is not enough; carefully planned strategies to deal with them are also necessary for effective administration. Organizational-environmental concerns, the seven-component model for managing organizational complexity, and forecasting technologies are among topics discussed. (Editor/TA)
Small Area Forecasts: Policies, Results, and Evaluation.
ERIC Educational Resources Information Center
Southeast Michigan Council of Governments, Detroit.
This document describes aspects of the Small Area Forecast (SAF) process, from the preparation of policy alternatives to measures to provide for evaluation. The three principle areas discussed are: (1) the six alternative sets of public policies which might be followed in the Southeast region of Michigan for meeting population needs in the areas…
The Quest for the Perfect Weather Forecaster
ERIC Educational Resources Information Center
Kahl, Jonathan; Horwitz, Kevin; Berg, Craig; Gruhl, Mary
It is said that meteorology is the only profession where a person can be wrong half the time and still keep his or her job. The truth is not quite so bleak, but one can still ask, "Just how accurate are weather forecasters, anyway?" This article presents two projects for middle level students to investigate this issue in a hands-on,…
Visually Comparing Weather Features in Forecasts.
Quinan, P Samuel; Meyer, Miriah
Meteorologists process and analyze weather forecasts using visualization in order to examine the behaviors of and relationships among weather features. In this design study conducted with meteorologists in decision support roles, we identified and attempted to address two significant common challenges in weather visualization: the employment of inconsistent and often ineffective visual encoding practices across a wide range of visualizations, and a lack of support for directly visualizing how different weather features relate across an ensemble of possible forecast outcomes. In this work, we present a characterization of the problems and data associated with meteorological forecasting, we propose a set of informed default encoding choices that integrate existing meteorological conventions with effective visualization practice, and we extend a set of techniques as an initial step toward directly visualizing the interactions of multiple features over an ensemble forecast. We discuss the integration of these contributions into a functional prototype tool, and also reflect on the many practical challenges that arise when working with weather data. PMID:26390490
Using Satellite Data in Weather Forecasting: I
NASA Technical Reports Server (NTRS)
Jedlovec, Gary J.; Suggs, Ronnie J.; Lecue, Juan M.
The GOES Product Generation System (GPGS) is a set of computer codes and scripts that enable the assimilation of real-time Geostationary Operational Environmental Satellite (GOES) data into regional-weather-forecasting mathematical models. The GPGS can be used to derive such geophysical parameters as land surface temperature, the amount of precipitable water, the degree of cloud cover, the surface albedo, and the amount of insolation from satellite measurements of radiant energy emitted by the Earth and its atmosphere. GPGS incorporates a priori information (initial guesses of thermodynamic parameters of the atmosphere) and radiometric measurements from the geostationary operational environmental satellites along with mathematical models of physical principles that govern the transfer of energy in the atmosphere. GPGS solves the radiative-transfer equation and provides the resulting data products in formats suitable for use by weather-forecasting computer programs. The data-assimilation capability afforded by GPGS offers the potential to improve local weather forecasts ranging from 3 hours to 2 days - especially with respect to temperature, humidity, cloud cover, and the probability of precipitation. The improvements afforded by GPGS could be of interest to news media, utility companies, and other organizations that utilize regional weather forecasts.
Chesapeake Bay Hypoxic Volume Forecasts and Results
Evans, Mary Anne; Scavia, Donald
Given the average Jan-May 2013 total nitrogen load of 162,028 kg/day, this summer's hypoxia volume forecast is 6.1 km3, slightly smaller than average size for the period of record and almost the same as 2012. The late July 2013 measured volume was 6.92 km3.
Socio-Political Forecasting: Who Needs It?
ERIC Educational Resources Information Center
Burnett, D. Jack
Socio-political forecasting, a new dimension to university planning that can provide universities time to prepare for the impact of social and political changes, is examined. The four elements in the process are scenarios of the future, the probability/diffusion matrix, the profile of significant value-system changes, and integration and…
Flood Warning and Forecasting System in Slovakia
NASA Astrophysics Data System (ADS)
Leskova, Danica
In 2015, it finished project Flood Warning and Forecasting System (POVAPSYS) as part of the flood protection in Slovakia till 2010. The aim was to build POVAPSYS integrated computerized flood forecasting and warning system. It took a qualitatively higher level of output meteorological and hydrological services in case of floods affecting large territorial units, as well as local flood events. It is further unfolding demands on performance and coordination of meteorological and hydrological services, troubleshooting observation, evaluation of data, fast communication, modeling and forecasting of meteorological and hydrological processes. Integration of all information entering and exiting to and from the project POVAPSYS provides Hydrological Flood Forecasting System (HYPOS). The system provides information on the current hydrometeorological situation and its evolution with the generation of alerts and notifications in case of exceeding predefined thresholds. HYPOS's functioning of the system requires flawless operability in critical situations while minimizing the loss of its key parts. HYPOS is a core part of the project POVAPSYS, it is a comprehensive software solutions based on a modular principle, providing data and processed information including alarms, in real time. In order to achieve full functionality of the system, in proposal, we have put emphasis on reliability, robustness, availability and security.
Forecasting and management of hop downy mildew
Technology Transfer Automated Retrieval System (TEKTRAN)
Downy mildew of hop, caused by Pseudoperonospora humuli, is managed in the Pacific Northwestern U.S. by regular application of fungicides. A degree-day model that forecasts the first emergence of shoots systemically infection with P. humuli (termed basal spikes) and a risk index for secondary sprea...
Data mining methods for hydroclimatic forecasting
NASA Astrophysics Data System (ADS)
Wei, Wenge; Watkins, David W.
Skillful streamflow forecasts at seasonal lead times may be useful to water managers seeking to provide reliable water supplies and maximize hydrosystem benefits. In this study, a class of data mining techniques, known as tree-structured models, is investigated to address the nonlinear dynamics of climate teleconnections and screen promising probabilistic streamflow forecast models for river-reservoir systems. In a case study of the Lower Colorado River system in central Texas, a number of potential predictors are evaluated for forecasting seasonal streamflow, including large-scale climate indices related to the El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), and others. Results show that the tree-structured models can effectively capture the nonlinear features hidden in the data. Skill scores of probabilistic forecasts generated by both classification trees and logistic regression trees indicate that seasonal inflows throughout the system can be predicted with sufficient accuracy to improve water management, especially in the winter and spring seasons in central Texas.
In 2003, NOAA and EPA signed a Memorandum of Agreement to collaborate on the design and implementation of a capability to produce daily air quality modeling forecast information for the U.S. NOAA's ETA meteorological model and EPA's Community Multiscale Air Quality (CMAQ) model ...
Cognitive determinants of affective forecasting errors
Hoerger, Michael; Quirk, Stuart W.; Lucas, Richard E.; Carr, Thomas H.
Often to the detriment of human decision making, people are prone to an impact bias when making affective forecasts, overestimating the emotional consequences of future events. The cognitive processes underlying the impact bias, and methods for correcting it, have been debated and warrant further exploration. In the present investigation, we examined both individual differences and contextual variables associated with cognitive processing in affective forecasting for an election. Results showed that the perceived importance of the event and working memory capacity were both associated with an increased impact bias for some participants, whereas retrieval interference had no relationship with bias. Additionally, an experimental manipulation effectively reduced biased forecasts, particularly among participants who were most distracted thinking about peripheral life events. These findings have direct theoretical implications for understanding the impact bias, highlight the importance of individual differences in affective forecasting, and have ramifications for future decision making research. The possible functional role of the impact bias is discussed within the context of evolutionary psychology. PMID:21912580
Weather forecasting with open source software
NASA Astrophysics Data System (ADS)
Rautenhaus, Marc; Dörnbrack, Andreas
To forecast the weather situation during aircraft-based atmospheric field campaigns, we employ a tool chain of existing and self-developed open source software tools and open standards. Of particular value are the Python programming language with its extension libraries NumPy, SciPy, PyQt4, Matplotlib and the basemap toolkit, the NetCDF standard with the Climate and Forecast (CF) Metadata conventions, and the Open Geospatial Consortium Web Map Service standard. These open source libraries and open standards helped to implement the "Mission Support System", a Web Map Service based tool to support weather forecasting and flight planning during field campaigns. The tool has been implemented in Python and has also been released as open source (Rautenhaus et al., Geosci. Model Dev., 5, 55-71, 2012). In this presentation we discuss the usage of free and open source software for weather forecasting in the context of research flight planning, and highlight how the field campaign work benefits from using open source tools and open standards.
Forecasting sudden changes in environmental pollution patterns
Olascoaga, María J.; Haller, George
The lack of reliable forecasts for the spread of oceanic and atmospheric contamination hinders the effective protection of the ecosystem, society, and the economy from the fallouts of environmental disasters. The consequences can be dire, as evidenced by the Deepwater Horizon oil spill in the Gulf of Mexico in 2010. We present a methodology to predict major short-term changes in environmental contamination patterns, such as oil spills in the ocean and ash clouds in the atmosphere. Our approach is based on new mathematical results on the objective (frame-independent) identification of key material surfaces that drive tracer mixing in unsteady, finite-time flow data. Some of these material surfaces, known as Lagrangian coherent structures (LCSs), turn out to admit highly attracting cores that lead to inevitable material instabilities even under future uncertainties or unexpected perturbations to the observed flow. These LCS cores have the potential to forecast imminent shape changes in the contamination pattern, even before the instability builds up and brings large masses of water or air into motion. Exploiting this potential, the LCS-core analysis developed here provides a model-independent forecasting scheme that relies only on already observed or validated flow velocities at the time the prediction is made. We use this methodology to obtain high-precision forecasts of two major instabilities that occurred in the shape of the Deepwater Horizon oil spill. This is achieved using simulated surface currents preceding the prediction times and assuming that the oil behaves as a passive tracer. PMID:22411824
Multivariate postprocessing techniques for probabilistic hydrological forecasting
NASA Astrophysics Data System (ADS)
Hemri, S.; Lisniak, D.; Klein, B.
Hydrologic ensemble forecasts driven by atmospheric ensemble prediction systems need statistical postprocessing in order to account for systematic errors in terms of both location and spread. Runoff is an inherently multivariate process with typical events lasting from hours in case of floods to weeks or even months in case of droughts. This calls for multivariate postprocessing techniques that yield well-calibrated forecasts in univariate terms and ensure a realistic temporal dependence structure at the same time. To this end, the univariate ensemble model output statistics (EMOS) postprocessing method is combined with two different copula approaches that ensure multivariate calibration throughout the entire forecast horizon. The domain of this study covers three subcatchments of the river Rhine that represent different sizes and hydrological regimes: the Upper Rhine up to the gauge Maxau, the river Moselle up to the gauge Trier, and the river Lahn up to the gauge Kalkofen. In this study, the two approaches to model the temporal dependence structure are ensemble copula coupling (ECC), which preserves the dependence structure of the raw ensemble, and a Gaussian copula approach (GCA), which estimates the temporal correlations from training observations. The results indicate that both methods are suitable for modeling the temporal dependencies of probabilistic hydrologic forecasts.
