683 Assimilation of GPM-Retrieved Surface Meteorology Variables for Two Winter Storms

Tuesday, 14 January 2020
Hall B (Boston Convention and Exhibition Center)
X. Li, Univ. of Alabama in Huntsville, Huntsville, AL; and J. Srikishen, J. B. Roberts, W. A. Petersen, and C. R. Hain

The Global Precipitation Measurement (GPM) mission is a multi-satellite constellation that provides global observations from research and operational microwave sensors. The GPM microwave imagers measure brightness temperatures that can be used for estimating ocean surface and near surface meteorological properties including atmospheric humidity, temperature, wind speed, and sea surface temperature (SST). In this study GPM-retrieved ocean surface flux and surface properties were generated as a component of NASA Weather Focus Area and GPM Ground Validation participation in the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018) field research and forecast demonstration program(s). The retrieved GPM meteorological products were then used to explore regional assimilation impacts for the prediction of winter storms. Two heavy snowfall events are selected to investigate how the assimilation of the retrieved hourly temperature, moisture, and wind speed data affect the short-term forecast of the snowstorms.

The community NOAA Gridpoint Statistical Interpolation (GSI) v3.6 as developed by the Development Testbed Center (DTC) were used to conduct data assimilation. The GSI system was modified to properly ingest the retrieval data, and was used to conduct the data assimilation experiments. Case studies for heavy snowstorms affecting the Korean Peninsula and ICE-POP field domain on 27 - 28 February and 7 - 8 March 2018 were selected for testing with the regional Weather Research and Forecasting (WRF) model. The GPM retrieved near-surface meteorology data were assimilated into 9-, and 3-km resolution WRF initial conditions, and a 24-h forecast run of the WRF conducted. The control WRF model forecast was compared with data assimilation experiments to assess the impact(s) of data assimilation on precipitation, temperature, humidity, and flow structure. Our presentation will focus on analysis of the data assimilation impact on the WRF forecast quality on the selected ICE-POP winter weather events.

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