Analysis and Forecast Impacts from a 1DVAR Preprocessor-Driven Quality Control in the NCEP GDAS/GFS

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Monday, 5 January 2015
Kevin Garrett, NOAA/NESDIS, College Park, MD; and S. A. Boukabara

We present recent efforts supported by the Joint Center for Satellite Data Assimilation (JCSDA) to advance and increase passive microwave satellite observations assimilated within the GSI analysis system used to initialize both the Global Forecast System (GFS) model and the regional Hurricane WRF (HWRF) model at the National Oceanic and Atmospheric Administration (NOAA). Specifically, the use of a 1d-variational (1dvar) preprocessor within the GSI will be discussed. The 1dvar preprocessor, known as the Multi-Instrument Inversion and Data Assimilation Preprocessing System (MIIDAPS), is applicable to current and future satellite passive microwave sounders and imagers including those from POES and MetOp AMSU-A and MHS, SNPP/JPSS ATMS, F16-F20 SSMI/S, GCOM-W1 AMSR2, TRMM TMI and GPM GMI. The capability of the 1dvar preprocessor (which applies over all-surfaces and in all-weather conditions) includes increased quality control of the microwave radiances to be assimilated, provides dynamic surface emissivity over all surfaces therefore allowing the extension of the microwave data assimilation coverage, and cloud and rainy data assimilation through providing hydrometeor (cloud, rain, ice) information to the assimilation system. This 1dvar preprocessing increases the number and types of observations that can be assimilated. The information provided by the 1dvar preprocessor will help with the assimilation of surface sensitive channels over non-ocean surfaces as well as cloud and rainy radiance assimilation. Here we present the impact on both global analysis and forecast using a streamlined quality control based on the MIIDAPS outputs in the GSI. Specifically, metrics based on the number of observations passing quality control, O-A, and fits to observations will be used to assess analysis improvements, while traditional metrics such as anomaly correlation and hurricane track will be used to assess forecast impact.