PM10 forecasting using clusterwise regression
NASA Astrophysics Data System (ADS)
Poggi, Jean-Michel; Portier, Bruno
In this paper, we are interested in the statistical forecasting of the daily mean PM10 concentration. Hourly concentrations of PM10 have been measured in the city of Rouen, in Haute-Normandie, France. Located at northwest of Paris, near the south side of Manche sea and heavily industrialised. We consider three monitoring stations reflecting the diversity of situations: an urban background station, a traffic station and an industrial station near the cereal harbour of Rouen. We have focused our attention on data for the months that register higher values, from December to March, on years 2004-2009. The models are obtained from the winter days of the four seasons 2004/2005 to 2007/2008 (training data) and then the forecasting performance is evaluated on the winter days of the season 2008/2009 (test data). We show that it is possible to accurately forecast the daily mean concentration by fitting a function of meteorological predictors and the average concentration measured on the previous day. The values of observed meteorological variables are used for fitting the models and are also considered for the test data. We have compared the forecasts produced by three different methods: persistence, generalized additive nonlinear models and clusterwise linear regression models. This last method gives very impressive results and the end of the paper tries to analyze the reasons of such a good behavior.
Forecasting catastrophe by exploiting chaotic dynamics
Stewart, H.B.; Lansbury, A.N.
Our purpose here is to introduce a variation on the theme of short term forecasting from a chaotic time series. We show that for the lowest-dimensional chaotic attractors, it is possible to predict incipient catastrophes, or crises, by examining time series data taken near the catastrophic bifurcation threshold, but always remaining on the safe side of the threshold.
Computerized Enrollment Driven Financial Forecasting Model.
ERIC Educational Resources Information Center
Sarvella, John R.
An interactive, computerized model developed for Old Dominion University utilizes university historical data, demographic characteristics, projected selected economic variables and population figures by various age groups and planning districts to forecast enrollment, financial projections, and future fiscal conditions of the institution. The…
Long Range Financial Forecasting for School Districts.
ERIC Educational Resources Information Center
Baker, Michael E.
Public school systems infrequently project their financial outlook beyond the coming year. Yet, financial projections over a multiyear period are necessary if the financial "crises" that frequently occur in public organizations are to be avoided. This paper discusses the importance of financial forecasting and planning, the development of…
Financial Forecasts for the Next Decade.
ERIC Educational Resources Information Center
Ehrenburg, Ronald G.
Examines implications of financial forecasts for the next decade on institutions of higher education. These address the pressures on state spending for higher education of substantially increased student enrollments, likely responses of public institutions, likely responses of state systems, and financial pressures on private institutions. Both…
Long-range forecasting of intermittent streamflow
NASA Astrophysics Data System (ADS)
van Ogtrop, F. F.; Vervoort, R. W.; Heller, G. Z.; Stasinopoulos, D. M.; Rigby, R. A.
Long-range forecasting of intermittent streamflow in semi-arid Australia poses a number of major challenges. One of the challenges relates to modelling zero, skewed, non-stationary, and non-linear data. To address this, a probabilistic statistical model to forecast streamflow 12 months ahead is applied to five semi-arid catchments in South Western Queensland. The model uses logistic regression through Generalised Additive Models for Location, Scale and Shape (GAMLSS) to determine the probability of flow occurring in any of the systems. We then use the same regression framework in combination with a right-skewed distribution, the Box-Cox t distribution, to model the intensity (depth) of the non-zero streamflows. Time, seasonality and climate indices, describing the Pacific and Indian Ocean sea surface temperatures, are tested as covariates in the GAMLSS model to make probabilistic 12-month forecasts of the occurrence and intensity of streamflow. The output reveals that in the study region the occurrence and variability of flow is driven by sea surface temperatures and therefore forecasts can be made with some skill.
Long-range forecasting of intermittent streamflow
NASA Astrophysics Data System (ADS)
van Ogtrop, F. F.; Vervoort, R. W.; Heller, G. Z.; Stasinopoulos, D. M.; Rigby, R. A.
Long-range forecasting of intermittent streamflow in semi-arid Australia poses a number of major challenges. One of the challenges relates to modelling zero, skewed, non-stationary, and non-linear data. To address this, a statistical model to forecast streamflow up to 12 months ahead is applied to five semi-arid catchments in South Western Queensland. The model uses logistic regression through Generalised Additive Models for Location, Scale and Shape (GAMLSS) to determine the probability of flow occurring in any of the systems. We then use the same regression framework in combination with a right-skewed distribution, the Box-Cox t distribution, to model the intensity (depth) of the non-zero streamflows. Time, seasonality and climate indices, describing the Pacific and Indian Ocean sea surface temperatures, are tested as covariates in the GAMLSS model to make probabilistic 6 and 12-month forecasts of the occurrence and intensity of streamflow. The output reveals that in the study region the occurrence and variability of flow is driven by sea surface temperatures and therefore forecasts can be made with some skill.
Increased awareness of national air quality issues on the part of the media and the general public have recently led to more demand for short-term (1-2 day) air quality forecasts for use in assessing potential health impacts (e.g., on children, the elderly, and asthmatics) and po...
Demand forecasting using fuzzy neural computation, with special emphasis on weekend and public holiday forecasting
Srinivasan, D.; Chang, C.S.; Liew, A.C.
This paper describes the implementation and forecasting results of a hybrid fuzzy neural technique, which combines neural network modeling, and techniques from fuzzy logic and fuzzy set theory for electric load forecasting. The strengths of this powerful technique lie in its ability to forecast accurately on weekdays, as well as, on weekends, public holidays, and days before and after public holidays. Furthermore, use of fuzzy logic effectively handles the load variations due to special events. The Fuzzy-Neural Network (FNN) has been extensively tested on actual data obtained from a power system for 24-hour ahead prediction based on forecast weather information. Very impressive results, with an average error of 0.62% on weekdays, 0.83% on Saturdays and 1.17% on Sundays and public holidays have been obtained. This approach avoids complex mathematical calculations and training on many years of data, and is simple to implement on a personal computer.
Modeling, Simulation, and Forecasting of Subseasonal Variability
NASA Technical Reports Server (NTRS)
Waliser, Duane; Schubert, Siegfried; Kumar, Arun; Weickmann, Klaus; Dole, Randall
A planning workshop on "Modeling, Simulation and Forecasting of Subseasonal Variability" was held in June 2003. This workshop was the first of a number of meetings planned to follow the NASA-sponsored workshop entitled "Prospects For Improved Forecasts Of Weather And Short-Term Climate Variability On Sub-Seasonal Time Scales" that was held April 2002. The 2002 workshop highlighted a number of key sources of unrealized predictability on subseasonal time scales including tropical heating, soil wetness, the Madden Julian Oscillation (MJO) [a.k.a Intraseasonal Oscillation (ISO)], the Arctic Oscillation (AO) and the Pacific/North American (PNA) pattern. The overarching objective of the 2003 follow-up workshop was to proceed with a number of recommendations made from the 2002 workshop, as well as to set an agenda and collate efforts in the areas of modeling, simulation and forecasting intraseasonal and short-term climate variability. More specifically, the aims of the 2003 workshop were to: 1) develop a baseline of the "state of the art" in subseasonal prediction capabilities, 2) implement a program to carry out experimental subseasonal forecasts, and 3) develop strategies for tapping the above sources of predictability by focusing research, model development, and the development/acquisition of new observations on the subseasonal problem. The workshop was held over two days and was attended by over 80 scientists, modelers, forecasters and agency personnel. The agenda of the workshop focused on issues related to the MJO and tropicalextratropical interactions as they relate to the subseasonal simulation and prediction problem. This included the development of plans for a coordinated set of GCM hindcast experiments to assess current model subseasonal prediction capabilities and shortcomings, an emphasis on developing a strategy to rectify shortcomings associated with tropical intraseasonal variability, namely diabatic processes, and continuing the implementation of an
Ethical issues in forecasting of natural hazards
NASA Astrophysics Data System (ADS)
Tinti, Stefano
Natural hazards have by definition a large impact on the society and, therefore, since the beginning of science one of the major aspiration of mankind has been the prediction of natural calamities in the attempt to avoid or to mitigate their effects. In modern societies where science and technology have gained a foundational role, forecasts and predictions have become part of the every-day life and may also influence state policies and economic development. And in parallel with the growing importance of forecasting, even ethical problems for forecasters and for forecasters communities have started to appear. In this work two of the many geo-ethical issues are considered mostly: 1) how to cope with uncertainties that are inherently associated with any forecast statement; 2) how to handle predictions in scientific journals and scientific conferences The former issue is mainly related to the impact of predictions on the general public and on managers and operators in the civil protection field. Forecasters operate in specific contexts that 1) may change from country to country, depending on the local adopted best practices, but also, which is more constraining, on the local legal regulations and laws; 2) may change from discipline to discipline according to the development of the specific knowhow and the range of the forecast (from minutes to centuries) The second issue has to do with the communication of the scientific results on predictions and on prediction methods to the audience mainly composed of scientists, and involves one of the basic elements of science. In principle, scientists should use scientific communication means (papers in scientific journals, conferences, …) to illustrate results that are sound and certain, or the methods by means of which they conduct their research. But scientists involved in predictions have inherently to do with uncertainties, and, since there is no common agreement on how to deal with them, there is the risk that scientific
Ensemble postprocessing for probabilistic quantitative precipitation forecasts
NASA Astrophysics Data System (ADS)
Bentzien, S.; Friederichs, P.
Precipitation is one of the most difficult weather variables to predict in hydrometeorological applications. In order to assess the uncertainty inherent in deterministic numerical weather prediction (NWP), meteorological services around the globe develop ensemble prediction systems (EPS) based on high-resolution NWP systems. With non-hydrostatic model dynamics and without parameterization of deep moist convection, high-resolution NWP models are able to describe convective processes in more detail and provide more realistic mesoscale structures. However, precipitation forecasts are still affected by displacement errors, systematic biases and fast error growth on small scales. Probabilistic guidance can be achieved from an ensemble setup which accounts for model error and uncertainty of initial and boundary conditions. The German Meteorological Service (Deutscher Wetterdienst, DWD) provides such an ensemble system based on the German-focused limited-area model COSMO-DE. With a horizontal grid-spacing of 2.8 km, COSMO-DE is the convection-permitting high-resolution part of the operational model chain at DWD. The COSMO-DE-EPS consists of 20 realizations of COSMO-DE, driven by initial and boundary conditions derived from 4 global models and 5 perturbations of model physics. Ensemble systems like COSMO-DE-EPS are often limited with respect to ensemble size due to the immense computational costs. As a consequence, they can be biased and exhibit insufficient ensemble spread, and probabilistic forecasts may be not well calibrated. In this study, probabilistic quantitative precipitation forecasts are derived from COSMO-DE-EPS and evaluated at more than 1000 rain gauges located all over Germany. COSMO-DE-EPS is a frequently updated ensemble system, initialized 8 times a day. We use the time-lagged approach to inexpensively increase ensemble spread, which results in more reliable forecasts especially for extreme precipitation events. Moreover, we will show that statistical
Automated turbulence forecasts for aviation hazards
NASA Astrophysics Data System (ADS)
Sharman, R.; Frehlich, R.; Vandenberghe, F.
