Comparison of characteristics associated with super cyclonic storm 'Amphan' using numerical model WRF-ARW analysis and ERA5 reanalysis

The numerical simulation is performed to study the super cyclonic storm (SuCS) 'Amphan' by using the advanced research weathe r research and forecasting (WRF-ARW) model with 9 km resolution and in single domain while running WRF Pre Processing System (WPS) over the Bay of Bengal (BoB). The super cyclonic storm (SuCS) 'Amphan' originated over south Andaman Sea, which touched southeast BoB on 13th May 2020. The super cyclon ic storm (SuCS) 'Amphan' underwent rapid intensification into a very severe cyclonic storm (VSCS) on 17th May 2020 and subsequently converted into extremely severe cyclonic storm (ESCS) on 18th May 2020 and finally into a super cyclonic storm (SuCS) around 1200 UTC of 18th May 2020 till next 24 hours, before weakening into ESCS and laid down as a low-pressure region over north Bangladesh and neighbourhood around midnight of 21st May 2020. In this study, high resolution advanced research weather research and forecasting (WRF-ARW v4.1.2) with the combination of the Yonsei University (YSU) planetary boundary layer (PBL), Kain-Fritsch (KF) cumulus convection, and Ferrier microphysics scheme, is used for the simulation of SuCS 'Amphan'. Spatial distribution of w ind, potential vorticity (PV), and vertical integrated moisture transport (VIMT) are analysed for the intensity and the behaviour of the characteristics of SuCS 'Amphan'. Fifth generation of European center for medium-range weather forecast (ECMWF) atmospheric reanalysis of global climate (ERA5) resolves the atmosphere with 137 levels from the surface at the height of 80 km. ERA5 gives hourly estimations of a big number of atmospheric variables and covers the Earth on a 31 km grid. ERA5 observations are used for the validation of WRF-ARW model results. It is observed that spatial distribution of wind and vertical integrated moisture transport (VIMT), are well matched with the observations. The study is a prelude for sensitivity analysis and data assimilation using four-dimensional variational data assimilation (4DVAR) technique.