In this paper, we perform data assimilation using radiance data from different sensors mhs, amsu-a for the improved weather prediction in for the study area covering Uttarakhand and Himachal Pradesh at different resolutions of 27km, 9km and 3km. Two fairly successful cases of explicit prediction of precipitation are presented. Two heavy rain events each in the year 2013, 2014 and 2015 selected with different rainfall distribution in space and time are utilized to examine the improvement for rainfall forecast after data assimilation. The simulation experiments and analysis have been carried out using open source softwares and tools WRF 3.6.1 3DVAR, Grid Analysis and Display System (GrADS) and MET. The Grid Analysis and Display System (GrADS) is a programmable interface that is used for easy access, manipulation, and visualization of various data. MET is designed to be a highly-configurable, state-of-the-art suite of verification tools. There is a high impact of initial conditions on the modeling accuracy of numerical weather prediction (NWP). Here we investigate the potential of data assimilation in improving the NWP rainfall forecasts in the area. The general behavior and evolution of the reanalyzed precipitation agree very well with the observations. Observations from satellite radiance were assimilated into the model at different intervals. The resulting forecast, covering a period of several days, accurately reproduced the intensification and evolution of the events. The simulated rainfall has also been compared with that derived from the Tropical Rainfall Measuring Mission (TRMM) satellite NCEP FNL, CPC and in situ observations. The encouraging results from the study can be the basis for further investigation of the direct assimilation of radiance data in 3DVAR system and Ensemble method of Kalman filter. It is remarkable that, the satellite radiances have greater effect on rainfall forecasts than initial dynamic downscaling.


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