The present study is aimed to investigate sub-surface ocean processes and their contribution to the intensification of a tropical cyclone (TC) from a coupled-modeling perspective. The Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model was employed to simulate TC Phailin, which originated over the Bay of Bengal and made landfall on the eastern coast of India in October 2013. Three sub-surface ocean condition datasets—viz., (a) the European Centre for Medium-Range Weather Forecast (ECMWF) Ocean Reanalysis, (b) the Climate Forecast System Version 2 (CFSV2) Operational Analysis, and (c) the Hybrid Coordinate Ocean Model (HYCOM) Reanalysis datasets—were used for the initial and boundary conditions for the oceanic component of the coupled model in three different simulations of TC Phailin. All the simulations showed a delay in intensification compared to the observation, and ECMWF simulated the most intensified TC. CFSV2 simulated a deeper mixed layer (ML) and higher mixing, which hindered the intensification. Furthermore, higher entrainment of cold water in the ML led to cold water reaching the surface and, consequently, decreased sea surface temperature, which acted as negative feedback in the intensification of the storm in the cases of CFSV2 and HYCOM. ECMWF realistically simulated the interactions of the TC with a cold-core eddy before landfall. A sudden increase in ML heat content, the addition of heat in the ML due to entrainment, and the prevention of cold water reaching the surface were indicative of the breaking of the barrier layer (BL) in ECMWF, which was further corroborated by the spatial distribution of BL thickness in the simulation. This acted as positive feedback in the intensification of the TC. The findings of this study strongly suggest that not only the incorporation of physical oceanic sub-surface processes in the modeling of TCs but also the proper representation of prevailing mesoscale features and ocean sub-surface temperature, salinity, and current profiles in datasets is essential for realistic simulations of TCs.

The present study is aimed to investigate the rainfall characteristics of Monsoon Deep Depressions (MDD) originating over the Bay of Bengal (BoB) basin using a coupled ocean-atmospheric model (COAWST) and a stand-alone atmospheric (WRF) model with a lead time of up to 72h. It is found that though the tracks of the four MDDs considered in the study have been reasonably simulated, the intensity was overestimated in both sets of simulations compared to India Meteorological Department (IMD) best estimates. Upon decomposition of the contributors to the rainrate for the composite of the storms in the deep depression (DD) phase, it was found that the moisture sources/sinks play a more important role than the cloud sources/sinks in modulating the rainfall processes. Further analysis of the moisture sources/sinks showed that the horizontal and vertical advection are the major drivers in modulating the contribution of the moisture sources/sinks. The validation of rainfall using CMORPH datasets suggested that the coupled simulations had a higher skill in rainfall prediction. Furthermore, the composite of different components of moisture sources/sinks (especially vertical advection) was found to be more realistically simulated in COAWST compared to WRF upon validation with MERRA datasets. Analysis of the composite energetics showed that scarcity of bulk kinetic energy in the later hours of the DD phase in COAWST led to the dissipation of the storm core, which led to better prediction of rainfall. On the other hand, a re-intensification of the storm core by means of condensational heating led to an overestimation of rainfall in WRF, which finally resulted in lower skill in rainfall prediction. In spite of the stand-alone atmospheric model capturing the horizontal moisture incursion in the lower levels significantly, the better representation of the vertical structure enabled the coupled model to capture the precipitation features more realistically, increasing skill in rainfall prediction.