Data Assimilation Seminar
Dr. Prashant Kumar, (June 26, 2023, 15:00-16:30)
|Affiliation||Indian Space Research Organisation, India|
|Title||The Assimilation Impact of Indian Satellites to Improve Short Range Weather Prediction using WRF Model|
The satellite observations are crucial in constructing the initial state of the atmosphere that helps to improve short- and medium-range weather predictions. The author will show current/future space-borne sensors available from ISRO for meteorological applications. These satellite measurements are assimilated in the weather model in addition to conventional observations and other global observing systems.
The author will discuss his research on assimilating all-sky water vapor(WV) radiance from two Indian geostationary satellites(INSAT-3D and INSAT-3DR) in the Weather Research and Forecasting(WRF) model. For all-sky assimilation, hydrometeors are considered as control variables by adding in background error covariance. Additionally, this study uses the application of a Global Satellite-based Inter-Calibration System(GSICS) based bias correction mechanism on WV radiances before their assimilation.
Further, a big challenge in satellite data assimilation is the effective use of InfraRed(IR) window channel radiances in the high-resolution weather model. Here, a hybrid data-assimilation method is prepared for the very severe cyclonic storm "Vardah," in which the three-dimensional variational(3D-Var) method is used to assimilate control observations, and the particle filter method is used to assimilate Indian geostationary satellite INSAT-3D data.
In another research, the impact of rainfall assimilation on mesoscale model forecasts is evaluated. The WRF model and its 4D-Var data assimilation system are used to assimilate the satellite-retrieved rainfall. The author will also discuss his research on generating a high spatiotemporal resolution GSMaP version(GSMaP_IMD) over the Indian mainland under ISRO JAXA joint research. The targeted resolutions are hourly and 0.1° × 0.1°. The results suggest GSMaP_IMD has a smaller root-mean-square difference(RMSD) and higher correlation when evaluated against independent rainfall products. These improvements are significant in orographic regions with high rainfall amounts, mainly the western Ghats and northeastern parts of India.