WRF-based 3DVAR Assimilation of Wind data for TCs & MDs of Indian Seas

Data assimilation plays a vital role in obtaining the accurate initial state of the atmosphere in the atmospheric models for better model performance. This study investigates the effects of assimilating sea surface wind data from QuikSCAT and Oceansat-2 satellites and DWR's reflectivity and radial velocity into the ARW-WRF -V.2.2 and -V3.2 models using the 3DVAR technique for the simulation of MDs and TCs of Indian regions. Results of simulations performed with and without data assimilation are compared with the TRMM observations, JTWC observations, and GFS-ANL fields for the validation of the forecast. Assimilating QuikSCAT wind data improves simulations of mslp fields, 950 hPa wind vectors, thermal structure of the meteorological systems, relative vorticity profile, and the intensity and spatial distribution of the precipitation. Track simulation results obtained for assimilation cases for cyclones show good agreement with the best track of the JTWC. The forecast impact (FI) on850 hPa U- and V- components, FI on 850 hPa and 500 hPa temperature and relative humidity are used to study month long assimilation of the QuikSCAT wind on the average short-range weather prediction.