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A regionally enhanced global (REG) data assimilation (DA) method is proposed. The technique blends high-resolution model information from a single or multiple limited-area model domains with global model and observational information to create a regionally enhanced analysis of the global atmospheric state. This single analysis provides initial conditions for both the global and limited-area model forecasts. The potential benefits of the approach for operational data assimilation are (i) reduced development cost, (ii) reduced overall computational cost, (iii) improved limited-area forecast performance from the use of global information about the atmospheric flow, and (iv) improved global forecast performance from the use of more accurate model information in the limited-area domains. The method is tested by an implementation on the U.S. Navy's four-dimensional variational global data assimilation system and global and limited-area numerical weather prediction models. The results of the monthlong forecast experiments suggest that the REG DA approach has the potential to deliver the desired benefits.
1. Introduction
Most major numerical weather prediction centers of the world produce both global and limited-area model (LAM) forecasts. These centers typically maintain both a global and a limited-area data assimilation (DA) system, and some of them prepare limited-area analyses and forecasts for multiple limited-area domains. For instance, the Fleet Numerical Meteorology and Oceanography Center (FNMOC) of the U.S. Navy prepares limited-area weather analyses and forecasts for more than 80 domains, covering a large fraction of the Northern Hemisphere (NH). At these centers, both the development and computational cost of data assimilation could potentially be reduced by preparing the global and limited-area analyses by a single, unified data assimilation system. The savings in development and computational time could be used for the enhancement of the unified data assimilation system.
Streamlining development and production is not the only motivation to search for a unified global–limited-area data assimilation approach, as it is not unreasonable to assume that the quality of both the global and LAM initial conditions may benefit from such an approach. In particular, LAM initial conditions can potentially benefit from the use of global model and observational information, because it can aid in the proper interpretation of the contribution of the large-scale component of the atmospheric flow in the limited-area domains. The detrimental effects of the lack of complete information about the large-scale flow on the analyses produced by limited-area data assimilation have been long recognized in operational numerical weather prediction. Some forecast centers, for instance, use an approach called partial cycling of the limited-area data assimilation process: the cycling of the limited-area data assimilation always falls back on a recent global analysis to carry out only a few cycles of limited-area data assimilation (Rogers et al. 2009). Another approach is to modify the cost function minimized by the limited-area data assimilation scheme such that it penalizes departures from the host global analysis that provides the lateral boundary conditions (Guidard and Fischer 2008; Dahlgren and Gustafsson 2012). The global initial conditions may also benefit from the LAM information, because it provides not only an additional estimate of the atmospheric state, but also a higher resolution and presumably more accurate information about the effects about the boundary conditions at the surface (e.g., orography, coastlines, land coverage) and the atmospheric processes (e.g., nonlinear interactions at the smaller scales, cloud physics).
Previous studies with highly idealized Lorenz models of the atmospheric dynamics (Yoon et al. 2012; Kretschmer et al. 2015) demonstrated the potential for improvement of both the global and limited-area forecasts from a unified data assimilation approach. These idealized studies were motivated by the mixed results of an earlier attempt (Merkova et al. 2011) to incorporate higher-resolution limited-area model information into the global data assimilation process. That study used older, reduced-resolution versions of the operational models of the National Centers for Environmental Prediction (NCEP) and the research data assimilation system of Szunyogh et al. (2008) and led to the conclusion that further research was necessary to refine the technical approach. Yoon et al. (2012) explored a joint-state approach that prepared an analysis of a joint state vector composed of all state vector components of the global and limited-area models. One unexpected difficulty with the implementation of that approach was that in some situations, relatively large differences developed between the global and limited-area analyses in the limited-area region. Fixing this problem required the introduction of an extra term in the cost function of the data assimilation scheme to penalize differences between the global and limited-area analyses in the limited-area regions. While the addition of the penalty term had the desired effect, it made the implementation of the technique more complicated. Kretschmer et al. (2015) found that similarly good results could be achieved with a simpler composite-state method: they estimated the state of a composite state vector defined by a linear combination of the global state vector and the limited-area state vectors in the limited-area domains and by the components of the global state vector elsewhere. The components of the global initial conditions in the limited-area domains were obtained by interpolation from the components of the composite state vector. The composite-state method was not only easier to implement than the joint-state approach, but its computational cost was also lower, especially for overlapping limited-area domains, because it estimated a state vector of fewer components.
The present study is the first attempt since Merkova et al. (2011) to test the concept of unified global-limited-area data assimilation on operational models. The technical approach used here is similar, but not completely identical, to that of Kretschmer et al. (2015). To be specific, the equivalent of the composite state vector, called the regionally enhanced global (REG) state vector, is used only to predict the observations in the computation of the innovations, the differences between the observations and their predicted values. This information is then used to obtain the model initial conditions by updating a global background state estimate rather than a background estimate of the REG state vector.
We test our approach by an implementation on the U.S. Navy's operational global and limited-area models and global data assimilation system. The global model is the Navy Global Environment Model (NAVGEM; Hogan et al. 2014), a spectral transform model operationally run at resolution T425. The regional model is the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS; Hodur 1997), which we use with no ocean–atmosphere coupling. COAMPS is implemented operationally on the more than 80 limited-area domains over the world with a varying horizontal resolution from 25 km to 500 m. COAMPS uses partial cycling and a 3D-Var data assimilation scheme (Daley and Barker 2001) to obtain limited-area initial conditions, and the version used for this study does not assimilate satellite radiance observations. The global data assimilation system of NAVGEM, the Naval Research Laboratory Atmospheric Variational Data Assimilation System-Accelerated Representer (NAVDAS-AR), employs a 4D-Var data assimilation scheme (Xu et al. 2005; Rosmond and Xu 2006). A future implementation of REG DA on NAVDAS-AR, a REG 4D-Var, would allow the Navy to unify the DA systems of NAVGEM and COAMPS. Doing so would enable COAMPS initial conditions to be informed by a larger number and variety of satellite observations and would also reduce the overall computation cost of DA by eliminating it for the many individual limited-area domains.
The paper is organized as follows. Section 2 introduces the general concept of REG DA; section 3 outlines the specific implementation on the Navy's data assimilation system and models; section 4 describes the design of the experiments to evaluate the global and limited-area forecast performance; section 5 presents the results of the experiments; and section 6 offers conclusions and outlines future work.
2. The REG state vector
We expect a more accurate computation of the predicted values of the observations in data assimilation to lead to more accurate analyses. Our hypothesis is that injecting limited-area forecast information into the computation of the predicted values of the observations is an efficient approach to improve their accuracy.
a. Definition
We assume that in addition to a global model, we also have access to a limited-area model that can add resolution and accuracy to the global model forecasts in one or more limited-area domains. In our implementation of REG DA on the Navy's forecast system, which we will describe in section 3, the global and limited-area model states are blended on a high-resolution global grid, and the limited-area domains cover only part of the NH. To simplify notation, in this section, we assume that the limited-area model information is available for the entire globe, and the global and limited-area model state are blended on the grid of the limited-area model.
We define the REG state vector by
where is the interpolation function that maps the discrete global model representation of the atmospheric state into its limited-area model representation , is the high-resolution state vector of a limited-area model forecast, and is a (possibly location dependent) blending coefficient.
b. Implementation
Let be a background estimate of the global state and the background estimate of the limited-area state. In modern data assimilation, is a short-term global model forecast, while is a short-term limited-area forecast. Both state vectors are considered random vector variables, with the source of the randomness being the forecast errors. The REG background state vector,
is a more accurate estimate of the (true) atmospheric state than either or , because is a weighted average of the two that filters random errors. If there were no systematic differences between the accuracy of and (differences between the means of their errors), the value of α that would minimize the variance of the error of would be determined by the variance of the errors of and . But we expect to provide systematically more accurate information about the atmospheric flow near the surface because of the more detailed representation of the air–surface interactions and atmospheric physical processes in the limited-area model. Hence, we expect to have smaller systematic errors than near the surface.
