INSAT-3D SST and its diurnal variability assessment using in-situ and MODIS observations

Abstract

Sea Surface Temperature is an Essential Climate Variable used in oceanographic and meteorological application studies such as air-sea interaction, ocean mixing, boundary layer processes, and ocean state forecast, which require high temporal resolution SST. Indian geostationary satellite INSAT-3D imager has been designed to provide such information at 30 min intervals, 4 km spatial resolution along with several other parameters over the North Indian Ocean. In this study, the 30-minute interval INSAT-3D SST and its diurnal variability are validated (inter-compared) with collocated in-situ buoy measurements and compared with contemporary MODerate-resolution Imaging Spectroradiometer SST observations. A reasonable agreement exists between the INSAT-3D and in situ data with a correlation coefficient of 0.70 and Root Mean Square Error (RMSE) of 1.28 °C. This relationship is better for the daytime observations and during the pre and post-monsoon seasons, while the relationship is relatively weaker for the nighttime and monsoon seasons with remarked imprints from convective processes in the upper oceans. For the Arabian Sea and Bay of Bengal regions, the INSAT-3D SST shows better correlation and negative bias in comparison with in situ observations, while it is weaker with positive bias for the Equatorial Indian Ocean. Inter-comparison with MODIS Aqua/Terra day and night passes SST also shows a similar relationship, however with a much-improved correlation coefficient (∼0.95) and highly consistent in spatial and temporal variability with a cool bias of 0.14 °C. INSAT-3D SST shows a significantly high average diurnal temperature difference of 2.24 °C, whereas buoys show 0.54 °C. These high RMSE and diurnal variability results suggest that INSAT-3D SST retrieval can be improved to study the many oceanographic processes such as eddies, fronts, and estimation of air-sea fluxes. Therefore, this study helps to improve the retrieval algorithm of INSAT-3D SST, by developing the region-specific regression coefficients along with data merging and assimilation techniques.