An operational turbulence forecast system for commercial and aviation use is described that is based on an ensemble of turbulence diagnostics derived from standard NWP model outputs. In the U. S. this forecast product is named GTG (Graphical Turbulence Guidance) and has been described in detail in Sharman et al., WAF 2006. Since turbulence has many sources in the atmosphere, the ensemble approach of combining diagnostics has been shown to provide greater statistical accuracy than the use of a single diagnostic, or of a subgrid tke parameterization. GTG is sponsored by the FAA, and has undergone rigorous accuracy, safety, and usability evaluations. The GTG product is now hosted on NOAA's Aviation Data Service (ADDS), web site (http://aviationweather.gov/), for access by pilots, air traffic controllers, and dispatchers. During this talk the various turbulence diagnostics, their statistical properties, and their relative performance (based on comparisons to observations) will be presented. Importantly, the model output is ɛ1/3 (where ɛ is the eddy dissipation rate), so is aircraft independent. The diagnostics are individually and collectively calibrated so that their PDFs satisfy the expected log normal distribution of ɛ^1/3. Some of the diagnostics try to take into account the role of gravity waves and inertia-gravity waves in the turbulence generation process. Although the current GTG product is based on the RUC forecast model running over the CONUS, it is transitioning to a WRF based product, and in fact WRF-based versions are currently running operationally over Taiwan and has also been implemented for use by the French Navy in climatological studies. Yet another version has been developed which uses GFS model output to provide global turbulence forecasts. Thus the forecast product is available as a postprocessing program for WRF or other model output and provides 3D maps of turbulence likelihood of any region where NWP model data is available. Although the
Diagnostic studies of ensemble forecast "jumps"
NASA Astrophysics Data System (ADS)
Magnusson, Linus; Hewson, Tim; Ferranti, Laura; Rodwell, Mark
During 2015 we saw exceptional consistency in successive seasonal forecasts produced at ECMWF, for the winter period 2015/16, right across the globe. This winter was characterised by a well-predicted and unusually strong El Nino, and some have ascribed the consistency to that. For most of December this consistency was mirrored in the (separate) ECMWF monthly forecast system, which correctly predicted anomalously strong (mild) zonal flow, over the North Atlantic and western Eurasia, even in forecasts for weeks 3 and 4. In monthly forecasts in general these weeks are often devoid of strong signals. However in late December and early January strong signals, even in week 2, proved to be incorrect, most notably over the North Atlantic and Eurasian sectors. Indeed on at least two occasions the outcome was beyond the ensemble forecast range over Scandinavia. In one of these conditions flipped from extreme mild to extreme cold as a high latitude block developed. Temperature prediction is very important to many customers, notably those dealing with renewable energy, because cold weather causes increased demand but also tends to coincide with reduced wind power production. So understandably jumps can cause consternation amongst some customer groups, and are very difficult to handle operationally. This presentation will discuss the results of initial diagnostic investigations into what caused the "ensemble jumps", particularly at the week two lead, though reference will also be made to a related shorter range (day 3) jump that was important for flooding over the UK. Initial results suggest that an inability of the ECMWF model to correctly represent convective outbreaks over North America (that for winter-time were quite extreme) played an important role. Significantly, during this period, an unusually large amount of upper air data over North America was rejected or ascribed low weight. These results bear similarities to previous diagnostic studies at ECMWF, wherein major
17 CFR 210.11-03 - Presentation of financial forecast.
Code of Federal Regulations, 2011 CFR
... 17 Commodity and Securities Exchanges 2 2011-04-01 2011-04-01 false Presentation of financial forecast. 210.11-03 Section 210.11-03 Commodity and Securities Exchanges SECURITIES AND EXCHANGE COMMISSION... Information § 210.11-03 Presentation of financial forecast. (a) A financial forecast may be filed in lieu...
Communicating Environmental Uncertainty: The Nature of Weather Forecasts.
ERIC Educational Resources Information Center
Travis, Richard W.; Riebsame, William E.
Traces the path of weather forecasts from the time they are made by the National Oceanic and Atmospheric Administration until the time they are received by the public through the mass media. The purpose of the article is to provide geography teachers with basic information on weather forecasts, interpretation of forecast terms, and indications…
Ensemble approach for hydrological forecasting in ungauged catchments
NASA Astrophysics Data System (ADS)
Randrianasolo, Annie; Ramos, Maria-Helena; Andreassian, Vazken
This study focuses on the application of ensemble approaches to forecast flows in ungauged catchments. The aim is to study the best strategy to search for information in gauged "donor" basins and to transfer it to the ungauged site. We investigate what information is needed to set up a rainfall-runoff model and to perform forecast updating in real time. These two components of a flood forecasting system are thus decoupled in our approach. The methodology adopted integrates the scenarios of regional transfer of information and the scenarios of ensemble weather forecasting together in a forecasting system. The approach of ensemble forecasting is thus generalised to the particular case of hydrological forecasting in ungauged basins. The study is based on 211 catchments in France and on an archive of about 4.5 years of ensemble forecasts of rainfall, which are used for hydrological modelling on a daily time step. Flow forecasts are evaluated with special attention paid to the attributes of reliability and accuracy of the forecasts. The results show that forecast reliability in ungauged sites can be improved by using several sets of parameters from neighbour catchments, while forecast accuracy is improved with the transfer of updating information from gauged neighbour catchments.
Method for Water Management Considering Long-term Probabilistic Forecasts
NASA Astrophysics Data System (ADS)
Hwang, J.; Kang, J.; Suh, A. S.
This research is aimed at predicting the monthly inflow of the Andong-dam basin in South Korea using long-term probabilistic forecasts to apply long-term forecasts to water management. Forecasted Cumulative Distribution Functions (CDFs) of monthly precipitation are plotted by combining the range of monthly precipitation based on proper Probability Density Function (PDF) in past data with probabilistic forecasts in each category. Ensembles of inflow are estimated by entering generated ensembles of precipitation based on the CDFs into the 'abcd' water budget model. The bias and RMSE between averages in past data and observed inflow are compared to them in forecasted ensembles. In our results, the bias and RMSE of average precipitation in the forecasted ensemble are bigger than in past data, whereas the average inflow in the forecasted ensemble is smaller than in past data. This result could be used for reference data to apply long-term forecasts to water management, because of the limit in the number of forecasted data for verification and differences between the Andong-dam basin and the forecasted regions. This research has significance by suggesting a method of applying probabilistic information in climate variables from long-term forecasts to water management in Korea. Original data of a climate model, which produces long-term probabilistic forecasts should be verified directly as input data of a water budget model in the future, so that a more scientific response in water management against uncertainty of climate change could be reached.
STEP, Year 1, Volume III: An Enrollment Forecaster for STEP.
ERIC Educational Resources Information Center
Ackerman, Jerry; And Others
This volume describes an automated procedure for multiyear enrollment forecasting in the Trenton, New Jersey, public schools. Enrollment forecasts generated by this procedure will provide enrollment estimates in each district's instructional program. Data required to operate the forecaster will be collected during the second year of its operation.…
Naive vs. Sophisticated Methods of Forecasting Public Library Circulations.
ERIC Educational Resources Information Center
Brooks, Terrence A.
Two sophisticated--autoregressive integrated moving average (ARIMA), straight-line regression--and two naive--simple average, monthly average--forecasting techniques were used to forecast monthly circulation totals of 34 public libraries. Comparisons of forecasts and actual totals revealed that ARIMA and monthly average methods had smallest mean…
Issues in Forecasting Graduate Dental Education Manpower Supply and Requirements.
ERIC Educational Resources Information Center
Born, David O.
The history of forecasting in dentistry is explored with a focus on several major forecasting techniques, briefly examining the basic assumptions, data requirements, and strengths and weaknesses of each. Three perspectives held by forecasters are isolated: health status, social need, and economic. (Author/MLW)
14 CFR 135.213 - Weather reports and forecasts.
Code of Federal Regulations, 2012 CFR
... 14 Aeronautics and Space 3 2012-01-01 2012-01-01 false Weather reports and forecasts. 135.213... Operating Limitations and Weather Requirements § 135.213 Weather reports and forecasts. (a) Whenever a person operating an aircraft under this part is required to use a weather report or forecast, that...
14 CFR 135.213 - Weather reports and forecasts.
Code of Federal Regulations, 2014 CFR
... 14 Aeronautics and Space 3 2014-01-01 2014-01-01 false Weather reports and forecasts. 135.213... Operating Limitations and Weather Requirements § 135.213 Weather reports and forecasts. (a) Whenever a person operating an aircraft under this part is required to use a weather report or forecast, that...
14 CFR 135.213 - Weather reports and forecasts.
Code of Federal Regulations, 2013 CFR
... 14 Aeronautics and Space 3 2013-01-01 2013-01-01 false Weather reports and forecasts. 135.213... Operating Limitations and Weather Requirements § 135.213 Weather reports and forecasts. (a) Whenever a person operating an aircraft under this part is required to use a weather report or forecast, that...
Selection and Classification Using a Forecast Applicant Pool.
ERIC Educational Resources Information Center
Hendrix, William H.
The document presents a forecast model of the future Air Force applicant pool. By forecasting applicants' quality (means and standard deviations of aptitude scores) and quantity (total number of applicants), a potential enlistee could be compared to the forecasted pool. The data used to develop the model consisted of means, standard deviation, and…
New Employment Forecasts. Hotel and Catering Industry 1988-1993.
ERIC Educational Resources Information Center
Measurement for Management Decision, Ltd., London (England).
Econometric forecasting models were used to forecast employment levels in the hotel and catering industry in Great Britain through 1993 under several different forecasting scenarios. The growth in employment in the hotel and catering industry over the next 5 years is likely to be broadly based, both across income levels of domestic consumers,…
14 CFR 135.213 - Weather reports and forecasts.
Code of Federal Regulations, 2010 CFR
... 14 Aeronautics and Space 3 2010-01-01 2010-01-01 false Weather reports and forecasts. 135.213... Operating Limitations and Weather Requirements § 135.213 Weather reports and forecasts. (a) Whenever a person operating an aircraft under this part is required to use a weather report or forecast, that...
14 CFR 135.213 - Weather reports and forecasts.
Code of Federal Regulations, 2011 CFR
... 14 Aeronautics and Space 3 2011-01-01 2011-01-01 false Weather reports and forecasts. 135.213... Operating Limitations and Weather Requirements § 135.213 Weather reports and forecasts. (a) Whenever a person operating an aircraft under this part is required to use a weather report or forecast, that...
