Inter-comparison of the Impact of INSAT-3D Atmospheric Motion Vectors in 3DVAR and Hybrid Ensemble-3DVAR Data Assimilation Systems during Indian Summer Monsoon

Abstract The impact of observations may depend on various factors such as background error covariance in the data assimilation (DA) system. The present study compares the impact of INSAT-3D Atmospheric Motion Vector (AMV) observations in traditional three-dimensional variational (3DVAR) DA system and hybrid Ensemble Transform Kalman Filter (ETKF)-3DVAR DA system (HYBRID) available in Weather Research and Forecast (WRF) modeling system. The objective of the study is to understand how the impact of INSAT-3D AMV observations differ when assimilated using 3DVAR and HYBRID DA systems. The DA experiments are conducted over a ~ 4 week period of Indian Summer Monsoon Rainfall of July 2016. Four sets of experiments are performed with and without INSAT-3D AMV in both the DA systems. The domain-wide verification with respect to radiosonde observations reveals that forecasts in HYBRID experiments are more accurate than 3DVAR experiments, in general. Geographical distribution depicts the positive impacts of INSAT-3D AMV observations across the domain in both 3DVAR and HYBRID DA systems. The AMV observations show a larger relative impact in HYBRID than in 3DVAR. The relative improvement in HYBRID with AMV DA compared to 3DVAR is 77% and 71% for wind and tropical temperature. The skill scores for quantitative evaluation of precipitation forecast indicate a modest improvement in rainfall for HYBRID run and incorporating the AMV observation do not considerably enhance the skill of 24 h and 48 h rainfall forecast.