Study on the performance of WRF 4DVAR with GSMaP_NOW rainfall assimilation in forecasting heavy rainfall over the Maritime Continent

Abstract

This study evaluated the performance of the Weather Research and Forecasting (WRF) model with Four-Dimensional Variational (4DVar) data assimilation using the Global Satellite Mapping of Precipitation (GSMaP_NOW). Several verification metrics, including the root mean square error (RMSE), bias Score, equitable threat score (ETS), - fractional bias score (FBS), and fractional skill score (FSS) were employed in the assessment. The results demonstrated that 4DVar improved the accuracy of vertical velocity and specific humidity predictions at mid and upper levels, as well as the enhanced heavy rainfall forecasting. Spatially, 4DVar was able to increase specific humidity and vertical velocity in lowland areas, leading to higher rainfall in those regions. Future studies should investigate the assimilation of additional conventional and satellite observations to further enhance forecast accuracy.

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