General Circulation Studies
{'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}
The primary objective of this ongoing research project is to               improve our understanding and prediction of atmospheric                         variability with time scales longer than a week.  The research                  involves the diagnosis of observational and model generated data                sets, using statistical techniques such as simple compositing,                  digital filtering in the time domain, principal component                       analysis and canonical correlation analysis, and emphasizing the                dynamical interpretation of the results in light of our current                 understanding of large-scale atmospheric dynamics.  The work is                 closely linked to projects in general circulation modeling at                   NCAR and NOAA/GFDL and to operational and experimental numerical                weather prediction at NOAA/NMC and ECMWF.                                            Fluctuations with periods on the order of weeks and longer                 are generated both by dynamical processes internal to the                       atmosphere and by interactions between the atmosphere and the                   underlying media (primarily the oceans and the land-surface                     hydrology).  The relevant dynamics include a variety of processes               that operate on low-frequencies, as well as the forcing by high                 frequency transients associated with baroclinic instability.  By                careful analysis of the spatial patterns and time sequences of                  time-filtered meteorological fields, the PIs hope to isolate the                signatures associated with the various processes and document how               they contribute to the total variability.                                            This research is important because it contributes in a                     direct way toward our ability to predict atmospheric variability                on time-scales relevant to human activities.
{'FirstName': 'John', 'LastName': 'Wallace', 'PI_MID_INIT': 'M', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'John M Wallace', 'EmailAddress': 'wallace@atmos.washington.edu', 'NSF_ID': '000102844', 'StartDate': '04/15/1989', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Washington', 'CityName': 'SEATTLE', 'ZipCode': '981951016', 'PhoneNumber': '2065434043', 'StreetAddress': '4333 BROOKLYN AVE NE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Washington', 'StateCode': 'WA', 'CONGRESSDISTRICT': '07', 'CONGRESS_DISTRICT_ORG': 'WA07', 'ORG_UEI_NUM': 'HD1WMN6945W6', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF WASHINGTON', 'ORG_PRNT_UEI_NUM': None}
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[{'Code': '1527', 'Text': 'LARGE-SCALE DYNAMIC METEOROLOG'}, {'Code': '5740', 'Text': 'Climate & Large-Scale Dynamics'}]
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