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This study presents a framework to assimilate data observed by a variety of space- and ground-based remote sensing instruments. The framework is based on the WRF 3-D Var system. Data to be assimilated are brightness temperature from TRMM Microwave Imager (TMI), radar reflectivity from conventional radars, precipitable water vapor (PWV) retrieved from GPS observations, and horizontal winds from operational wind profilers. A heavy rainfall case, which was occurred around Kyushu Island in 2003, is chosen for an application. Results indicate that each data can contribute to significant reduction of the inherent spin-up problem. Assimilation of TMI data improves forecasting of convections coming from offshore, which is effective over the lead time of 6-hour. Temperature retrieval with radar reflectivity is necessary for quantitative precipitation forecasting (QPF). GPS-PWV data is useful with TMI data to recover water vapor field. Assimilation of ground-based data has positive impact up to 6-hour ahead at most in this study. After all, QPF with assimilation of all datasets shows the best performance.

THREE-DIMENSIONAL VARIATIONAL DATA ASSIMILATION OF MULTIPLE REMOTE SENSING OBSERVATIONS