Predictability of Monthly and Seasonal Average Circulation  of the Atmosphere
{'Code': '06020106', 'Directorate': {'Abbreviation': 'GEO', 'LongName': 'Directorate For Geosciences'}, 'Division': {'Abbreviation': 'AGS', 'LongName': 'Div Atmospheric & Geospace Sciences'}}
{'SignBlockName': 'Jay S. Fein', 'PO_EMAI': None, 'PO_PHON': None}
One of the very exciting frontiers of research in atmospheric                   sciences is understanding and extending the limits of dynamic                   predictibility on monthly and seasonal time scales.  The                        potential societal impact of successful extended range                          forecasting is enormous.  The main objective of this proposal is                to carry out numerical modeling and diagnostic studies to                       investigate the predictibility of the monthly and seasonally                    averaged circulation of the atmosphere.  The longer term goal of                the research is to investigate the predictibility of the coupled                land-ocean-atmosphere system.  The focus of the work is                         practical, seeking to put together a numerical model, adapted                   from the operational version used by the National Meteorological                Center(NMC), that should be able to produce extended range                      forecasts, and to put such a model to test.  To accomplish this,                the strategy is to utilize realistic models of individual                       components (atmosphere, ocean, and land surface processes) and to               carry out sensitivity and predictibility studies, first to                      examine the predictibility of each component separately, and                    ultimately to examine the predictibility of the fully coupled                   interactive system.  This is important because it will not only                 provide a crucial interface between the university research                     community and the NMC, but also allow development of better data                sets as well as improve NMC's forecast capabilities beyond the                  current limit of around two weeks.
{'FirstName': 'Jagadish', 'LastName': 'Shukla', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Jagadish Shukla', 'EmailAddress': 'jshukla@gmu.edu', 'NSF_ID': '000091332', 'StartDate': '07/15/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Maryland, College Park', 'CityName': 'COLLEGE PARK', 'ZipCode': '207425100', 'PhoneNumber': '3014056269', 'StreetAddress': '3112 LEE BUILDING', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Maryland', 'StateCode': 'MD', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_ORG': 'MD04', 'ORG_UEI_NUM': 'NPU8ULVAAS23', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF MARYLAND, COLLEGE PARK', 'ORG_PRNT_UEI_NUM': 'NPU8ULVAAS23'}
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[{'Code': '1527', 'Text': 'LARGE-SCALE DYNAMIC METEOROLOG'}, {'Code': '2488', 'Text': None}, {'Code': '3007', 'Text': None}, {'Code': '3471', 'Text': None}, {'Code': '4984', 'Text': None}, {'Code': '5730', 'Text': 'GLOBAL ATMOSPHERIC RESEARCH'}, {'Code': '5740', 'Text': 'Climate & Large-Scale Dynamics'}, {'Code': '9120', 'Text': 'CROSS-DIRECTORATE PROGRAMS'}]
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