Let and be the observation functions operating on and , respectively. Under the assumption that has a smaller systematic error than , the vector of innovations,
more accurately satisfies the assumption of data assimilation that nonzero innovations are the results of random errors in the background and the observations than if it was computed by . This motivates using rather than for the computation of the (vector of) innovations.
In the present implementation of REG DA, we use the global data assimilation system to compute the analysis increment from the innovations, which we then add to the global background to obtain a global analysis :
We generate the limited-area analysis from the global analysis by interpolation. An alternative approach would be to obtain an analysis
of the REG state vector and generate both the global analysis and the limited-area analysis by interpolation from . We chose the former strategy to implement first, because it is easier to maintain the proper atmospheric balance between the wind and the mass field in the global analysis. As we find the second strategy an intriguing alternative option, at the time of writing, we are working on its implementation.
For , our approach is equivalent to preparing the global analysis the conventional way and obtaining the limited-area analysis by interpolation from the global analysis. If , however, is different from a conventional global analysis. Most importantly, we expect it to make a smaller correction for systematic errors in . This is a desirable behavior in situations where the lower resolution of the global model leads to the type of (possibly flow dependent) systematic error in (e.g., Baek et al. 2006, 2009; Danforth et al. 2007) that can lead to a strong model "spinup" if corrected by the data assimilation. In such situations, could also be a more accurate analysis of the atmospheric state than if it were obtained from a conventional global analysis.
While REG DA can be implemented on any type of DA scheme (e.g., EnKF or 4D-Var), the approximations that have to be made in a practical implementation of the approach depend on the type of the DA scheme. For a discussion of the roots of this dependence, we first recall that the observation function plays a dual role in data assimilation: it is used for the computation of both the innovations and the forecast error covariances in observation space. While the type of the DA scheme has no effect on the computation of the innovations, there is a fundamental difference between the ways EnKF and 4D-Var estimate the forecast error covariances. We expect REG DA to have a positive impact on the analyses and ensuing forecasts primarily because of the more accurate computation of the innovations rather than the more accurate prediction of the error covariances. As the focus of the present paper is on 4D-Var, we only note that an implementation of REG DA on an EnKF would have a limited impact on the estimates of the covariances, because without increasing the number of ensemble members, those estimates would be unlikely to significantly benefit from the availability of higher-resolution information in the limited-area domains. In the case of a 4D-Var, the limiting factor is the resolution of the tangent linear model (TLM) used in the computation of the covariances (see appendix). In particular, in the operational practice of 4D-Var, the TLM and its adjoint are integrated at a reduced resolution, compared to that of the original model. For instance, while the operational resolution of NAVGEM is T425 (1278 × 639 Gaussian grid), the operational resolution of the TLM in NAVDAS-AR is only T119. At an operational center that currently uses a 4D-Var for limited-area data assimilation, the TLM of the limited-area model is already available, and REG DA could be used to produce higher-resolution predictions of the covariances in the limited-area domains [see Eq. (A14) in the appendix]. Maintaining the TLM (and its adjoint) of the limited-area model, however, would add to the development cost on the long run. Hence, we propose an implementation, in which only the TLM (and its adjoint) of the global model is used. In such an implementation, the savings in computer wall-clock time from not doing limited-area data assimilation could be used to increase the resolution of the global TLM (and its adjoint). The global analysis would benefit directly, while the limited-area analysis would benefit indirectly from the increased resolution of the TLM (and its adjoint).
3. Implementation on NAVGEM and COAMPS
Our implementation of REG 4D-Var on the Navy's operational forecast system allows for the use of multiple limited-area domains. We show results for a configuration that has three limited-area domains: a North American, a northeast Pacific, and a European domain (Fig. 1). The NAVGEM global and the COAMPS limited-area model states are blended on a common high-resolution grid rather than the COAMPS grid.
a. Outline of the implementation
The implementation of REG 4D-Var on the Navy's operational global and limited-area models and global data assimilation system has the following top-level steps (Fig. 2):
Preparation of the short-term NAVGEM forecast that provides the global background , , where N is the total number of times at which observations are available in the assimilation window.
Preparation of the short-term COAMPS forecasts that provide the limited-area background , , for each region.
Interpolation of both the NAVGEM and the COAMPS forecast fields onto a common high-resolution grid.
Blending and , , in each limited-area region according to Eq. (2).
Computation of the analysis increment by NAVDAS-AR.
Preparation of the global analysis by adding correction to global background.
Preparation of the limited-area analyses by interpolating global analysis to regional model's grid.
Because the observation time window of NAVDAS-AR is a 6-h-long period centered at the analysis time, the data assimilation time window for the background state is from forecast time 3–9 h relative to the previous analysis time. NAVGEM and COAMPS both provide hourly outputs, so a composite state is created for each of the 7 h of the observation time window, which means that for steps 1, 2, and 4.
In step 5, we turn on the digital filter initialization option of NAVDAS-AR to ensure proper balance between the momentum and mass field in the analysis increment. The digital filter of NAVDAS-AR is based on the algorithm of Lynch and Huang (1992) and Lynch et al. (1997) and is applied during the last iteration step of the inner loop of NAVDAS-AR to the TLM solution (T. Rosmond 2017, personal communication). In step 6, the analysis increment is added to the NAVGEM background to create a REG analysis, which also serves as the initial condition of the NAVGEM forecast. The COAMPS initial conditions for the three domains are obtained by the interpolation of the same REG analysis.
b. The interpolation procedures
The common grid used for the blending of the global and limited-area states must have a resolution similar to that of the limited-area model, because otherwise, the data assimilation system could not take advantage of the higher-resolution forecast information in the computation of the innovations. Both the global and limited-area model fields are interpolated onto this common grid. Because NAVGEM is a spectral transform model, for the interpolation of the NAVGEM fields, we first prepare a higher-resolution spectral representation of the fields by zero padding, that is, by adding higher-wavenumber components with zero spectral coefficients to the spectral representation of the fields, then transforming the fields onto a higher-resolution Gaussian grid (the common grid).
Because COAMPS is a finite-difference model that represents the fields on a grid in all phases of the model computations, the interpolation of the COAMPS fields requires a grid-space interpolation in both the horizontal and the vertical directions. We use a nearest-neighbor approach for the horizontal interpolation. The vertical interpolation is more involved than the horizontal interpolation, because the two models use different vertical coordinates. In particular, NAVGEM uses a hybrid sigma-pressure coordinate with a pressure-based definition of sigma , where is the surface pressure. The coordinate transitions gradually from pure sigma at Earth's surface to pure pressure at the top of the model atmosphere (Hogan et al. 2014). COAMPS, in contrast, uses a height-based sigma coordinate , where is the terrain height, and H is the finite depth of the model atmosphere. The vertical interpolation is performed after completion of the horizontal interpolation of the COAMPS fields on the height-based sigma surfaces of the model. The hybrid sigma-pressure coordinate of each grid point is computed based on the horizontally interpolated COAMPS pressure and surface pressure fields. For this calculation, the horizontally interpolated surface pressure field is corrected for the difference between the orography of NAVGEM and the orography of COAMPS at each horizontal gridpoint location. The procedure follows that of Baek et al. (2009); that is, the corrected surface pressure is obtained by
where is the original surface pressure, g is standard gravity (9.8 m s−2), is the terrain difference between the two models, is the gas constant for dry air (287 J kg−1 K−1), and is the mean temperature of the deep (hypothetical) atmospheric layer, which we estimate by
In Eq. (7), γ is the standard atmospheric lapse rate (), and is the surface temperature. Because the temperature is available only at model levels, we estimate the surface temperature by Eq. (A5) of Baek et al. (2009):
where and are the values of sigma and the temperature at the lowest NAVGEM level, respectively. Figure 3 illustrates the effect of the correction for the orography difference on the COAMPS surface pressure field. The figure shows that the correction efficiently removes the direct effect of the orography difference. The remaining differences reflect differences in the evolution of the atmospheric flow between the two models, which may include indirect effects of the orography differences that we hope to take into account in our data assimilation approach.
