Impact of <scp>INSAT</scp>‐<scp>3D</scp> land surface temperature assimilation via simplified extended Kalman filter‐based land data assimilation system on forecasting of surface fields over India

Abstract The land surface temperature (LT) is a crucial variable that governs the energy and radiation budget of the earth's atmosphere and influences land‐atmosphere interactions. The LT plays a crucial role mainly in the short‐range forecast of a numerical weather prediction (NWP) model. The primary research goal in this research work undertaken is to assess the impact of assimilation of LT data from the Indian satellite (INSAT‐3D) into the NCMRWF global NWP model (NCUM) through a simplified Extended Kalman Filter (sEKF) land data assimilation system (LDAS), particularly important as there are few screen‐level observations over the region. A dedicated stand‐alone pre‐processing system has been designed to prepare LT observations in a compatible format for the land surface assimilation system. The approach for LT data assimilation from the INSAT‐3D satellite reduces the uncertainty associated with the initial state of LT analysis while simultaneously improving the accuracy of forecasts of near surface atmospheric variables. An observing system experiment (OSE) was carried out during both the summer (May) and winter (February) months by assimilating the INSAT‐3D LT data in a coupled land‐atmosphere analysis‐forecast system. The results obtained from the OSE demonstrate that the use of INSAT‐3D LT data improves the forecast skill of both maximum and minimum temperature over India, particularly in areas characterized by higher LT variability. The improvement is pronounced in forecasts of maximum (minimum) temperature during “Boreal” summer (“Boreal” winter) season. The verification scores also indicate that the incorporation of INSAT LT data substantially improves the NCUM model's forecast performance. By assimilating LT, the mean error of maximum and minimum temperature forecasts in India was decreased, accompanied by enhanced forecast accuracy within a time frame of approximately 24 h. The scores for the verification measures, specifically the Probability of Detection (POD), demonstrate a ~15% improvement in both the forecasts for maximum and minimum temperatures. This improves the temperature prediction as well as the ability to forecast intense weather episodes like cold spells and heat waves.