7 CFR 1710.301 - Financial forecasts-distribution borrowers.
Code of Federal Regulations, 2010 CFR
... 7 Agriculture 11 2010-01-01 2010-01-01 false Financial forecasts-distribution borrowers. 1710.301... AND GUARANTEES Long-Range Financial Forecasts § 1710.301 Financial forecasts—distribution borrowers. (a) Financial forecasts prepared by distribution borrowers shall cover at least a ten-year...
The Wind Forecast Improvement Project (WFIP). A Public-Private Partnership Addressing Wind Energy Forecast Needs
Wilczak, James M.; Finley, Cathy; Freedman, Jeff; Cline, Joel; Bianco, L.; Olson, J.; Djalaova, I.; Sheridan, L.; Ahlstrom, M.; Manobianco, J.; Zack, J.; Carley, J.; Benjamin, S.; Coulter, R. L.; Berg, Larry K.; Mirocha, Jeff D.; Clawson, K.; Natenberg, E.; Marquis, M.
The Wind Forecast Improvement Project (WFIP) is a public-private research program, the goals of which are to improve the accuracy of short-term (0-6 hr) wind power forecasts for the wind energy industry and then to quantify the economic savings that accrue from more efficient integration of wind energy into the electrical grid. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that include the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collection of special observations to be assimilated into forecast models to improve model initial conditions; and second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the U.S. (the upper Great Plains, and Texas), and included 12 wind profiling radars, 12 sodars, 184 instrumented tall towers and over 400 nacelle anemometers (provided by private industry), lidar, and several surface flux stations. Results demonstrate that a substantial improvement of up to 14% relative reduction in power root mean square error (RMSE) was achieved from the combination of improved NOAA numerical weather prediction (NWP) models and assimilation of the new observations. Data denial experiments run over select periods of time demonstrate that up to a 6% relative improvement came from the new observations. The use of ensemble forecasts produced even larger forecast improvements. Based on the success of WFIP, DOE is planning follow-on field programs.
Werth, D.; Garrett, A.
We validated one year of Global Forecast System (GFS) predictions of surface meteorological variables (wind speed, air temperature, dewpoint temperature, air pressure) over the entire planet for forecasts extending from zero hours into the future (an analysis) to 36 hours. Approximately 12,000 surface stations world-wide were included in this analysis. Root-Mean-Square- Errors (RMSE) increased as the forecast period increased from zero to 36 hours, but the initial RMSE were almost as large as the 36 hour forecast RMSE for all variables. Typical RMSE were 3 C for air temperature, 2-3mb for sea-level pressure, 3.5 C for dewpoint temperature and 2.5 m/s for wind speed. Approximately 20-40% of the GFS errors can be attributed to a lack of resolution of local features. We attribute the large initial RMSE for the zero hour forecasts to the inability of the GFS to resolve local terrain features that often dominate local weather conditions, e.g., mountain- valley circulations and sea and land breezes. Since the horizontal resolution of the GFS (about 1{sup o} of latitude and longitude) prevents it from simulating these locally-driven circulations, its performance will not improve until model resolution increases by a factor of 10 or more (from about 100 km to less than 10 km). Since this will not happen in the near future, an alternative for the near term to improve surface weather analyses and predictions for specific points in space and time would be implementation of a high-resolution, limited-area mesoscale atmospheric prediction model in regions of interest.
Seasonal UK Drought Forecasting using Statistical Methods
NASA Astrophysics Data System (ADS)
Richardson, Doug; Fowler, Hayley; Kilsby, Chris; Serinaldi, Francesco
In the UK drought is a recurrent feature of climate with potentially large impacts on public water supply. Water companies' ability to mitigate the impacts of drought by managing diminishing availability depends on forward planning and it would be extremely valuable to improve forecasts of drought on monthly to seasonal time scales. By focusing on statistical forecasting methods, this research aims to provide techniques that are simpler, faster and computationally cheaper than physically based models. In general, statistical forecasting is done by relating the variable of interest (some hydro-meteorological variable such as rainfall or streamflow, or a drought index) to one or more predictors via some formal dependence. These predictors are generally antecedent values of the response variable or external factors such as teleconnections. A candidate model is Generalised Additive Models for Location, Scale and Shape parameters (GAMLSS). GAMLSS is a very flexible class allowing for more general distribution functions (e.g. highly skewed and/or kurtotic distributions) and the modelling of not just the location parameter but also the scale and shape parameters. Additionally GAMLSS permits the forecasting of an entire distribution, allowing the output to be assessed in probabilistic terms rather than simply the mean and confidence intervals. Exploratory analysis of the relationship between long-memory processes (e.g. large-scale atmospheric circulation patterns, sea surface temperatures and soil moisture content) and drought should result in the identification of suitable predictors to be included in the forecasting model, and further our understanding of the drivers of UK drought.
[Explanation and forecast: relapse of juvenile offenders].
Giebel, S M
On the basis of n=82 juvenile offenders from a prison for juvenile offenders in Rheinland Pfalz the model of the logistic regression is compared with a procedure from the family of the neural nets in its efficiency to explain and predict "relapse" in form of a renewed imprisonment or prosecution /police search after dismissal. The group which can be examined is limited by the population of the prison for juvenile offenders and the explaining variables for "relapse" as "addicted to drugs" present non-metric scaling. For the explanation only probabilities for "relapse" can be indicated in this connection. By means of this probability it is possible to classify the individual case. The forecast is simulated by coincidental dividing of the data: the first part of the data is used for the explanation, the second for the forecast. With the comparison of the logistic regression with the neural nets, the superiority of neural nets in the explanation of "relapse" can be shown, since the neural nets are able to consider dependence between the explaining variables and according to that they offer a differentiated explanation. Their efficiency to predict "relapse" depends on the comparability of the distribution in the two coincidentally provided samples, the training data record for determining the explanation and the test case for the use of the explanation regarding the forecast. For optimal explanation and forecast neural nets are to be preferred to the logistic regression, since in the model with the better explanation also includes the potential for a usable better forecast. Moreover the model of the logistic regression is in fact a special case of the neural net, with a reduced complexity of the net. PMID:17124800
Operational forecasting based on a modified Weather Research and Forecasting model
Lundquist, J; Glascoe, L; Obrecht, J
Accurate short-term forecasts of wind resources are required for efficient wind farm operation and ultimately for the integration of large amounts of wind-generated power into electrical grids. Siemens Energy Inc. and Lawrence Livermore National Laboratory, with the University of Colorado at Boulder, are collaborating on the design of an operational forecasting system for large wind farms. The basis of the system is the numerical weather prediction tool, the Weather Research and Forecasting (WRF) model; large-eddy simulations and data assimilation approaches are used to refine and tailor the forecasting system. Representation of the atmospheric boundary layer is modified, based on high-resolution large-eddy simulations of the atmospheric boundary. These large-eddy simulations incorporate wake effects from upwind turbines on downwind turbines as well as represent complex atmospheric variability due to complex terrain and surface features as well as atmospheric stability. Real-time hub-height wind speed and other meteorological data streams from existing wind farms are incorporated into the modeling system to enable uncertainty quantification through probabilistic forecasts. A companion investigation has identified optimal boundary-layer physics options for low-level forecasts in complex terrain, toward employing decadal WRF simulations to anticipate large-scale changes in wind resource availability due to global climate change.
Peak Wind Tool for General Forecasting
NASA Technical Reports Server (NTRS)
Barrett, Joe H., III
The expected peak wind speed of the day is an important forecast element in the 45th Weather Squadron's (45 WS) daily 24-Hour and Weekly Planning Forecasts. The forecasts are used for ground and space launch operations at the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45 WS also issues wind advisories for KSC/CCAFS when they expect wind gusts to meet or exceed 25 kt, 35 kt and 50 kt thresholds at any level from the surface to 300 ft. The 45 WS forecasters have indicated peak wind speeds are challenging to forecast, particularly in the cool season months of October - April. In Phase I of this task, the Applied Meteorology Unit (AMU) developed a tool to help the 45 WS forecast non-convective winds at KSC/CCAFS for the 24-hour period of 0800 to 0800 local time. The tool was delivered as a Microsoft Excel graphical user interface (GUI). The GUI displayed the forecast of peak wind speed, 5-minute average wind speed at the time of the peak wind, timing of the peak wind and probability the peak speed would meet or exceed 25 kt, 35 kt and 50 kt. For the current task (Phase II ), the 45 WS requested additional observations be used for the creation of the forecast equations by expanding the period of record (POR). Additional parameters were evaluated as predictors, including wind speeds between 500 ft and 3000 ft, static stability classification, Bulk Richardson Number, mixing depth, vertical wind shear, temperature inversion strength and depth and wind direction. Using a verification data set, the AMU compared the performance of the Phase I and II prediction methods. Just as in Phase I, the tool was delivered as a Microsoft Excel GUI. The 45 WS requested the tool also be available in the Meteorological Interactive Data Display System (MIDDS). The AMU first expanded the POR by two years by adding tower observations, surface observations and CCAFS (XMR) soundings for the cool season months of March 2007 to April 2009. The POR was expanded
Forecasting approaches to the Mekong River
NASA Astrophysics Data System (ADS)
Plate, E. J.
Hydrologists distinguish between flood forecasts, which are concerned with events of the immediate future, and flood predictions, which are concerned with events that are possible, but whose date of occurrence is not determined. Although in principle both involve the determination of runoff from rainfall, the analytical approaches differ because of different objectives. The differences between the two approaches will be discussed, starting with an analysis of the forecasting process. The Mekong River in south-east Asia is used as an example. Prediction is defined as forecast for a hypothetical event, such as the 100-year flood, which is usually sufficiently specified by its magnitude and its probability of occurrence. It forms the basis for designing flood protection structures and risk management activities. The method for determining these quantities is hydrological modeling combined with extreme value statistics, today usually applied both to rainfall events and to observed river discharges. A rainfall-runoff model converts extreme rainfall events into extreme discharges, which at certain gage points along a river are calibrated against observed discharges. The quality of the model output is assessed against the mean value by means of the Nash-Sutcliffe quality criterion. The result of this procedure is a design hydrograph (or a family of design hydrographs) which are used as inputs into a hydraulic model, which converts the hydrograph into design water levels according to the hydraulic situation of the location. The accuracy of making a prediction in this sense is not particularly high: hydrologists know that the 100-year flood is a statistical quantity which can be estimated only within comparatively wide error bounds, and the hydraulics of a river site, in particular under conditions of heavy sediment loads has many uncertainties. Safety margins, such as additional freeboards are arranged to compensate for the uncertainty of the prediction. Forecasts, on the
Skill assessment for an operational algal bloom forecast system
Stumpf, Richard P.; Tomlinson, Michelle C.; Calkins, Julie A.; Kirkpatrick, Barbara; Fisher, Kathleen; Nierenberg, Kate; Currier, Robert; Wynne, Timothy T.