After the computation of the hybrid sigma-pressure coordinate of each grid point of the horizontally interpolated COAMPS fields, the COAMPS fields are interpolated linearly onto the hybrid sigma-pressure surfaces of NAVGEM. A seemingly problematic aspect of this interpolation is that it does not allow for the computation of the COAMPS fields at locations where the NAVGEM vertical coordinate is associated with a pressure that is higher than the pressure at the lowest COAMPS vertical level. This is the situation at locations where the orography is lower in NAVGEM than in COAMPS. At such locations, our interpolation procedure would introduce discontinuities into the interpolated COAMPS fields, as shown in Fig. 4. These potential discontinuities do not pose a problem in REG 4D-Var, because the interpolated fields are used only for the computation of the innovations, and no observation is expected to fall below the more realistic orography of COAMPS. We did verify that all observations satisfied this condition.
The final step is the computation of the REG background state vector by Eq. (2). In each experiment described in this paper, we use the same value of the blending coefficient α for all COAMPS domains, but near the lateral boundaries (15 grid points from the edge of the domains) and the top of the COAMPS model atmosphere, α is gradually tapered to zero in order to avoid introducing boundary effects into the analysis of the atmospheric fields.
4. Experiment design
Three types of experiments are discussed in this paper (Table 1). While all three are carried out at horizontal resolution T119 of NAVGEM (360 × 180 Gaussian grid) and 32 km of COAMPS and produce a T119-resolution global analysis, they differ in the ways they use NAVDAS-AR. The first type of experiment is a control experiment, in which the global analysis is prepared by the conventional configuration of NAVDAS-AR, integrating the TLM of NAVGEM at a reduced resolution of T47. The goal of this experiment is to simulate the feature of the operational DA system that the resolution of the TLM is much lower than that of forecast model. In the second type of experiment, which we call the blend-skip experiment, the TLM is integrated at an increased horizontal resolution of T119, and a full implementation of the REG DA code is used with a blending coefficient of . The purpose of this experiment is to test the effects of an increased resolution of the TLM, assess the potential detrimental effects of the extra interpolation steps of REG DA, and provide a clean baseline for the evaluation of the third type of experiments, in which REG DA is used with (30%-blend experiment), (50%-blend experiment), and (100%-blend experiment) blending coefficients.
We chose the particular limited-area domains shown in Fig. 1, because they provide a representative sample of the terrains of the limited-area domains routinely used by the U.S. Navy. The analyses of the experiments are obtained by continuously cycling NAVDAS-AR for the period 0000 UTC 1 October–0000 UTC 1 November 2012. Analyses are prepared every 6 h by assimilating all observations that were assimilated in real time by the FNMOC of the U.S. Navy.
5. Results
We first present diagnostics of the qualitative behavior of REG 4D-Var, and then we show an example of the analysis increment produced by the technique. Finally, we present diagnostics of the forecast accuracy for NAVGEM for the first 120 forecast hours and for COAMPS for the first 72 forecast hours.
a. Convergence and balance
These diagnostics are prepared to verify that the implementation of REG 4D-Var has no adverse effects on the minimization procedure of NAVDAS-AR. We first examine the speed of the convergence of the minimization algorithm (Fig. 5), as an occasional significant increase of the number of iterations, or a complete lack of convergence of the minimization process, would indicate a failure of REG 4D-Var. The blend-skip and nonzero-blend experiments require, on average, only five additional iterations, compared to the average 50 iterations of the control, which indicates that while the increased resolution of the TLM leads to a slight increase of the necessary iteration steps, the blending of COAMPS information does not lead to an additional increase.
The next set of diagnostics describes the effects of REG 4D-Var on the balance between the mass and the wind field in the analyses (Fig. 6). A standard approach of numerical weather prediction to detect lack of balance in the atmospheric initial conditions is to compute the surface pressure tendency for the first few forecast hours: an initially large, then quickly decaying, surface pressure tendency indicates a rapid adjustment of the mass field, that is, a lack of balance in the initial conditions. The figure shows the evolution of the maximum and minimum (largest magnitude negative) of the surface pressure tendency in the first 6 h for each NAVGEM (global) forecast: each curve is for a different initial condition (time). The results are very similar for the 50%-blend (top row) and control (bottom row) experiments. The only region in which the 50%-blend experiment shows larger initial tendencies than the control experiment for a few forecasts is the northeast Pacific domain. Because the magnitudes of the tendencies do not indicate serious balance issues even in those exceptional cases, we conclude that blending has no significant effect on the atmospheric balance in the analyses.
b. An example of the analysis increment
An example of the virtual potential temperature analysis increment produced by our system is shown in Fig. 7 for the model level nearest to the surface. The figure shows that the difference between the two increments is confined to the limited-area domains. Interestingly, the increment often has a larger local magnitude and/or the opposite sign for the 50%-blend experiment (middle panel) than for the blend-skip experiment (top panel). The local differences between the two increments (bottom panel) tend to be the largest in regions of high and/or complex orography (e.g., in the European domain). These are locations where the limited-area background typically has more realistic information than the global background about the distance of the pressure levels of the observations above the surface of the Earth. Observe, for instance, the pressure difference between the 900-hPa level and the surface pressure of the two models in Fig. 3. Figure 7 suggests that these are the locations where REG DA (the COAMPS background information) has the largest impact on the analyses.
c. Measures of the forecast errors
We calculate mean-square-error (MSE)-based diagnostics of the forecast performance by using operational analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF) as verification data, 1° × 1° for NAVGEM verification, and 0.5° × 0.5° for COAMPS verification. We calculate the MSE for each state variable and the standard pressure levels in two different ways. We either calculate a single scalar measure ε of the forecast error by
where N is the number of verification locations (grid points), T is the number of forecasts (verification times), is the forecast, and is the verifying analysis of forecast variable x (e.g., geopotential height) valid at time t; or we produce a map of the spatial distribution of the errors by displaying
For all the results shown using either of these measures, T is equal to 63, the number of forecasts over the entire experiment period that were verified against ECMWF analyses.
We test the statistical significance of the MSE difference between a pair of experiments by using the test statistic
where and are the values of the MSE for the two experiments at verification time t, and is the sample variance of the difference . The effective sample size is estimated by
where is the lag-1 autocorrelation coefficient for the time series of error differences . This test statistic z is then used to find a confidence probability p by using left-tail cumulative probabilities for the standard Gaussian distribution. We mark all MSE differences for which as statistically significant with hashed boxes. We emphasize, however, that p should not be interpreted as the probability that the mean difference between the errors is zero, or that the differences between the errors of the two experiments were produced by random chance alone (Wasserstein and Lazar 2016). The proper interpretation of a statistically significant positive difference is that it is sufficiently large, compared to the sample variance, to warrant further investigations into the forecast benefits of REG 4D-Var.
d. Impact diagnostics
We refer to diagnostics that show the MSE difference between two experiments in percentage format as impact diagnostics. For instance, when the results of experiment A are compared to the results of experiment B, the impact diagnostics are computed by , where is the MSE ε for a particular forecast variable in experiment A, and is the MSE ε for the same forecast variable in experiment B. Notice that the difference is taken such that a positive value of the diagnostics indicates that the forecasts of experiment A are more accurate on average than the forecasts of experiment B. While we include results for forecast time 0 h (the analysis time) in the figures, those results should be interpreted with some care, because the errors of the verifying ECMWF analyses are statistically not completely independent of those of our analyses, as they assimilate similar sets of observations.
