Engineering topics
Publications and source records attributed to Bijoy Vengasseril Thampi.
The motivation for this study is to develop a Machine learning methodology for the determination of CERES clear-sky scenes and the estimation of clear-sky TOA fluxes from CERES-TOA radiance measurements without using any imager information.
Objective - It is observed that 5.6% of all CERES Terra/Aqua data contains missing cloud cover information or insufficient imager data for a reliable scene identification (some times it can reach up to 50% of data for a specific scene type) - The unavailability of imager data lead to gaps in global radiation budget dataset. - In this study, our objective is to develop a Machine learning methodology for the improved determination of CERES scene type and subsequent clear-sky TOA flux estimation using standalone CERES TOA radiance measurements (without any MODIS/Imager data).