An operational forecast system for harmful algal blooms (HABs) in southwest Florida is analyzed for forecasting skill. The HABs, caused by the toxic dinoflagellate, Karenia brevis, lead to shellfish toxicity and to respiratory irritation. In addition to predicting new blooms and their extent, HAB forecasts are made twice weekly during a bloom event, using a combination of satellite derived image products, wind predictions, and a rule-based model derived from previous observations and research. These forecasts include: identification, intensification, transport, extent, and impact; the latter being the most significant to the public. Identification involves identifying new blooms as HABs and is validated against an operational monitoring program involving water sampling. Intensification forecasts, which are much less frequently made, can only be evaluated with satellite data on mono-specific blooms. Extent and transport forecasts of HABs are also evaluated against the water samples. Due to the resolution of the forecasts and available validation data, skill cannot be resolved at scales finer than 30 km. Initially, respiratory irritation forecasts were analyzed using anecdotal information, the only available data, which had a bias toward major respiratory events leading to a forecast accuracy exceeding 90%. When a systematic program of twice-daily observations from lifeguards was implemented, the forecast could be meaningfully assessed. The results show that the forecasts identify the occurrence of respiratory events at all lifeguard beaches 70% of the time. However, a high rate (80%) of false positive forecasts occurred at any given beach. As the forecasts were made at half to whole county level, the resolution of the validation data was reduced to county level, reducing false positives to 22% (accuracy of 78%). The study indicates the importance of systematic sampling, even when using qualitative descriptors, the use of validation resolution to evaluate forecast
Skill assessment for an operational algal bloom forecast system
NASA Astrophysics Data System (ADS)
Stumpf, Richard P.; Tomlinson, Michelle C.; Calkins, Julie A.; Kirkpatrick, Barbara; Fisher, Kathleen; Nierenberg, Kate; Currier, Robert; Wynne, Timothy T.
An operational forecast system for harmful algal blooms (HABs) in southwest Florida is analyzed for forecasting skill. The HABs, caused by the toxic dinoflagellate, Karenia brevis, lead to shellfish toxicity and to respiratory irritation. In addition to predicting new blooms and their extent, HAB forecasts are made twice weekly during a bloom event, using a combination of satellite derived image products, wind predictions, and a rule-based model derived from previous observations and research. These forecasts include: identification, intensification, transport, extent, and impact; the latter being the most significant to the public. Identification involves identifying new blooms as HABs and is validated against an operational monitoring program involving water sampling. Intensification forecasts, which are much less frequently made, can only be evaluated with satellite data on mono-specific blooms. Extent and transport forecasts of HABs are also evaluated against the water samples. Due to the resolution of the forecasts and available validation data, skill cannot be resolved at scales finer than 30 km. Initially, respiratory irritation forecasts were analyzed using anecdotal information, the only available data, which had a bias toward major respiratory events leading to a forecast accuracy exceeding 90%. When a systematic program of twice-daily observations from lifeguards was implemented, the forecast could be meaningfully assessed. The results show that the forecasts identify the occurrence of respiratory events at all lifeguard beaches 70% of the time. However, a high rate (80%) of false positive forecasts occurred at any given beach. As the forecasts were made at half to whole county level, the resolution of the validation data was reduced to county level, reducing false positives to 22% (accuracy of 78%). The study indicates the importance of systematic sampling, even when using qualitative descriptors, the use of validation resolution to evaluate forecast
The forecaster's added value in QPF
NASA Astrophysics Data System (ADS)
Turco, M.; Milelli, M.
To the authors' knowledge there are relatively few studies that try to answer this question: "Are humans able to add value to computer-generated forecasts and warnings?". Moreover, the answers are not always positive. In particular some postprocessing method is competitive or superior to human forecast. Within the alert system of ARPA Piemonte it is possible to study in an objective manner if the human forecaster is able to add value with respect to computer-generated forecasts. Every day the meteorology group of the Centro Funzionale of Regione Piemonte produces the HQPF (Human Quantitative Precipitation Forecast) in terms of an areal average and maximum value for each of the 13 warning areas, which have been created according to meteo-hydrological criteria. This allows the decision makers to produce an evaluation of the expected effects by comparing these HQPFs with predefined rainfall thresholds. Another important ingredient in this study is the very dense non-GTS (Global Telecommunication System) network of rain gauges available that makes possible a high resolution verification. In this work we compare the performances of the latest three years of QPF derived from the meteorological models COSMO-I7 (the Italian version of the COSMO Model, a mesoscale model developed in the framework of the COSMO Consortium) and IFS (the ECMWF global model) with the HQPF. In this analysis it is possible to introduce the hypothesis test developed by Hamill (1999), in which a confidence interval is calculated with the bootstrap method in order to establish the real difference between the skill scores of two competitive forecasts. It is important to underline that the conclusions refer to the analysis of the Piemonte operational alert system, so they cannot be directly taken as universally true. But we think that some of the main lessons that can be derived from this study could be useful for the meteorological community. In details, the main conclusions are the following
Forecastability as a Design Criterion in Wind Resource Assessment: Preprint
Zhang, J.; Hodge, B. M.
This paper proposes a methodology to include the wind power forecasting ability, or 'forecastability,' of a site as a design criterion in wind resource assessment and wind power plant design stages. The Unrestricted Wind Farm Layout Optimization (UWFLO) methodology is adopted to maximize the capacity factor of a wind power plant. The 1-hour-ahead persistence wind power forecasting method is used to characterize the forecastability of a potential wind power plant, thereby partially quantifying the integration cost. A trade-off between the maximum capacity factor and the forecastability is investigated.
International Cooperative for Aerosol Prediction Workshop on Aerosol Forecast Verification
NASA Technical Reports Server (NTRS)
Benedetti, Angela; Reid, Jeffrey S.; Colarco, Peter R.
The purpose of this workshop was to reinforce the working partnership between centers who are actively involved in global aerosol forecasting, and to discuss issues related to forecast verification. Participants included representatives from operational centers with global aerosol forecasting requirements, a panel of experts on Numerical Weather Prediction and Air Quality forecast verification, data providers, and several observers from the research community. The presentations centered on a review of current NWP and AQ practices with subsequent discussion focused on the challenges in defining appropriate verification measures for the next generation of aerosol forecast systems.
Official forecasts pushed out to a year ahead
Kerr, R.A.
The National Weather Service is about to unveil 15 month forecasting. NWS will not be predicting individual storms in these long-range forecasts, but rather large regions of above, below, or near normal temperature and precipitation. NWS meterologists are adopting three standard techniques for long-range forecasting, two based on objective methods for forecasting El Nino, and one, canonical correlation analysis, an effort to systematize what forecasters already do, looking for its of impending El Nino and other signs of imminent climatic change. 2 figs.
A parimutuel gambling perspective to compare probabilistic seismicity forecasts
NASA Astrophysics Data System (ADS)
Zechar, J. Douglas; Zhuang, Jiancang
Using analogies to gaming, we consider the problem of comparing multiple probabilistic seismicity forecasts. To measure relative model performance, we suggest a parimutuel gambling perspective which addresses shortcomings of other methods such as likelihood ratio, information gain and Molchan diagrams. We describe two variants of the parimutuel approach for a set of forecasts: head-to-head, in which forecasts are compared in pairs, and round table, in which all forecasts are compared simultaneously. For illustration, we compare the 5-yr forecasts of the Regional Earthquake Likelihood Models experiment for M4.95+ seismicity in California.
A neural network short-term forecast of significant thunderstorms
Mccann, D.W. )
Neural networks, an artificial-intelligence tools that excels in pattern recognition, are reviewed, and a 3-7-h significant thunderstorm forecast developed with this technique is discussed. Two neural networks learned to forecast significant thunderstorms from fields of surface-based lifted index and surface moisture convergence. These networks are sensitive to the patterns that skilled forecasters recognize as occurring prior to strong thunderstorms. The two neural networks are combined operationally at the National Severe Storm Forecast Center into a single hourly product that enhances pattern-recognition skills. Examples of neural network products are shown, and their potential impact on significant thunderstorm forecasting is demonstrated. 22 refs.
Multivariate postprocessing techniques for probabilistic hydrological forecasting
NASA Astrophysics Data System (ADS)
Hemri, Stephan; Lisniak, Dmytro; Klein, Bastian
Hydrologic ensemble forecasts driven by atmospheric ensemble prediction systems need statistical postprocessing in order to account for systematic errors in terms of both mean and spread. Runoff is an inherently multivariate process with typical events lasting from hours in case of floods to weeks or even months in case of droughts. This calls for multivariate postprocessing techniques that yield well calibrated forecasts in univariate terms and ensure a realistic temporal dependence structure at the same time. To this end, the univariate ensemble model output statistics (EMOS; Gneiting et al., 2005) postprocessing method is combined with two different copula approaches that ensure multivariate calibration throughout the entire forecast horizon. These approaches comprise ensemble copula coupling (ECC; Schefzik et al., 2013), which preserves the dependence structure of the raw ensemble, and a Gaussian copula approach (GCA; Pinson and Girard, 2012), which estimates the temporal correlations from training observations. Both methods are tested in a case study covering three subcatchments of the river Rhine that represent different sizes and hydrological regimes: the Upper Rhine up to the gauge Maxau, the river Moselle up to the gauge Trier, and the river Lahn up to the gauge Kalkofen. The results indicate that both ECC and GCA are suitable for modelling the temporal dependences of probabilistic hydrologic forecasts (Hemri et al., 2015). References Gneiting, T., A. E. Raftery, A. H. Westveld, and T. Goldman (2005), Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation, Monthly Weather Review, 133(5), 1098-1118, DOI: 10.1175/MWR2904.1. Hemri, S., D. Lisniak, and B. Klein, Multivariate postprocessing techniques for probabilistic hydrological forecasting, Water Resources Research, 51(9), 7436-7451, DOI: 10.1002/2014WR016473. Pinson, P., and R. Girard (2012), Evaluating the quality of scenarios of short-term wind power
A prospective earthquake forecast experiment for Japan
NASA Astrophysics Data System (ADS)
Yokoi, Sayoko; Nanjo, Kazuyoshi; Tsuruoka, Hiroshi; Hirata, Naoshi
One major focus of the current Japanese earthquake prediction research program (2009-2013) is to move toward creating testable earthquake forecast models. For this purpose we started an experiment of forecasting earthquake activity in Japan under the framework of the Collaboratory for the Study of Earthquake Predictability (CSEP) through an international collaboration. We established the CSEP Testing Centre, an infrastructure to encourage researchers to develop testable models for Japan, and to conduct verifiable prospective tests of their model performance. On 1 November in 2009, we started the 1st earthquake forecast testing experiment for the Japan area. We use the unified JMA catalogue compiled by the Japan Meteorological Agency as authorized catalogue. The experiment consists of 12 categories, with 4 testing classes with different time spans (1 day, 3 months, 1 year, and 3 years) and 3 testing regions called All Japan, Mainland, and Kanto. A total of 91 models were submitted to CSEP-Japan, and are evaluated with the CSEP official suite of tests about forecast performance. In this presentation, we show the results of the experiment of the 3-month testing class for 5 rounds. HIST-ETAS7pa, MARFS and RI10K models corresponding to the All Japan, Mainland and Kanto regions showed the best score based on the total log-likelihood. It is also clarified that time dependency of model parameters is no effective factor to pass the CSEP consistency tests for the 3-month testing class in all regions. Especially, spatial distribution in the All Japan region was too difficult to pass consistency test due to multiple events at a bin. Number of target events for a round in the Mainland region tended to be smaller than model's expectation during all rounds, which resulted in rejections of consistency test because of overestimation. In the Kanto region, pass ratios of consistency tests in each model showed more than 80%, which was associated with good balanced forecasting of event
NASA Products to Enhance Energy Utility Load Forecasting
NASA Technical Reports Server (NTRS)
Lough, G.; Zell, E.; Engel-Cox, J.; Fungard, Y.; Jedlovec, G.; Stackhouse, P.; Homer, R.; Biley, S.
