Land surface temperature from INSAT-3D imager data: Retrieval and assimilation in NWP model

A new algorithm is developed for retrieving the land surface temperature (LST) from the imager radiance observations on board geostationary operational Indian National Satellite (INSAT-3D). The algorithm is developed using the two thermal infrared channels (TIR1 10.3-11.3 mu m and TIR2 11.5-12.5 mu m) via genetic algorithm (GA). The transfer function that relates LST and thermal radiances is developed using radiative transfer model simulated database. The developed algorithm has been applied on the INSAT-3D observed radiances, and LST retrieved from the developed algorithm has been validated with Moderate Resolution Imaging Spectroradiometer land surface temperature (LST) product. The developed algorithm demonstrates a good accuracy, without significant bias and standard deviations of 1.78 K and 1.41 K during daytime and nighttime, respectively. The newly proposed algorithm performs better than the operational algorithm used for LST retrieval from INSAT-3D satellite. Further, a set of data assimilation experiments is conducted with the Weather Research and Forecasting (WRF) model to assess the impact of INSAT-3D LST on model forecast skill over the Indian region. The assimilation experiments demonstrated a positive impact of the assimilated INSAT-3D LST, particularly on the lower tropospheric temperature and moisture forecasts. The temperature and moisture forecast errors are reduced ( as large as 8-10%) with the assimilation of INSAT-3D LST, when compared to forecasts that were obtained without the assimilation of INSAT-3D LST. Results of the additional experiments of comparative performance of two LST products, retrieved from operational and newly proposed algorithms, indicate that the impact of INSAT-3D LST retrieved using newly proposed algorithm is significantly larger compared to the impact of INSAT-3D LST retrieved using operational algorithm.