Parameter Calibration to Improve the Prediction of Tropical Cyclones over the Bay of Bengal Using Machine Learning–Based Multiobjective Optimization

Abstract

Abstract The prediction skill of a numerical model can be enhanced by calibrating the sensitive parameters that significantly influence the model forecast. The objective of the present study is to improve the prediction of surface wind speed and precipitation by calibrating the Weather Research and Forecasting (WRF) Model parameters for the simulations of tropical cyclones over the Bay of Bengal region. Ten tropical cyclones across different intensity categories between 2011 and 2017 are selected for the calibration experiments. Eight sensitive model parameters are calibrated by minimizing the prediction error corresponding to 10-m wind speed and precipitation, using a multiobjective adaptive surrogate model-based optimization (MO-ASMO) framework. The 10-m wind speed and precipitation simulated by the default and calibrated parameter values across different aspects are compared. The results show that the calibrated parameters improved the prediction of 10-m wind speed by 17.62% and precipitation by 8.20% compared to the default parameters. The effect of calibrated parameters on other model output variables, such as cyclone track and intensities, and 500-hPa wind fields, is investigated. Eight tropical cyclones across different categories between 2011 and 2018 are selected to corroborate the performance of the calibrated parameter values for other cyclone events. The robustness of the calibrated parameters across different boundary conditions and grid resolutions is also examined. These results will have significant implications for improving the predictability of tropical cyclone characteristics, which allows us to better plan adaptation and mitigation strategies and thus help in reducing the adverse effects of tropical cyclones on society.