Wednesday, 9 January 2013
Exhibit Hall 3 (Austin Convention Center)
Due to the lack of conventional observations over the oceans, it is very difficult to predict hurricane genesis and its evolution without using satellite data. New satellite observations from Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) on board the Suomi National Polar-orbiting Partnership (Suomi NPP) and Joint Polar Satellite Systems (JPSS) satellites provide the high vertical and good spatial resolution information for atmospheric temperature and moisture. Under this study, a near real-time assimilation/forecast system is developed to demonstrate how Suomi NPP and JPSS sounder data could potentially help us improving the forecast of tropical cyclone. The system is built based on the current available infrastructure from Space Science Engineering Center (SSEC) at University of Wisconsin-Madison and NOAA. The latest Community Gridpoint Statistical Interpolation (GSI) System and Weather Research Forecast (WRF) model are the basis of our assimilation and forecast system. The NOAA National Centers for Environment Prediction (NCEP) global forecast system (GFS) output will be used as GSI/WRF background and boundary input information. All conventional observations and satellite radiances (including ATMS) can also be got from NOAA/NCEP real time forecast system. In addition to the regular data from NCEP, the Product Evaluation and Algorithm Test Elements (PEATE) and Community Satellite Processing Package (CSPP) at SSEC can directly provide us the raw observational data and science data from Suomi NPP and later JPSS. CrIS radiance can be further inversed into temperature and moisture profiles by using Cooperative Institute for Meteorological Satellite Studies (CIMSS) hyperspectral IR sounder retrieval (CHISR) algorithm. So both radiance and retrieved sounding data can be tested in assimilation and forecast system. The experience and strategy learned from this system can help us better use of the Suomi NPP and JPSS sounder data and finally improve the hurricane forecasts. A few case studies will be presented to demonstrate the system functions and capability on hurricane forecasting.
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