Figure 8 is the first of a series of impact diagnostics figures. It compares the geopotential height forecast error of the control with those of the blend-skip and 50%-blend experiments. The different panels show the results for different regions: the left panel is for the entire Northern Hemisphere extratropics, while the next three panels are for the three regions (the name of each region is indicated above the top panel). The forecasts of the blend-skip experiment are clearly more accurate than the forecasts of the control, which confirms our expectation that the quality of the global analyses would benefit from an increase of the resolution of the TLM and its adjoint. It also indicates that the improvements from the higher-resolution TLM and adjoint outweigh the potential degradations from the interpolation errors of our REG DA implementation. Similar to the forecasts of the blend-skip experiment, the forecasts of the 50%-blend experiment are clearly more accurate than those from the control.
In what follows, we investigate the forecast effects of REG DA by computing the impact diagnostics for each experiment that uses a particular value of the blending coefficient and the blend-skip experiment. The impact diagnostics we show are calculated such that positive values in the figures indicate positive forecast impact for the particular value of the blending coefficient. Because the TLM is integrated at resolution T119 in both types of the experiments that we compare, the forecast impact shown in these figures is solely due to the use of COAMPS information in the data assimilation step of the particular experiment.
Figures 9–12 each summarize the results for a different state variable. The format of these figures is similar to that of Fig. 8, except they now have six rows of panels. The six rows consist of three pairs of figures for NAVGEM and COAMPS, with each pair showing results for a different value of the blending coefficient. In addition, the first column for COAMPS shows results for a combination (composite) of the three limited-area domains rather than the NH extratropics. The results of this column represent the overall performance of REG DA. In this column, the diagnostics change more smoothly between the forecast lead times and vertical levels than in the other columns, because they are based on larger spatial samples of the errors.
Figure 9 can be directly compared to Fig. 8, as it also shows results for the geopotential height: blending clearly leads to more mixed results (Fig. 9) than increasing the resolution of the TLM (Fig. 8). But the results are dominantly positive for the 30%- and 50%-blend experiments for both NAVGEM and COAMPS. For these blending coefficients, the number of lead times and vertical levels with statistically significant positive forecast impact is about the same for NAVGEM and COAMPS, but negative forecast impact occurs more rarely for COAMPS. Most importantly, for the composite region of COAMPS, degradations occur only at analysis time and only in the upper troposphere, and none of the degradations is statistically significant. In contrast, for NAVGEM in the NH extratropics, some of the degradations in the upper troposphere at analysis time are statistically significant and also present at 12-h lead time, which suggests that the degradations detected at analysis time may not be pure artifacts of the verification. In general, the forecast improvements propagate from the lower to the upper troposphere as forecast lead time increases, and there is considerable variability in the performance of both NAVGEM and COAMPS between the different limited-area regions.
The performance of the models in the 100%-blend experiment is, in general, poorer than in the 30%- and 50%-blend experiments. The degradation that results from using only the COAMPS background information in the limited-area domains is particularly notable in the higher-tropospheric layers (starting at 700 hPa) in the first 36 h of the NAVGEM forecasts. In the lower layers, at the shortest forecast times, the overall improvements in the 100%-blend experiments are just as large as or even larger than in the 50%-blend experiment. These results suggest that while the use of the higher-resolution COAMPS background information helps the interpretation of the observations near the surface, the poorer COAMPS background information about the larger-scale processes in the free atmosphere leads to a degradation of the analyses in the upper layers. In the northeast Pacific domain, which is dominantly an oceanic region at the exit of the Pacific storm track, the forecasts of the 100%-blend experiment are degraded in the lower layers, which also supports this conclusion.
The forecast impact of REG DA on the temperature forecasts (Fig. 10) is weaker than on the geopotential height forecasts. Otherwise, there are several similarities between the results for the two state variables: the results are positive for the 30%- and 50%-blend experiments and mixed at best for the 100%-blend experiment, and there are fewer lead times and vertical levels of degradation for COAMPS than NAVGEM. Most notably, the degradations at the lowest two levels in the NAVGEM forecasts at all forecast times are absent in the COAMPS forecasts. There is considerable variability in the forecast performance among the three limited-area domains. Interestingly, degradations and statistically significant improvements are both found more frequently for NAVGEM than COAMPS. For instance, REG DA performs particularly well for NAVGEM in the northeast Pacific domain, which covers a dominantly oceanic region. In addition, the magnitude of the low-level degradations for NAVGEM is the largest in the European domain, the domain that has the most complex orography. The most likely explanation for this result is that it is an artifact of the verification approach. In a mountainous region, the surface pressure is often lower than 1000 or even 925 hPa (Fig. 3). The extrapolated (hypothetical) "below ground" value of the temperature is more sensitive to the presumed lapse rate than the extrapolated value of the pressure. In addition, because the NAVGEM orography is often lower than the orography of the verification dataset, the verification is based on comparing an actual predicted temperature to an extrapolated value at several locations. The possibility that blending truly leads to degradation of the temperature forecasts near the surface, however, cannot be ruled out unequivocally. In that case, it is possible that the degradation can be reduced or completely eliminated by the refinement of the interpolation in the blending process. This is an issue that we hope to further investigate in the future.
The impact diagnostics results are quite positive for the two horizontal components of the wind (Figs. 11 and 12). Similar to the results for the geopotential height and temperature, the forecasts of the 30%- and 50%-blend experiments perform much better than the forecasts of the 100%-blend experiment. Particularly notable is the good performance of REG DA for COAMPS in the composite domain in the 30%-blend experiment. Similar to the results for the geopotential height, the statistically significant forecast improvements tend to propagate from the lower to the upper troposphere as forecast time increases.
In summary, the impact diagnostics suggest that REG DA with a 30% or 50% blending coefficient has an overall positive impact on both the NAVGEM and COAMPS forecasts. The forecast impact for the 100% blending coefficient is more negative. This result suggests that while using some short-term limited-area forecast information in the calculation of the innovations has a positive forecast impact, using only limited-area forecast information has a mixed impact.
e. Systematic and transient errors
To investigate the origin of the forecast improvements from REG DA, we decompose the MSE ε into an error variance V and a square of the bias component. That is, , where
The square of the bias is a measure of the magnitude of the persistent part of the systematic errors, while the error variance describes the magnitude of the transient component of the errors, which includes both the random errors and the flow-dependent part of the systematic errors.
We show results for the geopotential height at the 500-hPa pressure level (Fig. 13). The organization of the rows and columns of this multipanel figure is the same as that of the impact figures (Figs. 9–12). We use a logarithmic scale on the y axis of the panels, because the amplification of the magnitude of the random errors by the chaotic dynamics of the atmosphere leads to an exponential growth of the transient errors in the first few forecast days. The error components for the experiments with blending coefficients are shown relative to the respective error components for the blend-skip experiment. A positive value indicates lower errors in the experiment with than in the blend-skip experiment, while a negative value indicates higher errors in the experiment with . While we include results for the 100%-blend experiment in the figures for the sake of completeness, our discussion below is based on the results for the 30%- and 50%-blend experiments.
The results suggest a general picture in which the forecasts of the experiments with are more accurate than the forecasts of the blend-skip experiment. The advantage of the experiments grows with the forecast lead time for the entire 72 h of the COAMPS forecasts and the first 96 h of the NAVGEM forecasts. The fact that this advantage largely disappears after 96 h for NAVGEM suggests that the injection of COAMPS information in NAVDAS-AR leads to improvements of the global forecasts primarily at the smaller scales, at which the predictability time limit is shorter.
