Wednesday, 13 January 2016
Observations from next generation of environmental sensors onboard the Joint Polar Satellite System (JPSS) and Geostationary Operational Environmental Satellite R-Series (GOES-R), provide us the critical high resolution and high temporal information for numerical weather forecast (NWP). How to better represent these satellite observations and how to get value added information into NWP system still need more studies. Recently scientists from Cooperative Institute of Meteorological Satellite Studies (CIMSS) at University of Wisconsin-Madison have developed a near realtime regional Satellite Data Assimilation system for Tropical storm forecasts (SDAT) (http://cimss.ssec.wisc.edu/sdat). The system is built with the community Gridpoint Statistical Interpolation (GSI) assimilation and advanced Weather Research Forecast (WRF) model. With GSI, SDAT can assimilate all operational available satellite data including GOES, AMSUA/AMSUB, HIRS, MHS, ATMS, AIRS and IASI radiances and some satellite derived products. In addition, some research products, such as hyperspectral IR retrieved temperature and moisture profiles, GOES imager atmospheric motion vector (AMV) and GOES sounder layer precipitable water (LPW), are also added into the system. Using SDAT as a research testbed, studies are conducted to show how to improve high impact weather forecast by better handling cloud information in satellite data. By collocating high spatial resolution MODIS/VIIRS data with the corresponding hyperspectral resolution AIRS/CrIS data, precise clear pixels of AIRS/CrIS can be identified and some partially or thin cloud contamination from pixels can be removed by taking advantage of high spatial resolution and high accurate MODIS/VIIRS cloud information. The results have demonstrated that both of these strategies have greatly improved the hurricane track and intensity forecast. Similar studies have also been tested in the microwave sounders by the collocation of AMSU/MODIS and ATMS/VIIRS data. Besides we have studied the impacts of high temporal GOES retrieved total/layer precipitable water on the severe storm. Lessons learned will help us better use the high temporal and high spatial resolution GOES-R Advanced Baseline Imager (ABI) data. Results from above along with other progresses of SDAT will be presented in the meeting.
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