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The prediction skill of a numerical model can be enhanced by calibrating thesensitive parameters that significantly influence the model forecast. Theobjective of the present study is to improve the prediction of surface windspeed and precipitation by calibrating the Weather Research and Forecasting(WRF) model parameters for the simulations of tropical cyclones over the Bay ofBengal region. Ten tropical cyclones across different intensity categoriesbetween 2011 and 2017 are selected for the calibration experiments. Eightsensitive model parameters are calibrated by minimizing the prediction errorcorresponding to 10m wind speed and precipitation, using a multiobjectiveadaptive surrogate model-based optimization (MO-ASMO) framework. The 10m windspeed and precipitation simulated by the default and calibrated parametervalues across different aspects are compared. The results show that thecalibrated parameters improved the prediction of 10m wind speed by 17.62% andprecipitation by 8.20% compared to the default parameters. The effect ofcalibrated parameters on other model output variables, such as cyclone trackand intensities, and 500 hPa wind fields, is investigated. Eight tropicalcyclones across different categories between 2011 and 2018 are selected tocorroborate the performance of the calibrated parameter values for othercyclone events. Finally, the robustness of the calibrated parameters acrossdifferent boundary conditions and grid resolutions is also examined. Theseresults will have significant implications for improving the predictability oftropical cyclone characteristics. This allows us to better plan the adaptationand mitigation strategies and thus help in preventing the adverse effects onsociety.

journal of applied meteorology and climatology

WRF model parameter calibration to improve the prediction of tropical  cyclones over the Bay of Bengal using Machine Learning-based Multiobjective  Optimization