The Role of Diurnal Sea Surface Temperature Variability in Air-Sea Heat Fluxes and Tropical Cyclone Heat Potential in the Bay of Bengal

The role of spatial variability of sea surface temperature (SST) in Tropical Cyclone Heat Potential (TCHC) has been studied well and it has a direct impact on the distribution of turbulent heat fluxes.  However, the thermal structure of the upper ocean is also critical for cyclone formation and the influence of diurnal SST variability to TCHC remains an active area of research, particularly in warm ocean basins such as the Bay of Bengal (BOB), which accounts for many devastating cyclones globally.  This study intends to investigate the impact of diurnal SST variability on air-sea heat flux distribution and TCHP in the BOB, with the objective of improving the understanding of the pre-cyclone oceanic conditions.  The methodology incorporates a multi-dataset approach to capture the fine-scale temporal and spatial thermal structure of the upper ocean.  The Copernicus Global Ocean Physics Analysis and Forecast product is used to obtain sea temperature and sea surface height, which are employed to compute the depth of the 26° C isotherm—a key parameter for calculating TCHP.  To address the computational challenges associated with high-resolution datasets, a machine learning approach, a Convolution Neural Network (CNN) is framed to estimate TCHP.  Additionally, the inherent uncertainties are quantified using altimetry and SST observations from microwave imager data.  The  combination of multi-dataset approach is expected to provide a more accurate representaiton of diuarnal SST variablity and its influence on air-sea heat fluxes and TCHP.