Prediction of severe tropical cyclones over the Bay of Bengal during 2007–2010 using high-resolution mesoscale model
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In this paper, the performance of a high-resolution mesoscale model for the prediction of severe tropical cyclones over the Bay of Bengal during 2007–2010 (Sidr, Nargis, Aila, and Laila) is discussed. The advanced Weather Research Forecast (WRF) modeling system (ARW core) is used with a combination of Yonsei University PBL schemes, Kain-Fritsch cumulus parameterization, and Ferrier cloud microphysics schemes for the simulations. The initial and boundary conditions for the simulations are derived from global operational analysis and forecast products of the National Center for Environmental Prediction-Global Forecast System (NCEP-GFS) available at 1°lon/lat resolution. The simulation results of the extreme weather parameters such as heavy rainfall, strong wind and track of those four severe cyclones, are critically evaluated and discussed by comparing with the Joint Typhoon Warning Center (JTWC) estimated values. The simulations of the cyclones reveal that the cyclone track, intensity, and time of landfall are reasonably well simulated by the model. The mean track error at the time of landfall of the cyclone is 98 km, in which the minimum error was found to be for the cyclone Nargis (22 km) and maximum error for the cyclone Laila (304 km). The landfall time of all the cyclones is also fairly simulated by the model. The distribution and intensity of rainfall are well simulated by the model as well and were comparable with the TRMM estimates.
KeywordsTropical cyclone Track Intensity Vector displacement error Model simulations
The authors sincerely acknowledge NCEP for providing the global analysis and forecast fields, NASA for precipitation data; the track and intensity were furnished by JTWC. We thank Dr. Dev Niyogi of Purdue State University, USA, for his useful suggestion and two anonymous reviewers for their valuable comments on the manuscript. We also thank Mr. A. R. Subbiah, Director, RIMES, for the institutional support.
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