Incorporation of Observed Precipitation Estimates in        Four-Dimensional Data Assimilation Schemes for Mesoscale    Models
{'Code': '06020105', 'Directorate': {'Abbreviation': 'GEO', 'LongName': 'Directorate For Geosciences'}, 'Division': {'Abbreviation': 'AGS', 'LongName': 'Div Atmospheric & Geospace Sciences'}}
{'SignBlockName': 'Stephan P. Nelson', 'PO_EMAI': None, 'PO_PHON': None}
A potential for significant improvement in mesoscale                       numerical weather prediction lies in finding the optimal use                    for the new data sets (from next generation radar, or NEXRAD,                   satellites, automated surface observations, wind profilers,                     aircraft reports, etc.) that will become available                              operationally during the next decade.  These observations are                   incorporated into a four-dimensional analysis process that                      produces an initial state compatible with the forecast model.                   The research under this award specifically addresses the issue                  of how to include precipitation measurements in this analysis                   process so that the model's rainfall rates will resemble the                    atmosphere's as closely as possible as the forecast begins.                     The principal investigators will explore methods to using                       raingauge, satellite and radar data to determine the vertical                   distribution of heating in the lower atmosphere.  This                          information is then incorporated into the atmospheric analysis                  during a pre-forecast integration in which the mass and wind                    fields are brought into dynamical balance with the imposed                      condensational heating.  This approach should greatly reduced                   the problem of "spin-up" in the model prediction.                                    This project is part of a joint program between NOAA's                     National Meteorological Center (NMC) and NSF.  An important                     aspect of the research will be to test the new algorithms that                                                                                                  are developed in the NMC operational models.
[{'FirstName': 'Glen', 'LastName': 'Lesins', 'PI_MID_INIT': 'B', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Glen B Lesins', 'EmailAddress': None, 'NSF_ID': '000363969', 'StartDate': '10/15/1988', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Frederick', 'LastName': 'Carr', 'PI_MID_INIT': 'H', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Frederick H Carr', 'EmailAddress': 'fcarr@ou.edu', 'NSF_ID': '000108329', 'StartDate': '10/15/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}]
{'Name': 'University of Oklahoma Norman Campus', 'CityName': 'NORMAN', 'ZipCode': '730193003', 'PhoneNumber': '4053254757', 'StreetAddress': '660 PARRINGTON OVAL RM 301', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Oklahoma', 'StateCode': 'OK', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_ORG': 'OK04', 'ORG_UEI_NUM': 'EVTSTTLCEWS5', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF OKLAHOMA', 'ORG_PRNT_UEI_NUM': None}
{'Name': None, 'CityName': None, 'StateCode': None, 'ZipCode': None, 'StreetAddress': None, 'CountryCode': None, 'CountryName': None, 'StateName': None, 'CountryFlag': None, 'CONGRESSDISTRICT': None, 'CONGRESS_DISTRICT_PERF': '""'}
[{'Code': '1525', 'Text': 'Physical & Dynamic Meteorology'}, {'Code': '9145', 'Text': 'SPECIAL PROGRAMS-RESERVE'}]
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1988&All=true', 'xml': '8807752.xml'}