The impact of REG DA on the variance of the analysis and 12-h lead time forecast errors is positive for COAMPS and neutral for NAVGEM. This result suggests that the limited-area initial conditions benefit more from the filtering of random errors and the correction of flow-dependent systematic errors. The impact on the variance of the forecast errors becomes increasingly more positive as forecast time increases in the NH extratropics for NAVGEM and the composite domain for COAMPS. The typical trend for the individual domains is similar, but there are some exceptions to that rule at the shorter forecast times. The longer-term (48 h for COAMPS and 96 h for NAVGEM) positive impact of REG DA on the MSE is dominantly due to the positive impact on the error variance.
There is more variability in the impact of REG DA on the bias (mean systematic errors) than the variance among the models, verification regions, and forecast lead times. In particular, while the impact on the bias is negative in the first 48 h for NAVGEM, it becomes positive for COAMPS by the 24-h forecast lead time. Particularly notable is the difference between the behavior of NAVGEM and COAMPS in the European domain: while the impact never becomes positive for NAVGEM, it is not only positive for COAMPS, but also makes a significant contribution to the positive impact on the MSE. This result suggests that REG DA can be particularly beneficial for the COAMPS forecasts in regions of such complex orography as Europe. This is a highly encouraging result, because reducing systematic errors in regions of complex orography is one of the main motivations to use a limited-area data assimilation system for the downscaling of the initial conditions. We also note that the negative impact on the bias component of the NAVGEM forecast errors is not necessarily a negative result for REG DA. In fact, in the presence of model errors whose correction by the data assimilation would lead to a strong model "spinup," this is an expected tradeoff to reduce the error variance and the MSE (Baek et al. 2006, 2009).
Interestingly, there is a limited-area domain—the North American domain—where REG DA has a positive impact on the bias of the NAVGEM forecasts after 24 h. We use this domain to illustrate the impact of REG DA on the spatial distribution of the error components. We first show the impact on the error components for the geopotential height at several pressure levels (Fig. 14). The bias reduction over the Rocky Mountains and Sierra Madres is confined to the atmospheric layer between 925 and 1000 hPa. Above that layer, the reduction in bias turns into a widespread increase of the bias. We speculate that this increase of the bias is introduced by injecting strong boundary effects from the COAMPS model, which has a top at 4 hPa, into the NAVGEM, which has a top at 0.04 hPa. We hope that this bias can be greatly reduced or completely eliminated by a more rapid tapering of the blending coefficient approaching the top of the COAMPS model. The reduction of the error variance for the tropical cyclones (off the East Coast), however, extends into the upper troposphere.
Figure 15 shows the evolution of the 500-hPa diagnostics from Fig. 14 with forecast lead time. At analysis time, there are widespread bias reductions over the mountains, but these improvements disappear by the 12-h lead time. The bias reductions re-emerge at 24-h lead time, and their magnitude and the area they cover continue to grow as forecast time increases. The error variance shows a similar behavior along the tropical cyclone paths off the East Coast.
Diagnostics similar to those shown in Figs. 13–15 for the geopotential height were also prepared for the air temperature and the zonal and meridional wind components. We found that the notable changes in the air temperature forecasts were mainly below 700 hPa, with the largest differences at the surface. As an example, Fig. 16 shows the time evolution of the error components for the temperature at 1000 hPa. It should be noted that the 1000-hPa temperature field is obtained by extrapolation at the many locations where the surface pressure is lower than 1000 hPa. The errors in the prediction of this field, nevertheless, are a good measure of the performance of the model in predicting the air temperature near the surface. One particularly interesting feature of the figure is the large reduction of bias over Louisiana that has a rapidly growing magnitude with increasing forecast time. An inspection of the individual forecasts that contributed to this result revealed that the large bias correction was due to the systematic improvement of the prediction of frontal passages in the region.
6. Conclusions
We described the results of our first attempt at building a global data assimilation system that produces initial conditions for an operational global and limited-area model in multiple domains simultaneously. The motivation to build such a data assimilation system is the hope that it would eventually eliminate the necessity of maintaining a limited-area data assimilation system and doing limited-area data assimilation for multiple limited-area domains. The possibility of eliminating limited-area data assimilation is appealing not only because it would reduce development and computational cost, but also because limited-area data assimilation has fundamental limitations. Most importantly, limited-area data assimilation has no observational or model information about the large-scale atmospheric flow outside the limited-area domain, which makes the proper interpretation of the large-scale component of the flow inside the limited-area domain highly challenging. In practice, this challenge is addressed by partial cycling of the limited-area data assimilation, which is a less-than-ideal solution from both a theoretical and practical point of view. Our hope is that the unified data assimilation system would produce higher-quality limited-area initial conditions than partial cycling. The results of the present paper show that REG DA can produce limited-area initial conditions that lead to more accurate forecasts than the limited-area initial conditions obtained by the interpolation of a conventional global analysis. Particularly encouraging is the result that REG DA reduced the systematic limited-area forecast errors over complex topography. It is yet to be seen whether or not the magnitudes of these error reductions are comparable to, or even larger than, those from a partially cycled limited-area data assimilation.
The savings in development and computational time from eliminating limited-area data assimilation could be used for the enhancement of the unified data assimilation system, which itself could greatly improve the global initial conditions. As an example, we showed that the performance of the U.S. Navy's global data assimilation system (NAVDAS-AR) could be improved by increasing the resolution of the TLM (and its adjoint) of the global NAVGEM in data assimilation. It is important to point out that the other improvements from REG DA found here are in addition to the improvements from the increased resolution of the TLM. These additional improvements include a small but consistent overall improvement of the global forecasts in addition to the improvements of the limited-area forecasts.
Our technical approach for REG DA was motivated by the composite state method of Kretschmer et al. (2015). Their paper showed for a highly idealized model of the atmosphere that doing data assimilation by estimating a state vector, defined by a linear combination of the state vectors of the global model and the limited-area model in the limited-area domains, produced global and limited-area initial conditions that produced more accurate forecasts than the separate global and limited-area data assimilations. The particular 4D-Var-based implementation of REG DA tested in this paper, which we call REG 4D-Var, computes the innovations based on a model state defined by a linear combination of the state vectors of the global model and the limited-area model in the limited-area domains, but unlike Kretschmer et al. (2015), it updates a background from a global model integration. We find the results with the present implementation sufficiently encouraging to proceed to the testing of other strategies for the generation of the background. The list of other potential refinements to the technical approach includes the adjustment of the prescribed observation error variance in regions where higher-resolution limited-area model information is available (in expectation of reduced representativeness errors) and using blending coefficients that are optimized independently for each domain. In addition, the Navy has recently transitioned to using a hybrid 4D-Var system, which uses an ensemble-based approach to enhance the estimate of the background error covariance matrix. REG 4D-Var could be easily implemented on the hybrid data assimilation system.
This study was conducted in collaboration with two of the Naval Research Laboratory institutions, NRL Monterey and NRL D.C. Funding was provided from a Texas A&M University Diversity Fellowship and ONR Grants N00014-12-1-0785, N00014-16-1-3011, and N00014-18-1-2509 and NRL Award N00173-16-1-3011.
