Performance of WRF-ARW model in real-time prediction of Bay of Bengal cyclone 'Phailin'

Pure Appl. Geophys. 173 (2016), 17831801 2015 Springer Basel

DOI 10.1007/s00024-015-1206-7 Pure and Applied Geophysics

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Web End = Performance of WRF-ARW model in real-time prediction of Bay of Bengal cyclone Phailin

M. MANDAL,1 K. S. SINGH,1 M. BALAJI,1 and M. MOHAPATRA2

AbstractThis study examines the performance of the Advanced Research core of Weather Research and Forecasting (ARW-WRF) model in prediction of the Bay of Bengal cyclone Phailin. The two-way interactive double-nested model at 27 and 9-km resolutions customized at Indian Institute of Technology Kharagpur (IITKGP) is used to predict the storm on real-time basis and ve predictions are made with ve different initial conditions. The initial and boundary conditions for the model are derived from the Global Forecasting System (GFS) analysis and forecast respectively. The track of storm is well predicted in all the ve forecasts. In particular, the forecast with less initial positional error led to more accurate track and landfall prediction. It is observed that the predicted peak intensity and translation speed of the storm depends strongly on initial intensity error, vertical wind shear and vertical distribution of maximum potential vorticity. The trend of intensication and dissipation of the storm is well predicted by the model in terms of central sea level pressure (CSLP). The intensity in terms of maximum surface wind (MSW) is under-predicted by the model and it is suggested that the MSW estimated from predicted pressure drop may be used as prediction guideline. The storm intensied rapidly during its passage over the high Tropical Cyclone Heat Potential zone and is reasonably well predicted by the model. Though the magnitude of the precipitation is not well predicted, distribution of precipitation is fairly well predicted by the model. The track and intensity of the storm predicted by the customized WRF-ARW is better than that of other NWP models. The landfall (time and position) is also better predicted by the model compared to other NWP models if initialized at cyclonic storm stage. The results indicate that the customized model have good potential for real-time prediction of...