Tropical cyclone Phailin Track Simulation using the advanced mesoscale Weather Research and Forecasting (WRF) model
Keywords:
Phailin, WRF Model, physics parameterizations, Cyclone track, Track errorAbstract
The Severe Cyclonic Storm Phailin caused extensive damage and loss of life in Odissa, India, during October
2013. The cyclone developed from a low pressure system that formed under the influence of an upper-air cyclonic
circulation in the Andaman Sea and intensified into a Severe Cyclonic Storm and crossed Odisha coast near Gopalpur
on 12 October, 2013. High surge (~2.3m) generated by the cyclone washed away some of the coastal structures
constructed at the Gopalpur port besides causing coastal erosion. Wind damage was quite extensive around 50km radius
of the cyclone track. The model domain consists of one coarse and two nested domains. The resolution of the coarse
domain is 45 km while the two nested domains have resolutions of 15 and 5 km, respectively. The results from the inner
most domain have been considered for analyzing and comparing the results. Model simulation outputs are compared
with corresponding observation data. The model was run for 72 hrs starting from 10 October, 2013 to 13 October 2013.
The track and intensity of simulated cyclone are compared with best track estimates provided by the Joint Typhoon
Warning Centre (JTWC) data. Simulations are performed using four convective cumulus parameterization schemes,
namely, BMJ (Betts-Miller-Janjic), GD (Grell-Devenyi), G3D (improved Grell-Denenyi) and KF (Kain-Fritsch) in
combination with different microphysics parameterization schemes, namely, Kessler Scheme, Lin et al. Scheme, WSM-3
scheme, WSM-5 scheme and Thompson Schemes. The main purpose of the present study is to find the best suitable
combination of microphysics, cumulus and PBL schemes for the simulation of accurate track of severe tropical cyclones
over Bay of Bengal. The cumulus, planetary boundary layer (PBL) and microphysics (MP) parameterization schemes
have more impact on the track and intensity prediction skill than the other parameterizations employed in the mesoscale
model.