REG DA for Incremental 4D-Var
We describe the implementation of REG DA on an incremental 4D-Var scheme for a single iteration of the outer loop.
a. Incremental 4D-Var
In the incremental formulation of 4D-Var (Courtier et al. 1994), the increment is defined by
where are the times at which observations are available in the assimilation time window, and is the global background state at . The analysis
at time is obtained by computing the analysis increment that minimizes the cost function (e.g., Szunyogh 2014),
and adding it to the background , where is the linearization of about ; represents the TLM dynamics between times and for the nonlinear state space trajectory associated with the initial condition . In Eq. (A3), the expression in the square brackets of the second (observation) term approximates . The approximate equality of the two expressions,
follows from
Equation (A3) describes the cost function minimized by the inner loop of the algorithm. When multiple steps of the outer loop are taken, the nonlinear trajectory is recomputed at the beginning of each step of the outer loop, using the updated background from the previous step as initial condition. This involves recomputing for the updated values of .
b. The effect of replacing by
The analog of Eq. (A4) for is
and the linearization of about satisfies
Introducing the notation
Eq. (A11) can be written as
With the help of and , where the latter represents the linearization of the limited-area dynamics about the nonlinear trajectory , Eq. (A13) can be written as
Making the assumption that the interpolation function can be effectively approximated by a linear mapping,1 Eq. (A14) can also be written as
where the linear model integration, interpolation, and blending are amalgamated into a single linear mapping . This result shows that REG DA can be implemented by replacing by Eq. (A14) and by in the cost function of Eq. (A3): the new cost function is
The second (observation) term of the cost function depends on the control variable , which has the same resolution as in the original cost function [Eq. (A3)], through Eq. (A13).
In the operational practice of 4D-Var, the TLM is integrated at a reduced resolution, compared to the resolution of the original model. That is, the resolution of the TLM solution is lower than that of in the calculation of . The linearized observation function therefore operates on the reduced-resolution in the observation term of the cost function [Eq. (A3)]. Likewise, when is approximated by an integration of the reduced-resolution global TLM, the linearized observation function operates on a TLM field that has the resolution of the reduced-resolution global TLM.
Current affiliation: School of Earth Sciences, and ARC Centre of Excellence for Climate Extremes, University of Melbourne, Melbourne, Australia.
© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).
Term may not be strictly linear, because the vertical interpolation often involves a nonlinear change of coordinates.


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Wo, I did not take this picture. Wish I had. Check out Gordie's link at http://www.flickr.com/photos/gordie_broon/8600490014/. Don't forget to put the http:// in front.
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Wo, I did not take this picture. Wish I had. Check out Gordie's link at http://www.flickr.com/photos/gordie_broon/8600490014/. Don't forget to put the http:// in front.
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Biology is Not a Confusion of Conflicting Opinions
Biology is the Study of Measurable Facts
About the Natural Laws that Created Life on Earth
Next month will be the third anniversary of Bare Bones Biology. Here we replay the first episode, which is as true today as it was three years ago.
Why not begin with the television where some guy last night spent quite a few thousands of dollars trying to tell me the government should not be spending so much money because job creation comes from what he calls "the real world." He claims the real world is small business.
Pardon me? I thought we agreed the earth is not flat. If the earth is round, then small business is not at its center. But much more seriously, if we truly believe the wealth of this country comes from small business, or from any other individual human endeavor, we will continue to trash the ecosystem in the name of our own wealth. And when it's gone, or more likely when we use up the resources that create our wealth, oops, no more small business.
There is no center to the living ecosystem. Everything interacts with everything else. The living ecosystem runs by rules that we, including small business, must learn to understand if we truly want to create jobs. We can not build a healthy culture without a healthy ecosystem. We forget that we live inside the ecosystem. Look out your window. See those trees. They give us life. Without them, literally, we would not exist. The earth would be like Mars. Dead.
Have we forgotten that all money is based in resources? And all the resources of life come from the ecosystem. Or are we confused by the illogical and conflicting claims of the public media — even PBS and the school system? Has it become so complicated that we give up trying to understand? How do we know if all that outside my window is healthy, or if it isn't?
Bare Bones Biology agrees that life is confusing, but we say it's not as confusing as the media make it out to be. Sometimes human politics wants us to be confused about things that are very well understood. Certainly details of the ecosystem are more complicated than humans can understand. It would take longer than my life to figure out all the details of all the relationships among all the living things just in my front yard. There are so many interactions, and everything depends on something else, and it's all changing all the time. True. That's life. And that's what it looks like if we continue to look down at all the details, and forget to look up at the bare bones functional framework of the whole system.
I'm a biologist, did I tell you that? And I can tell you that scientists are confused about all the details of the ecosystem. But the good thing about science and scientists is that they usually know what they do know and what they don't know. And for the most part we are not confused about the bare bones functional laws that drive the ecosystem. The basic laws of nature are as solid and universal and reliable as gravity, and the laws of transfer of energy, that's thermodynamics, and life on earth — which is the ecosystem — is built around these natural laws.
We need to understand these laws and how they relate to biology if we want to rebuild a healthy human presence within a healthy ecosystem. Biology is not a confusion of conflicting opinions. Biology is the study of measurable facts about the natural laws that permit life on earth. Facts do not change. That's why we call them facts. And they don't care about our opinions. I don't understand why we want to fight over them, given that we all need the same things. We are acting like a bunch of cartoon characters with question marks over our heads, trying to decide whether to swim across the alligator infested river, or take a flimsy canoe, when there is a well built bridge just over on the left side of the picture. It would make a lot more sense, wouldn't it, to sit back together and figure out how to get across the bridge. To get what we all need by nurturing the health of our ecosystem.
So Bare Bones Biology will focus on the facts and their logic, that shows how everything truly does fit together in the largest unit of life that we know anything about, the whole earth ecosystem.
This is Bare Bones Biology, a weekly production of FactFictionFancy.com, and KEOS radio, in Bryan, Texas. The podcast can be downloaded at:
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How cold is it?
Pickup wouldn't start this morning. Two days ago I forgot to take my gallon of coffee water out of the pickup into the house. In the morning, frozen solid, so I brought it in. I also threw out several inches of ice from Bitsy's water. This morning, two days later, coffee water still has ice floating on it and Bitsy's chunk of water is still sitting there. Snow has not melted from the peaks to which I am going next Monday.
But we still went out in the 40-50 mph wind to Santa Fe's amazing four-acre dog park.
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My readers will like thie above post much more than they like most of mine.
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While you pursue your human goals
Believing that you help the suffering,
The butterflies are dying in your own back yard.
You can blame it on karma,
Or God's will,
Or some evil influence,
And all of that is true.
But the slaughter continues because of the human ego
that can walk across a field of death and destruction
And only see itself.
I wrote this poem last week while watching a fine blazing sunrise that reminded me of a quote from Psalm 89.
"The heavens are yours, and the earth is yours;
everything in the world is yours—you created it all."
Not thinking about the consequences, I immediately emailed my poem, without commentary, to several friends. Of course the moment I punched the "send" button I realized each of these people probably will assume I was writing about them personally. So then I followed up to explain what I really meant. One friend wrote back the following:
"If people think that it is about them, then maybe it is.
"But what SHOULD I do?" they will ask.
And your answer may seem nearly as difficult to them as the one that went, 'Sell what you have, and give to the poor.' Your answer is,
"Change your value system.
Stop listening to advertisements.
Stop feeding your emotions with STUFF; just get what you need, or genuinely want.
Get out, find some natural landscape somewhere. Breathe the air. Notice the rest of life besides humanity.
Discover what that other life needs. As a major item it needs less humans, so your main aim will be to have a small family if any, 1-2 children, and to persuade your friends and relations to do that too.
Your second aim will be to consume less goods (especially from distant places) and less energy, also less meat. There are many ways to do that.
The end."
Yes indeed, but my answer to the question of what we can do might not seem as daunting as her answer. Mine would be:
Watch the sun rise up and move across the sky as it greens the living earth. Meditate about the laws of nature that God created to generate and maintain Life, the whole earth ecosystem.
