Influence of coastal land–water–atmosphere interactions on tropical cyclone intensity over the Bay of Bengal

Jawad, an anomalous post-monsoon tropical cyclone (TC) that originated over the Bay of Bengal (BoB), renewed the challenges encountered in forecasting TCs by operational agencies, as the TC behaved contrarily in terms of predicted track and intensity. The novelty of this study is, for the first time, the impacts of the regional coastal land and ocean processes along with large-scale features are comprehensively examined that are regulated by the TC characteristics with thrust on its track and intensity. A total of four experiments, i.e., two Land Use Land Cover (LULC) datasets, viz., (1) United States Geological Survey (USGS), and (2) Indian Space Research Organization (ISRO) Advanced Wide Field Sensor (AWiFS) and two sea surface temperature (SST) datasets (i.e., INCOIS and default GFS) are tested to investigate the coastal processes using the Weather Research and Forecasting (WRF) model at a horizontal resolution of 3 km. The simulations are carried out with a lead time up to 96 h. Results suggested that ISRO LULC along with default SST experiment named as ISR (INCOIS SST and ISRO LULC named as INS) has the highest (lowest) forecast skills, i.e., track, intensity and precipitation. The vertical structures of specific humidity and the location of the updraft in ISRO LULC are associated with an accurate representation of latent heat flux (LHF) near the coast, enabling better intensity estimates of the cyclone. On the contrary, the overestimation of LHF led to the overestimation of intensity in INS. The results also highlighted that the land–atmospheric coupling index and storm energetics are better represented by ISR to support the realistic modulation of the intensification and associated convective processes. Interestingly, it is also noted that the effect of LULC on the energy exchanges between the surface and lower atmosphere in the coastal region is overridden by the effect of SST leading to poor results for INC (INCOIS SST and USGS LULC); therefore, choice of appropriate combination of LULC and SST is equally important for better forecast skills. This study demonstrates that an optimal combination of region-specific LULC and SST data is essential for accurate cyclone intensity prediction, particularly near the coastal region.