Impact of SCATSat‐1 Retrieved Wind Vectors on Short‐Range WRF Model Predictions Over the South Asia Region

Abstract

The Indian Space Research Organization launched SCATterometer Satellite-1 (SCATSat1) satellite by PSLV-C35 on 26 September 2016 from Sriharikota. In this study, SCATSat-1 retrieved winds are assimilated in the regional atmospheric model subsequent to preliminary evaluation of SCATSat-1 retrieved winds against National Centers for Environmental Prediction final analyses. The Weather Research and Forecasting (WRF) model and its three-and four-dimensional variational (3D-Var and 4D-Var) data assimilation system are used to demonstrate the importance of SCATSat-1 retrieved winds on short-range weather prediction. In the first set of experiments, two parallel runs are performed with and without assimilation of SCATSat-1 retrieved winds using 6-hourly cycling during entire month of December 2016. Results suggest that assimilation of SCATSat-1 retrieved winds improved the WRF model analyses and subsequent forecasts of winds and moisture. In the second set of experiments, parallel assimilation experiments are performed with SCATSat-1 retrieved winds using 3D-Var and 4D-Var method for the entire lifetime of tropical cyclone Ockhi as a case study. Results suggest that assimilation of SCATSat-1 winds has positive impact on Ockhi tropical cyclone prediction with slightly better forecast skill for 4D-Var technique. Moreover, assimilation of SCATSat-1 retrieved winds improved predictions of surface convergence and accumulated rainfall. In the last set of experiments, forecast sensitivity to observations are performed for SCATSat-1 retrieved different resolution winds using the WRF tangent linear and adjoint models for the entire lifetime of tropical cyclone Ockhi. Results suggest that SCATSat-1 retrieved winds have positive impact on tropical cyclone simulation.

Impact of Assimilation of Satellite Retrieved Ocean Surface Winds on the Tropical Cyclone Simulations Over the North Indian Ocean

“…They show that the impact of SCATSAT‐1 wind assimilation on track and intensity prediction is slightly better in 4DVAR than in 3DVAR. Our results are in line with the results of P. Kumar and Gairola (2019). Gao et al.…”

“…The decrement in the RMSE confirms that the assimilation is reducing the error in the analysis with respect to observations. P. Kumar and Gairola (2019) found that by assimilation of SCATSAT‐1 wind in the WRF model using a three‐dimensional variational data assimilation method (3DVAR), analysis bias (observation‐analysis, O‐A) becomes smaller than first‐guess bias (observation‐back ground, O‐B). The root mean square difference (RMSD) reduces from 2.04 to 1.23 m s −1 for zonal wind and 2.02 to 1.23 m s −1 for meridional wind.…”