Existing energy load forecasting tools rely upon historical load and forecasted weather to predict load within energy company service areas. The shortcomings of load forecasts are often the result of weather forecasts that are not at a fine enough spatial or temporal resolution to capture local-scale weather events. This project aims to improve the performance of load forecasting tools through the integration of high-resolution, weather-related NASA Earth Science Data, such as temperature, relative humidity, and wind speed. Three companies are participating in operational testing one natural gas company, and two electric providers. Operational results comparing load forecasts with and without NASA weather forecasts have been generated since March 2010. We have worked with end users at the three companies to refine selection of weather forecast information and optimize load forecast model performance. The project will conclude in 2012 with transitioning documented improvements from the inclusion of NASA forecasts for sustained use by energy utilities nationwide in a variety of load forecasting tools. In addition, Battelle has consulted with energy companies nationwide to document their information needs for long-term planning, in light of climate change and regulatory impacts.
Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: Forward modeling
NASA Astrophysics Data System (ADS)
Morcrette, J.-J.; Boucher, O.; Jones, L.; Salmond, D.; Bechtold, P.; Beljaars, A.; Benedetti, A.; Bonet, A.; Kaiser, J. W.; Razinger, M.; Schulz, M.; Serrar, S.; Simmons, A. J.; Sofiev, M.; Suttie, M.; Tompkins, A. M.; Untch, A.
This paper presents the aerosol modeling now part of the ECMWF Integrated Forecasting System (IFS). It includes new prognostic variables for the mass of sea salt, dust, organic matter and black carbon, and sulphate aerosols, interactive with both the dynamics and the physics of the model. It details the various parameterizations used in the IFS to account for the presence of tropospheric aerosols. Details are given of the various formulations and data sets for the sources of the different aerosols and of the parameterizations describing their sinks. Comparisons of monthly mean and daily aerosol quantities like optical depths against satellite and surface observations are presented. The capability of the forecast model to simulate aerosol events is illustrated through comparisons of dust plume events. The ECMWF IFS provides a good description of the horizontal distribution and temporal variability of the main aerosol types. The forecast-only model described here generally gives the total aerosol optical depth within 0.12 of the relevant observations and can therefore provide the background trajectory information for the aerosol assimilation system described in part 2 of this paper.
Forecast skill impact of drifting buoys in the Southern Hemisphere
NASA Technical Reports Server (NTRS)
Kalnay, E.; Atlas, R.; Baker, W.; Halem, M.
Two analyses are performed to evaluate the effect of drift buoys and the FGGE's special observing system (SOS) on forecasting. The FGGE analysis utilizes all level II-b conventional and special data, and the Nosat analysis employs only surface and conventional upper air data. Twelve five-day forecasts are produced from these data. An additional experiment utilizing the FGGE data base minus buoys data, and the Nosat data base including buoys data is being conducted. The forecasts are compared and synoptic evaluation of the effect of buoys data is described. The results reveal that the FGGE data base with the SOS significantly improves forecasting in the Southern Hemisphere and the loss of buoys data does not have a great effect on forecasting. The Nosat data has less impact on forecasting; however, the addition of buoys data provides an improvement in forecast skills.
Forecasting flows in Apalachicola River using neural networks
NASA Astrophysics Data System (ADS)
Huang, Wenrui; Xu, Bing; Chan-Hilton, Amy
Forecasting river flow is important to water resources management and planning. In this study, an artificial neural network (ANN) model was successfully developed to forecast river flow in Apalachicola River. The model used a feed-forward, back-propagation network structure with an optimized conjugated training algorithm. Using long-term observations of rainfall and river flow during 1939-2000, the ANN model was satisfactorily trained and verified. Model predictions of river flow match well with the observations. The correlation coefficients between forecasting and observation for daily, monthly, quarterly and yearly flow forecasting are 0.98, 0.95, 0.91 and 0.83, respectively. Results of the forecasted flow rates from the ANN model were compared with those from a traditional autoregressive integrated moving average (ARIMA) forecasting model. Results indicate that the ANN model provides better accuracy in forecasting river flow than does the ARIMA model.
Demand 80/81: Forecasts of energy consumption to the year 2000. Volume 1: Forecasts and description of the forecasting model
NASA Astrophysics Data System (ADS)
Borges, A. M.; Crow, R. T.
National forecasts of end use consumption of electricity, liquid hydrocarbons, gaseous hydrocarbons, and coal are presented. The forecasts are based on an econometric model whose equations represent energy consumption of each form of energy in each end use sector. Each forecast is conditional upon a common forecast of long run economic growth, coupled with a scenario concerning energy prices and conservation policy. The scenarios are composed of four alternative sets of assumptions about energy prices and three alternative sets of assumptions on conservation policy.
Improvements of seasonal weather forecasts using optimal combination of multimodel hydrodynamical forecasts
NASA Astrophysics Data System (ADS)
Khan, V.; Tischenko, V.; Kryjov, V.; Vilfand, R.
The main objective of the present study is to improve seasonal weather forecasting applying statistical analysis to hydrodynamical model outputs from Russian and foreign GCM models. Quantitative estimates of the ability of the models to reproduce the temporal and spatial variability of the meteorological fields were obtained. Reasonable skill scores of forecasts have been observed over tropical zones, while the forecast assessments were low over North Eurasian region. Although performance of basic methods of complexation demonstrated advantage of the multimodel forecast over individual forecasts constituting the ensemble, the prognostic ability of complexated forecast is still not enough high in high latitudes regions. In attempt to increase the predictability, a new statistical approach based on "predictant-predictors" system was elaborated. H-500 data from model set were used as predictors, and T850 - as a predictant. Correlation analysis between the local Т850 and the global H-500 from different models was appplyed to identify informative geographical regions of H-500 for each model. Compact representation of the H-500 predictor data was done using EOF analysis. Two best-predictor models from extended predictor dataset were identified at concrete prognostic season after applying stepwise multiple regression procedure. Evaluation of the statistical approach on dependent and cross-valiadated datasets demonstrates high skill score for dependent and cross-validated datasets. However the method has some deficiencies related with instability of found equations and needs more test experiments. Preliminary results of this study figure out that adaptive statistical methods for optimal complexation of hydrodynamical models can be useful tool to improve long-range forecasts. This work is partially supported by RFBR grant N 07-05-00740, 07-05-00240.
Operational earthquake forecasting can enhance earthquake preparedness
Jordan, T.H.; Marzocchi, W.; Michael, A.J.; Gerstenberger, M.C.
We cannot yet predict large earthquakes in the short term with much reliability and skill, but the strong clustering exhibited in seismic sequences tells us that earthquake probabilities are not constant in time; they generally rise and fall over periods of days to years in correlation with nearby seismic activity. Operational earthquake forecasting (OEF) is the dissemination of authoritative information about these time‐dependent probabilities to help communities prepare for potentially destructive earthquakes. The goal of OEF is to inform the decisions that people and organizations must continually make to mitigate seismic risk and prepare for potentially destructive earthquakes on time scales from days to decades. To fulfill this role, OEF must provide a complete description of the seismic hazard—ground‐motion exceedance probabilities as well as short‐term rupture probabilities—in concert with the long‐term forecasts of probabilistic seismic‐hazard analysis (PSHA).
Sentinels of the Sun: Forecasting Space Weather
NASA Astrophysics Data System (ADS)
Poland, Arthur I.
The story of humanity's interest in space weather may go back to prehistoric times when people at high latitudes noticed the northern lights. Interest became more acute after the development of electrical technologies such as the telegraph, and certainly during World War II when shortwave radio communication came into practical use. Solar observing actually began to be supported by the military, with the observatory at Climax, Colorado being established to monitor the Sun during the war. With the advent of satellites and manned space travel to the Moon, space weather became a seriously funded endeavor both for basic research and forecasting. In the book, Sentinels of the Sun: Forecasting Space Weather, Barbara Poppe does an excellent job of telling this story for the nonprofessional. Moreover, as a professional who has studied space weather since before humans landed on the Moon, I found the book to be a very enjoyable read.
Weather forecasting support for AASE-2
NASA Technical Reports Server (NTRS)
Forbes, Gregory S.
The AFEAS Contract and NASA Grant were awarded to Penn State in order to obtain real-time weather forecasting support for the NASA AASE-II Project, which was conducted between October 1991 and March 1992. Because of the special weather sensitivities of the NASA ER-2 aircraft, AASE-II planners felt that public weather forecasts issued by the National Weather Service would not be adequate for mission planning purposes. A likely consequence of resorting to that medium would have been that scientists would have had to be at work by 4 AM day after day in the hope that the aircraft could fly, only to be frustrated by a great number of 'scrubbed' missions. Thus, the Pennsylvania State University was contracted to provide real-time weather support to the AASE-II mission.
Probabilistic forecasts based on radar rainfall uncertainty
NASA Astrophysics Data System (ADS)
Liguori, S.; Rico-Ramirez, M. A.
