Hybrid Assimilation of Satellite Rainfall Product with High Density Gauge Network to Improve Daily Estimation: A Case of Karnataka, India

Accurate rainfall estimation during the Indian summer monsoon (ISM) is one of most crucial activities in and around Sub-continent. Japan Aerospace Exploration Agency (JAXA) provides a couple Global Satellite Mapping Precipitation (GSMaP) products, namely, GSMaP_MVK, which satellite-based product calculated with ancillary data including global objective analysis data, GSMaP_Gauge, adjusted by rain gauges. In this study, daily amount from GSMaP (version 7) validated against dense gauge network over Karnataka, southwestern states India, ISM 2016–2018. Furthermore, as primary these observations are assimilated using hybrid assimilation method to improve final estimate. The combination two-dimensional variational (2D-Var) Kalman filter, 2D-Var utilized merge filter applied update background error method. Preliminary verification results suggest that GSMaP_Gauge has sufficient skill north interior Karnataka south regions, large errors orographic heavy region Western Ghats. These larger GSMaP_MVK regions. Hybrid randomly selected products when compared independent observations. improvements more prominent shows improvement due absence adjustment JAXA operational processing. superiority Cressman optimal interpolation methods for impacts numbers also presented present study.