As the sun moves down every evening, consider that no opinion or no action of yourself or any other person created Life, and no opinion or no action of yourself or any other person can change how God's laws of nature function to maintain Life of earth.
Stop wasting your time arguing over opinions and debating fake advertising propaganda that is designed by the corposystem to spread death and destruction to the living earth. Learn to know the difference between opinions, and the created facts of life. Feed your emotions by learning how God's created natural laws actually do function to maintain Life. Specifically, how does Life stay alive? Without this knowledge, how can you function to protect Life?
Discuss these things as often as possible with other people, including people who are genuine experts. Learn to recognize expertise. (Hint, reporters' expertise is reporting things, not evaluating them. Technologists expertise is making things and selling them to make money — that is a corposystem value, Life's value is not to make money. Life's value is to maintain Life. Genuine experts study how things work.)
And then understand that wise compassion is not that soft-fuzzy feeling that makes us believe we are doing good for other people whenever WE feel good. Real wise compassion is the genuine heartfelt wish and obligation of every human to provide for the welfare of Life "unto the seventh generation" or beyond. It's our only value on earth, to protect God's creation, the Life-System, against the evils of human ego and the human corpo-system that deals in death and destruction in the name of fake compassion.
When you do as I suggest, then I trust your good judgment. You will automatically do as my friend suggests. If you do as she suggests, then you already understand much of what I suggest. Keep doing it.
Lynn Lamoreux
Photos by Lynn
This blog is an expanded version of Bare Bones Biology radio program that will play next week on KEOS Radio, 98.1 FM, Bryan, Texas. Bare Bones Biology is a completely nonprofit project. The podcast can be downloaded at:
For the people who read my blog, here's a quote from The Dalai Lama.
"The model of turning the other cheek or of having an overriding compassion for all beings, however they treat you, is about the elimination of self-centeredness and the cultivation of qualities that – in a theistic tradition – bring the practitioner ever closer to the Creatior, making him or her ever more similar to God, so that life may be lived in the image of God. In a nontheistic tradition like Buddhism, compassion brings its practitioner ever closer to freedom from clinging to the narrow confines of a self-centered way of being and opens a space for the interconnectedness with all beings."
Suggested References:
Lynn Lamoreux: Bare Bones Ecology Energy Handbook available free under Chapters on right side of blog
The Dalai Lama: Toward a True Kinship of Faiths
The Eagle: Butterflies
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I have somewhat reluctantly signed your petition. My reluctance is not because the topic is unimportant, but because I dislike our political warfare of this generation that takes the form of dividing survival topics into smaller bits that seem unrelated and using them to blame the "other guy" for the problem, which is neither logical nor realistic. So, I hesitate to sign for three major reasons.
First – I can't believe we are ignoring the men, whose future is at stake equally with the women.
Second – If we continue to escalate our arguments over the bits, I doubt we will be able to put the pieces back together. The ecosystem is one whole living thing, not a collection of separate human-specific issues. All of our food, air, water and soil are made by the ecosystem, which I believe is God's system for the survival of all Life on earth. Our population is threatening the welfare of every corner of the ecosystem, and that affects the welfare of every living entity within the ecosystem, some of which are humans. This is by far the most important reason that we must learn our place in the whole scheme of God's Creation. Obviously, the human world is not caring for the women and children first — now that food is running out as a result of overpopulation. Money and petitions will not change these facts unless they change the human behaviors that cause the food running out, which is too many people eating it. So I don't anticipate that conditions for women will improve while conditions for everyone deteriorate.
What we need to do is focus on health of the ecosystem. The ecosystem is a living entity that is deathly ill at this time, and the only way to cure an illness is to remove the cause. Only by reducing the human population can we improve conditions for women and men. Too bad we can't just agree on this – it is a fact, and facts are things we can't change. So it would be better to stop fighting over it and face it is a fact and focus on finding a better way to resolve our relationship to the fact – better than just blaming someone else. If we want better conditions, at this point, the only way to get them is to find a method of worldwide population reduction that is better than starvation, war and climate.
However, as far as I know, I have not succeeded in convincing even one person that facing facts is the best way to solve problems, and the matter is of massive importance to every person on earth, so I will honor your way and sign your petition,
Third – I have faith in Mr. Kerry that he will carry his responsibilities responsibly.
So in one sense, on the side of logic, I feel as though I'm contributing to the uproar rather than the solution by signing this, but I also feel, on the emotional side, that whatever works is necessary and you are doing a good job maintaining a balance between logic, reality and need.
I continue to wonder of those who oppose are as (what is the word? Uncaring?) as they seem to be, or is it only that they do not have access to the logical facts – the information about how God's Creation functions to keep itself alive. Still, we can't resolve the problem until we are willing to work within the factual reality, so I keep trying.
I have agreed to one more try at discussing some of these relationships by "manning" a booth at Earth Day this coming April in College Station. I agreed to discuss – not to debate. Debate is war, and it's an un-winable war when both sides are right. There is a better way.
Lynn Lamoreux
Photos by Lynn
This blog is an expanded version of Bare Bones Biology radio program that will play next week on KEOS Radio, 98.1 FM, Bryan, Texas. Bare Bones Biology is a completely nonprofit project. The podcast can be downloaded at:
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Bitsy lifted her head off my leg and went to sniff around the edges of her window on the passenger side of the truck. My lungs begged for more GOOD air for a good change. The sky gradually changed to a proper sky color, and, avoiding cities, we crossed over into the fresh air of New Mexico.
Next time I'll avoid Lubbock – got lost in exact same place again, after rattling around city streets for half an hour. Amarillo, a city that I like reasonably well has been under highway construction for so long that I gave up trying to remember what lane to be in, and went via Lubbock instead. What an astonishingly illogical state is Texas. Too bad, too. Texas had everything going for it when I arrived 35 years ago. If we had carried on with Ann Richards' responsibility to the future, instead of wallowing in yesterday's old, dry dreams of empire that have proven over and again in human history to be not viable, we in Texas could have led the world toward a future instead of toward its destruction and I would no be driving through the chill northern air.
Anyhow, I can breath in New Mexico, especially while I am not inside an infernal machine made of metal and oil and gas with all the windows rolled up, and the trip through Billy the Kid country is mostly fresh and fine, especially if you like red dirt and blue skies. And it is quite interesting that in the absence of toxic chemicals I lose all cravings for coffee and sugar.
But it is hard work for the truck and for me on a two-lane road with a heavy side wind, and quite a few giant trucks trying to pass without blowing me off the road. I spent the time between excitements imagining Billy, who was part of that frontier dream of yesterday, riding for – well it took us three hours with a truck that has big gas tanks – and he with only a horse for transportation in a land with no grass or water in sight.
We arrived at the Santa Rosa Trailer Park because I already knew it was there, and we stayed for an extra day because they have fixed their internet, access is not rationed, and it's good to take a rest day whenever possible. And there was a forecast of snow in Santa Fe, which is only about 100 miles away. An easy drive for a last day.
The Santa Rosa Trailer Park is not easy to see or find, because it is relatively quiet. It's tucked away down from the road, and it's the OLD road, not the new highway 40. There are more trails around the park than I have the energy to walk. And they have real dinners. I bought dinner for myself two days in a row. A record. All they lack is a dog park for Bitsy, who got bored and grumpy and curled up under the sleep bag in the 50 degree temperature. Who knows what is the chill factor in a 30 mph wind that never stops.
Oh yes, we also gained an hour, which means it is either earlier or later than my body thinks it is, and I overheard someone say we are about to gain another one — or lose the first one – again – tomorrow. No wonder the children of today believe that humans control the universal empire. Or could, if we try hard enough.
Filed under: Photography, Reportage | Tagged: Santa Rosa Trailer Park | Leave a comment »


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Editor's note: Some of the photos in this story are graphic.
