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.