The potential advantages resulting from integrating weather radar rainfall estimates in hydro-meteorological forecasting systems is limited by the inherent uncertainty affecting radar rainfall measurements, which is due to various sources of error [1-3]. The improvement of quality control and correction techniques is recognized to play a role for the future improvement of radar-based flow predictions. However, the knowledge of the uncertainty affecting radar rainfall data can also be effectively used to build a hydro-meteorological forecasting system in a probabilistic framework. This work discusses the results of the implementation of a novel probabilistic forecasting system developed to improve ensemble predictions over a small urban area located in the North of England. An ensemble of radar rainfall fields can be determined as the sum of a deterministic component and a perturbation field, the latter being informed by the knowledge of the spatial-temporal characteristics of the radar error assessed with reference to rain-gauges measurements. This approach is similar to the REAL system [4] developed for use in the Southern-Alps. The radar uncertainty estimate can then be propagated with a nowcasting model, used to extrapolate an ensemble of radar rainfall forecasts, which can ultimately drive hydrological ensemble predictions. A radar ensemble generator has been calibrated using radar rainfall data made available from the UK Met Office after applying post-processing and corrections algorithms [5-6]. One hour rainfall accumulations from 235 rain gauges recorded for the year 2007 have provided the reference to determine the radar error. Statistics describing the spatial characteristics of the error (i.e. mean and covariance) have been computed off-line at gauges location, along with the parameters describing the error temporal correlation. A system has then been set up to impose the space-time error properties to stochastic perturbations, generated in real-time at
Forecasting urban highway travel for year 2005
Miaou, Shaw-Pin . Transportation Center); Rathi, A.K.; Southworth, F.; Greene, D.L. )
As part of a study aimed at estimating suburban highway needs for year 2005, models were developed for forecasting daily vehicle miles of travel (DVMT) for urban areas and its distribution by highway functional class, urban location, and urban area size. A regression model combining both time series and cross-sectional data is used to establish the relationship between the per capita DVMT of 339 urban areas in the United States and a set of explanatory variables including real income, employment, number of persons per household, number of driver licenses per 1000 persons, a variable representing highway supply deficiency, and a time variable. The dynamic shift over time in share of travel between urban locations and highway functional classes as urban areas grow in size is represented by conditional logit models. This paper presents the major findings from the forecasting and distribution models for urban highway travel in year 2005. 30 refs., 3 figs., 9 tabs.
Forecasting residual herbicide concentrations in soil
NASA Astrophysics Data System (ADS)
McGrath, Gavan; Scanlan, Craig; van Zwieten, Lukas; Rose, Mick; Rose, Terry
High concentrations of herbicides remaining in soil at the time of planting can adversely impact agricultural production and lead to off-site impacts in streams and groundwater. Being able to forecast the likelihood of residual concentrations at specific times in the future offers the potential to improve environmental and economic outcomes. Here we develop a solution for the full transient probability density function for herbicide concentrations in soil as a function of rainfall variability. Quasi-analytical solutions that account for rainfall seasonality are also demonstrated. In addition, new rapid and relatively cost-effective bioassays to quantify herbicide concentrations in near real-time, offers opportunities for data assimilation approaches to improve forecast risks.
A monthly forecast experiment: Preliminary report
NASA Technical Reports Server (NTRS)
Miyakoda, K.; Sirutis, J.; Ploshay, J. J.
An experiment on monthly forecasts with eight winter cases is being carried out, using a 1980 general circulation model (GCM), which incorporates a set of dubgrid-scale physics characterized by the Mellon-Yamada turbulence closure (hierarchy level 2.5), the Monin-Obukhov parameterization for the layer next to the ground surface, Manabe's cumulus parameterization, and the soil heat conduction. The sample cases adopted are for the month of January in the years from 1977 to 1983, which include the extraordinarily severe winter of 1977 and the most pronounced E1 Nino year of 1983. Each case is predicted by prescribing the climatologically normal sea surface temperature as the lower boundary conditions and by using an ensemble means of three individual integrations. These integrations start with three different initial conditions based on the Level data generated separately at the Geophysical Fluid Dynamics Laboratory, the National Meteorological Center, and the European Centre for Medium Range Weather Forecasts.
The forecaster's added value in QPF
NASA Astrophysics Data System (ADS)
Turco, M.; Milelli, M.
To the authors' knowledge there are relatively few studies that try to answer this topic: "Are humans able to add value to computer-generated forecasts and warnings ?". Moreover, the answers are not always positive. In particular some postprocessing method is competitive or superior to human forecast (see for instance Baars et al., 2005, Charba et al., 2002, Doswell C., 2003, Roebber et al., 1996, Sanders F., 1986). Within the alert system of ARPA Piemonte it is possible to study in an objective manner if the human forecaster is able to add value with respect to computer-generated forecasts. Every day the meteorology group of the Centro Funzionale of Regione Piemonte produces the HQPF (Human QPF) in terms of an areal average for each of the 13 regional warning areas, which have been created according to meteo-hydrological criteria. This allows the decision makers to produce an evaluation of the expected effects by comparing these HQPFs with predefined rainfall thresholds. Another important ingredient in this study is the very dense non-GTS network of rain gauges available that makes possible a high resolution verification. In this context the most useful verification approach is the measure of the QPF and HQPF skills by first converting precipitation expressed as continuous amounts into ''exceedance'' categories (yes-no statements indicating whether precipitation equals or exceeds selected thresholds) and then computing the performances for each threshold. In particular in this work we compare the performances of the latest three years of QPF derived from two meteorological models COSMO-I7 (the Italian version of the COSMO Model, a mesoscale model developed in the framework of the COSMO Consortium) and IFS (the ECMWF global model) with the HQPF. In this analysis it is possible to introduce the hypothesis test developed by Hamill (1999), in which a confidence interval is calculated with the bootstrap method in order to establish the real difference between the
A hydrometeorological approach for probabilistic flood forecast
NASA Astrophysics Data System (ADS)
Siccardi, F.; Boni, G.; Ferraris, L.; Rudari, R.
We propose a new methodology for evaluating predictive cumulative distribution functions (CDF) of ground effects for flood forecasting in mountainous environments. The methodology is based on the proper nesting of models suitable for probabilistic meteorological forecast, downscaling of rainfall, and hydrological modeling in order to provide a probabilistic prediction of ground effects of heavy rainfall events. Different ways of nesting are defined as function of the ratio between three typical scales: scales at which rainfall processes are satisfactory represented by meteorological models, scales of the hydrological processes, and scales of the social response. Two different examples of the application of the methodology for different hydrological scales are presented. Predictive CDFs are evaluated, and the motivations that lead to a different paths for CDFs derivation are highlighted.
Biological Invasions: A Challenge In Ecological Forecasting
NASA Technical Reports Server (NTRS)
Schnase, J. L.; Smith, J. A.; Stohlgren, T. J.; Graves, S.; Trees, C.; Rood, Richard (Technical Monitor)
The spread of invasive species is one of the most daunting environmental, economic, and human-health problems facing the United States and the World today. It is one of several grand challenge environmental problems being considered by NASA's Earth Science Vision for 2025. The invasive species problem is complex and presents many challenges. Developing an invasive species predictive capability could significantly advance the science and technology of ecological forecasting.
Machine Learning of Maritime Fog Forecast Rules.
NASA Astrophysics Data System (ADS)
Tag, Paul M.; Peak, James E.
In recent years, the field of artificial intelligence has contributed significantly to the science of meteorology, most notably in the now familiar form of expert systems. Expert systems have focused on rules or heuristics by establishing, in computer code, the reasoning process of a weather forecaster predicting, for example, thunderstorms or fog. In addition to the years of effort that goes into developing such a knowledge base is the time-consuming task of extracting such knowledge and experience from experts. In this paper, the induction of rules directly from meteorological data is explored-a process called machine learning. A commercial machine learning program called C4.5, is applied to a meteorological problem, forecasting maritime fog, for which a reliable expert system has been previously developed. Two detasets are used: 1) weather ship observations originally used for testing and evaluating the expert system, and 2) buoy measurements taken off the coast of California. For both datasets, the rules produced by C4.5 are reasonable and make physical sense, thus demonstrating that an objective induction approach can reveal physical processes directly from data. For the ship database, the machine-generated rules are not as accurate as those from the expert system but are still significantly better than persistence forecasts. For the buoy data, the forecast accuracies are very high, but only slightly superior to persistence. The results indicate that the machine learning approach is a viable tool for developing meteorological expertise, but only when applied to reliable data with sufficient cases of known outcome. In those instances when such databases are available, the use of machine learning can provide useful insight that otherwise might take considerable human analysis to produce.
Forecasting Zakat collection using artificial neural network
NASA Astrophysics Data System (ADS)
Sy Ahmad Ubaidillah, Sh. Hafizah; Sallehuddin, Roselina
'Zakat', "that which purifies" or "alms", is the giving of a fixed portion of one's wealth to charity, generally to the poor and needy. It is one of the five pillars of Islam, and must be paid by all practicing Muslims who have the financial means (nisab). 'Nisab' is the minimum level to determine whether there is a 'zakat' to be paid on the assets. Today, in most Muslim countries, 'zakat' is collected through a decentralized and voluntary system. Under this voluntary system, 'zakat' committees are established, which are tasked with the collection and distribution of 'zakat' funds. 'Zakat' promotes a more equitable redistribution of wealth, and fosters a sense of solidarity amongst members of the 'Ummah'. The Malaysian government has established a 'zakat' center at every state to facilitate the management of 'zakat'. The center has to have a good 'zakat' management system to effectively execute its functions especially in the collection and distribution of 'zakat'. Therefore, a good forecasting model is needed. The purpose of this study is to develop a forecasting model for Pusat Zakat Pahang (PZP) to predict the total amount of collection from 'zakat' of assets more precisely. In this study, two different Artificial Neural Network (ANN) models using two different learning algorithms are developed; Back Propagation (BP) and Levenberg-Marquardt (LM). Both models are developed and compared in terms of their accuracy performance. The best model is determined based on the lowest mean square error and the highest correlations values. Based on the results obtained from the study, BP neural network is recommended as the forecasting model to forecast the collection from 'zakat' of assets for PZP.
Neural net forecasting for geomagnetic activity
NASA Technical Reports Server (NTRS)
Hernandez, J. V.; Tajima, T.; Horton, W.
We use neural nets to construct nonlinear models to forecast the AL index given solar wind and interplanetary magnetic field (IMF) data. We follow two approaches: (1) the state space reconstruction approach, which is a nonlinear generalization of autoregressive-moving average models (ARMA) and (2) the nonlinear filter approach, which reduces to a moving average model (MA) in the linear limit. The database used here is that of Bargatze et al. (1985).
Mission Requirements and Data Systems Support Forecast
NASA Technical Reports Server (NTRS)
This document was developed by the Flight Mission Support Office and prepared by the Forecast Analysis Section of the Bendix Field Engineering Corporation (BFEC) to provide NASA management with detailed mission information. It is one of a number of sources used in planning Mission Operations and Data Systems resource commitments in support of mission requirements. All mission dates are based on information available as of May 28, 1993.
Using HPC within an operational forecasting configuration
NASA Astrophysics Data System (ADS)
Jagers, H. R. A.; Genseberger, M.; van den Broek, M. A. F. H.
