Prediction of Extremely Severe Cyclonic Storm “Fani” Using Moving Nested Domain - Kuvar Satya Singh; Sridhara Nayak; Suman Maity; Hara Prasad Nayak; Soma Dutta (Atmosphere, 2023)
The prediction of extremely severe cyclonic storms has been a long-standing and challenging issue due to their short life period and large variation in intensities over a short time. In this study, we predict the track, intensity, and structure of an extremely severe cyclonic storm (ESCS) named ‘Fani,’ which developed over the Bay of Bengal region from 27 April to 4 May 2019, using the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) model. Two numerical experiments were conducted using the moving nested domain method with a 3 km horizontal resolution, one with the FLUX-1 air-sea flux parameterization scheme and the other with the FLUX-2 air-sea flux parameterization scheme. The NCEP operational Global Forecast System (GFS) analysis and forecast datasets with a 25 km horizontal resolution were used to derive the initial and boundary conditions. The WRF model’s predicted track and intensity were validated with the best-fit track dataset from the India Meteorological Department (IMD), and the structure was validated with different observations. The results showed that the WRF model with the FLUX-1 air-sea parameterization scheme accurately predicted the track, landfall (position and time), and intensity (minimum sea level pressure and maximum surface wind) of the storm. The track errors on days 1 to 4 were approximately 47 km, 123 km, 96 km, and 27 km in the FLUX-1 experiment and approximately 54 km, 142 km, 152 km, and 166 km in the FLUX-2 experiment, respectively. The intensity was better predicted in the FLUX-1 experiment during the first 60 h, while it was better predicted in the FLUX-2 experiment for the remaining period. The structure, in terms of relative humidity, water vapor, maximum reflectivity, and temperature anomaly of the storm, was also discussed and compared with available satellite and Doppler Weather Radar observations.