Improving the prediction of monsoon depressions by assimilating ASCAT soil moisture in NCUM-R modeling system

The present study investigates the impact of assimilation satellite derived soil wetness data from Advanced Scatterometer (ASCAT) on simulation inland moving monsoon depressions (MDs) over Indian region using high resolution NCUM-R forecasting system. simplified Extended Kalman Filter (sEKF) based Land Surface Data Assimilation (LSDA) system is used to create land surface moisture initial conditions for by assimilating ASCAT (SM) along with screen level observations. Two numerical experiments, namely CTL (incorporating only observations in LSDA) and (assimilating both SM are carried out update model conditions. Identical atmospheric condition prepared considering 4DVAR technique experiments. results this show that remotely sensed has a beneficial MD simulation, its movement, structure associated precipitation. analysis increment fluxes experiment clearly shows properly coupled positive feedback between atmosphere. vicinity considerably improved as compared well correlated observed rainfall regions. root mean square error top layer decreased 5 15% analyses analyses. land-atmosphere coupling investigated Dirmeyer's indices, it noticed precipitation modulated latent heat flux higher than simulation. This improvement due proper representation into LSDA. contribution individual terms vorticity equation total tendency represented during various phases MD. Overall, indicates use preparation helps improve forecast MDs regional NWP