Estimating the Influence of Cumulus Parametrization Schemes in Simulation of Super Cyclonic Storm ‘Amphan’ Over The Bay of Bengal Using WRF-ARW Model

Abstract

Tropical cyclone 'Amphan,' originating over the Bay of Bengal, made landfall as a well-defined low-pressure system across northern Bangladesh and the surrounding region around midnight on May 21, 2020. This research focuses on the influence of cumulus parameterization schemes (CPS) within the Advanced Research Weather Research Forecast (WRF-ARW) model for tropical cyclone 'Amphan.' Parameters such as maximum sustained wind (MSW), mean sea level pressure (MSLP), potential vorticity (PV), vertical integrated moisture transport (VIMT), and rainfall are investigated in this paper. This study employs the National Center for Environmental Prediction (NCEP)’s global operational analysis and prediction products at a 1°×1° resolution for initial and boundary conditions. The analyses compare different cumulus parameterization schemes Kain Fritsch (KF) Scheme, Betts-Miller-Janjic (BMJ), Grell-3, combined with the Yonsei University (YSU) planetary boundary layer (PBL) scheme and NCEP operational cloud microphysics scheme (Ferrier), against observed datasets from the Indian Meteorological Department (IMD) and ERA5 reanalysis datasets. Using the mass flux type scheme for cumulus parameterization, the results of MSW and MSLP with the Grell-3 scheme in WRF-ARW model are closely aligned with IMD observations during intense stages of tropical cyclone ‘Amphan’. The findings highlight the efficacy of the Grell-3 cumulus parameterization scheme in capturing the large-scale Latitude versus height structure of PV. This research investigates the vertically integrated moisture transport (VIMT) for the SuCS ‘Amphan’ and its correlation with rainfall over BoB from 16-21 May 2020 using WRF-ARW simulated datasets and ERA5 reanalysis datasets. The Grell-3 scheme exhibits notable differences in the area-averaged VIMT and corresponding rainfall. This research contributes to enhancing the understanding and prediction accuracy of tropical cyclones through comprehensive WRF-ARW model analyses.