Juan Angulo did not expect a peaceful protest to end with him barely being able to walk. He was one of three protesters who were hit with sponge and bean bag rounds during a peaceful protest on May 31.
The protest, which was organized by District 2 Commissioner Mariah Parker, attracted around two thousand people downtown to protest against police brutality. It ended with Athens-Clarke County police tear-gassing the couple hundred protesters that stayed into the evening.
The protesters stayed near the University of Georgia Arch, where the police tear-gassed them around midnight, a move ordered by ACC Police Department Chief Cleveland Spruill.
Angulo, who recently graduated from UGA with a plant biology degree, went to the protest at 6 p.m. and left when Parker ended the official protest around 7:30 p.m. Some protesters stayed at the Arch. Angulo returned that night around 9:30 p.m. He said the organizers made a point to tell him when he returned that the protest would remain peaceful — if he were to instigate violence, he should leave.
After the police sent warning drones that played messages asking protesters to disperse, they began throwing tear gas canisters around midnight.
Angulo said he was trying to put out tear gas canisters with water bottles when he started running away. He was shot twice in the leg, which immediately went numb, he said.
"It wasn't localized. It was throughout my entire leg and reaching into my lower back," Angulo said. "It was just quite possibly the worst pain I've ever experienced. I've broken bones, I've broken my ankle, I've been in car accidents. This tops that by so much more."
Angulo said he managed to run away from the protest and make it back to his friend's apartment. He also experienced the effects of the tear gas — his face, hands and lungs began burning.
He said the swelling in his leg persisted even a week after the protest. He used crutches to attend the weeklong peaceful protests at the median of Broad Street.
Spruill said during a June 4 livestream that the ACCPD used "sponge rounds and bean bags" on three people in a "defensive posture" to prevent them from picking up tear gas canisters and throwing them back at police officers.
Angulo said he didn't throw any tear gas canisters at the police and didn't see anybody attempting to. Grayson Pynn, another protester who was hit with the ammunition, said he also didn't throw any of the canisters. Steven Elliott, who lives in Athens, said he nudged the canister with his foot to move it away from protesters when he was hit.
.@accpolice uses tear gas to disperse protesters at the Arch @redandblack pic.twitter.com/uqulhSnTXa— Hunter Riggall (@HunterRiggall) June 1, 2020
During the protest, Pynn used a leaf blower to try and blow the tear gas away from the crowd, a move which he said wasn't very effective. While he was using the leaf blower, he said he looked up to see an officer pointing at him.
"I got hit in the gut and knocked the wind out of me. It's like belly flopping, you know, off a 20-foot diving board," said Pynn, a rising junior music composition major. "I didn't expect that to happen at all because it was just tear gas up to that point."
Pynn said he couldn't breathe for 20 seconds after being hit. After catching his breath, he said he left the protest.
Angulo said he wished the police department spent more time discussing the use of sponge and bean bag rounds.
"That stuff is actually insanely powerful. I'm not sure what kind of sponge they are using, but it didn't feel like just a sponge that you clean dishes with," Angulo said. "I felt like that was addressed simply so they could say they addressed it."


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This site is a compendium of R code meant to highlight the various uses of simulation to aid in the understanding of probability, statistics, and study design. I frequently draw on examples using my R package
simstudy. Occasionally, I opine on other topics related to causal inference, evidence, and research more generally.
In my last post, I made the point that p-values should not necessarily be considered sufficient evidence (or evidence at all) in drawing conclusions about associations we are interested in exploring. When it comes to contingency tables that represent the outcomes for two categorical variables, it isn't so obvious what measure of association should augment (or replace) the \(\chi^2\) statistic. I described a model-based measure of effect to quantify the strength of an association in the particular case where one of the categorical variables is ordinal. [Read More]
Alternatives to reporting a p-value: the case of a contingency table
I frequently find myself in discussions with collaborators about the merits of reporting p-values, particularly in the context of pilot studies or exploratory analysis. Over the past several years, the American Statistical Association has made several strong statements about the need to consider approaches that measure the strength of evidence or uncertainty that don't necessarily rely on p-values. In 2016, the ASA attempted to clarify the proper use and interpretation of the p-value by highlighting key principles "that could improve the conduct or interpretation of quantitative science, according to widespread consensus in the statistical community. [Read More]
Clustered randomized trials and the design effect
I am always saying that simulation can help illuminate interesting statistical concepts or ideas. The design effect that underlies much of clustered analysis is could benefit from a little exploration through simulation. I've written about clustered-related methods so much on this blog that I won't provide links - just peruse the list of entries on the home page and you are sure to spot a few. But, I haven't written explicitly about the design effect. [Read More]
Analysing an open cohort stepped-wedge clustered trial with repeated individual binary outcomes
I am currently wrestling with how to analyze data from a stepped-wedge designed cluster randomized trial. A few factors make this analysis particularly interesting. First, we want to allow for the possibility that between-period site-level correlation will decrease (or decay) over time. Second, there is possibly additional clustering at the patient level since individual outcomes will be measured repeatedly over time. And third, given that these outcomes are binary, there are no obvious software tools that can handle generalized linear models with this particular variance structure we want to model. [Read More]
A brief account (via simulation) of the ROC (and its AUC)
The ROC (receiver operating characteristic) curve visually depicts the ability of a measure or classification model to distinguish two groups. The area under the ROC (AUC), quantifies the extent of that ability. My goal here is to describe as simply as possible a process that serves as a foundation for the ROC, and to provide an interpretation of the AUC that is defined by that curve. A prediction problem The classic application for the ROC is a medical test designed to identify individuals with a particular medical condition or disease. [Read More]
Repeated measures can improve estimation when we only care about a single endpoint
I'm participating in the design of a new study that will evaluate interventions aimed at reducing both pain and opioid use for patients on dialysis. This study is likely to be somewhat complicated, possibly involving multiple clusters, multiple interventions, a sequential and/or adaptive randomization scheme, and a composite binary outcome. I'm not going into any of that here. There is one issue that should be fairly generalizable to other studies. [Read More]
Adding a "mixture" distribution to the simstudy package
I am contemplating adding a new distribution option to the package simstudy that would allow users to define a new variable as a mixture of previously defined (or already generated) variables. I think the easiest way to explain how to apply the new mixture option is to step through a few examples and see it in action. Specifying the "mixture" distribution As defined here, a mixture of variables is a random draw from a set of variables based on a defined set of probabilities. [Read More]
What can we really expect to learn from a pilot study?
I am involved with a very interesting project - the NIA IMPACT Collaboratory - where a primary goal is to fund a large group of pragmatic pilot studies to investigate promising interventions to improve health care and quality of life for people living with Alzheimer's disease and related dementias. One of my roles on the project team is to advise potential applicants on the development of their proposals. In order to provide helpful advice, it is important that we understand what we should actually expect to learn from a relatively small pilot study of a new intervention. [Read More]
Any one interested in a function to quickly generate data with many predictors?
A couple of months ago, I was contacted about the possibility of creating a simple function in simstudy to generate a large dataset that could include possibly 10's or 100's of potential predictors and an outcome. In this function, only a subset of the variables would actually be predictors. The idea is to be able to easily generate data for exploring ridge regression, Lasso regression, or other "regularization" methods. Alternatively, this can be used to very quickly generate correlated data (with one line of code) without going through the definition process. [Read More]
Selection bias, death, and dying
I am collaborating with a number of folks who think a lot about palliative or supportive care for people who are facing end-stage disease, such as advanced dementia, cancer, COPD, or congestive heart failure. A major concern for this population (which really includes just about everyone at some point) is the quality of life at the end of life and what kind of experiences, including interactions with the health care system, they have (and don't have) before death. [Read More]


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