Various natural disasters are caused by high-intensity events, for example: extreme rainfall can in a short time cause major damage in river catchments, storms can cause havoc in coastal areas. To assist emergency response teams in operational decisions, it's important to have reliable information and predictions as soon as possible. This starts before the event by providing early warnings about imminent risks and estimated probabilities of possible scenarios. In the context of various applications worldwide, Deltares has developed an open and highly configurable forecasting and early warning system: Delft-FEWS. Finding the right balance between simulation time (and hence prediction lead time) and simulation accuracy and detail is challenging. Model resolution may be crucial to capture certain critical physical processes. Uncertainty in forcing conditions may require running large ensembles of models; data assimilation techniques may require additional ensembles and repeated simulations. The computational demand is steadily increasing and data streams become bigger. Using HPC resources is a logical step; in different settings Delft-FEWS has been configured to take advantage of distributed computational resources available to improve and accelerate the forecasting process (e.g. Montanari et al, 2006). We will illustrate the system by means of a couple of practical applications including the real-time dynamic forecasting of wind driven waves, flow of water, and wave overtopping at dikes of Lake IJssel and neighboring lakes in the center of The Netherlands. Montanari et al., 2006. Development of an ensemble flood forecasting system for the Po river basin, First MAP D-PHASE Scientific Meeting, 6-8 November 2006, Vienna, Austria.
The potential uses of operational earthquake forecasting
Field, Ned; Jordan, Thomas; Jones, Lucille; Michael, Andrew; Blanpied, Michael L.
This article reports on a workshop held to explore the potential uses of operational earthquake forecasting (OEF). We discuss the current status of OEF in the United States and elsewhere, the types of products that could be generated, the various potential users and uses of OEF, and the need for carefully crafted communication protocols. Although operationalization challenges remain, there was clear consensus among the stakeholders at the workshop that OEF could be useful.
Reapplication of Traditional Hydrological Forecasting Methods
NASA Astrophysics Data System (ADS)
Kontur, I.; Keve, G.
At the end of the last Century Jozsef Pech, who was the head of the Hydrological Fore- casting Department in Hungary, developed graphical methods for hydrological fore- casting. These methods made possible to solve non- linear forecasting problems. To involve non-linearity into the models handmade drawings were applied. Basic ideas of these methods are still useful nowadays. Computers make easier the enormous graph- ical work that had to be carried out a century ago. In our investigation all the graphs, nomograms and equations of Pech were put into a computer, after adjusting them to the present hydrological boundary conditions. Routing of floods in time and space are shown on 3D maps. Connected water-level data from upstream and downstream gauges along with the propagation times are also displayed as surfaces. These graphs help to analyse flood events. Based on these analyses, computerised forecasting tools were made for the practical use of the models. The so-updated model has been tested on the Tisza, a river having countless of tributaries. In the last two years three extreme flood events have been experienced along this river, which have turned the attention towards the application of accurate flood forecasting methods. As Pech developed his model exactly for the Tisza its reapplication is very actual issue. As 1D hydraulic models are also being developed for this river (that also enable flood forecasting) it will be possible to compare the accuracy of the different methods. It may happen that methods developed by our forefathers will prove to be applicable in our times too.
Forecasting hotspots using predictive visual analytics approach
Maciejewski, Ross; Hafen, Ryan; Rudolph, Stephen; Cleveland, William; Ebert, David
A method for forecasting hotspots is provided. The method may include the steps of receiving input data at an input of the computational device, generating a temporal prediction based on the input data, generating a geospatial prediction based on the input data, and generating output data based on the time series and geospatial predictions. The output data may be configured to display at least one user interface at an output of the computational device.
The Discriminant Analysis Flare Forecasting System (DAFFS)
NASA Astrophysics Data System (ADS)
Leka, K. D.; Barnes, Graham; Wagner, Eric; Hill, Frank; Marble, Andrew R.
The Discriminant Analysis Flare Forecasting System (DAFFS) has been developed under NOAA/Small Business Innovative Research funds to quantitatively improve upon the NOAA/SWPC flare prediction. In the Phase-I of this project, it was demonstrated that DAFFS could indeed improve by the requested 25% most of the standard flare prediction data products from NOAA/SWPC. In the Phase-II of this project, a prototype has been developed and is presently running autonomously at NWRA.DAFFS uses near-real-time data from NOAA/GOES, SDO/HMI, and the NSO/GONG network to issue both region- and full-disk forecasts of solar flares, based on multi-variable non-parametric Discriminant Analysis. Presently, DAFFS provides forecasts which match those provided by NOAA/SWPC in terms of thresholds and validity periods (including 1-, 2-, and 3- day forecasts), although issued twice daily. Of particular note regarding DAFFS capabilities are the redundant system design, automatically-generated validation statistics and the large range of customizable options available. As part of this poster, a description of the data used, algorithm, performance and customizable options will be presented, as well as a demonstration of the DAFFS prototype.DAFFS development at NWRA is supported by NOAA/SBIR contracts WC-133R-13-CN-0079 and WC-133R-14-CN-0103, with additional support from NASA contract NNH12CG10C, plus acknowledgment to the SDO/HMI and NSO/GONG facilities and NOAA/SWPC personnel for data products, support, and feedback. DAFFS is presently ready for Phase-III development.
Forecast Variance Estimates Using Dart Inversion
NASA Astrophysics Data System (ADS)
Gica, E.
The tsunami forecast tool developed by the NOAA Center for Tsunami Research (NCTR) provides real-time tsunami forecast and is composed of the following major components: a pre-computed tsunami propagation database, an inversion algorithm that utilizes real-time tsunami data recorded at DART stations to define the tsunami source, and inundation models that predict tsunami wave characteristics at specific coastal locations. The propagation database is a collection of basin-wide tsunami model runs generated from 50x100 km "unit sources" with a slip of 1 meter. Linear combination and scaling of unit sources is possible since the nonlinearity in the deep ocean is negligible. To define the tsunami source using the unit sources, real-time DART data is ingested into an inversion algorithm. Based on the selected DART and length of tsunami time series, the inversion algorithm will select the best combination of unit sources and scaling factors that best fit the observed data at the selected locations. This combined source then serves as boundary condition for the inundation models. Different combinations of DARTs and length of tsunami time series used in the inversion algorithm will result in different selection of unit sources and scaling factors. Since the combined unit sources are used as boundary condition for inundation modeling, different sources will produce variations in the tsunami wave characteristics. As part of the testing procedures for the tsunami forecast tool, staff at NCTR and both National and Pacific Tsunami Warning Centers, performed post-event forecasts for several historical tsunamis. The extent of variation due to different source definitions obtained from the testing is analyzed by comparing the simulated maximum tsunami wave amplitude with recorded data at tide gauge locations. Results of the analysis will provide an error estimate defining the possible range of the simulated maximum tsunami wave amplitude for each specific inundation model.
Global uncertainty assessment in hydrological forecasting by means of statistical analysis of forecast errors
NASA Astrophysics Data System (ADS)
Montanari, A.; Grossi, G.
It is well known that uncertainty assessment in hydrological forecasting is a topical issue. Already in 1905 W.E. Cooke, who was issuing daily weather forecasts in Australia, stated: "It seems to me that the condition of confidence or otherwise form a very important part of the prediction, and ought to find expression". Uncertainty assessment in hydrology involves the analysis of multiple sources of error. The contribution of these latter to the formation of the global uncertainty cannot be quantified independently, unless (a) one is willing to introduce subjective assumptions about the nature of the individual error components or (2) independent observations are available for estimating input error, model error, parameter error and state error. An alternative approach, that is applied in this study and still requires the introduction of some assumptions, is to quantify the global hydrological uncertainty in an integrated way, without attempting to quantify each independent contribution. This methodology can be applied in situations characterized by limited data availability and therefore is gaining increasing attention by end users. This work aims to propose a statistically based approach for assessing the global uncertainty in hydrological forecasting, by building a statistical model for the forecast error xt,d, where t is the forecast time and d is the lead time. Accordingly, the probability distribution of xt,d is inferred through a non linear multiple regression, depending on an arbitrary number of selected conditioning variables. These include the current forecast issued by the hydrological model, the past forecast error and internal state variables of the model. The final goal is to indirectly relate the forecast error to the sources of uncertainty, through a probabilistic link with the conditioning variables. Any statistical model is based on assumptions whose fulfilment is to be checked in order to assure the validity of the underlying theory. Statistical
Forecasting droughts in West Africa: Operational practice and refined seasonal precipitation forecasts
NASA Astrophysics Data System (ADS)
Bliefernicht, Jan; Siegmund, Jonatan; Seidel, Jochen; Arnold, Hanna; Waongo, Moussa; Laux, Patrick; Kunstmann, Harald
Precipitation forecasts for the upcoming rainy seasons are one of the most important sources of information for an early warning of droughts and water scarcity in West Africa. The meteorological services in West Africa perform seasonal precipitation forecasts within the framework of PRESAO (the West African climate outlook forum) since the end of the 1990s. Various sources of information and statistical techniques are used by the individual services to provide a harmonized seasonal precipitation forecasts for decision makers in West Africa. In this study, we present a detailed overview of the operational practice in West Africa including a first statistical assessment of the performance of the precipitation forecasts for drought situations for the past 18 years (1998 to 2015). In addition, a long-term hindcasts (1982 to 2009) and a semi-operational experiment for the rainy season 2013 using statistical and/or dynamical downscaling are performed to refine the precipitation forecasts from the Climate Forecast System Version 2 (CFSv2), a global ensemble prediction system. This information is post-processed to provide user-oriented precipitation indices such as the onset of the rainy season for supporting water and land use management for rain-fed agriculture. The evaluation of the individual techniques is performed focusing on water-scarce regions of the Volta basin in Burkina Faso and Ghana. The forecasts of the individual techniques are compared to state-of-the-art global observed precipitation products and a novel precipitation database based on long-term daily rain-gage measurements provided by the national meteorological services. The statistical assessment of the PRESAO forecasts indicates skillful seasonal precipitation forecasts for many locations in the Volta basin, particularly for years with water deficits. The operational experiment for the rainy season 2013 illustrates the high potential of a physically-based downscaling for this region but still shows
Forecasting Social Unrest Using Activity Cascades.
Cadena, Jose; Korkmaz, Gizem; Kuhlman, Chris J; Marathe, Achla; Ramakrishnan, Naren; Vullikanti, Anil
Social unrest is endemic in many societies, and recent news has drawn attention to happenings in Latin America, the Middle East, and Eastern Europe. Civilian populations mobilize, sometimes spontaneously and sometimes in an organized manner, to raise awareness of key issues or to demand changes in governing or other organizational structures. It is of key interest to social scientists and policy makers to forecast civil unrest using indicators observed on media such as Twitter, news, and blogs. We present an event forecasting model using a notion of activity cascades in Twitter (proposed by Gonzalez-Bailon et al., 2011) to predict the occurrence of protests in three countries of Latin America: Brazil, Mexico, and Venezuela. The basic assumption is that the emergence of a suitably detected activity cascade is a precursor or a surrogate to a real protest event that will happen "on the ground." Our model supports the theoretical characterization of large cascades using spectral properties and uses properties of detected cascades to forecast events. Experimental results on many datasets, including the recent June 2013 protests in Brazil, demonstrate the effectiveness of our approach. PMID:26091012