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

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 10m wind speed and precipitation, using a multiobjective adaptive surrogate model-based optimization (MO-ASMO) framework. The 10m 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 10m 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 parametervalues for other cyclone events. Finally, 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. This allows us to better plan the adaptation and mitigation strategies and thus help in preventing the adverse effects on society.

Optimization of numerical weather model parameterizations for solar irradiance prediction in the tropics

“…Various schemes are available for each option, and appropriate schemes are selected and used for accurate weather forecasting according to the target meteorological parameter, target region and other computational conditions. 15,16) Baki et al 17,18) Chinta et al 19,20) Di et al 21) and Mohanty et al 22) mentioned the impact of the different meteorological data used as initial and boundary conditions of the simulation to the simulation results and their accuracy, and pointed out that the performance of NWP models depends on the quality, reliability and representativeness of the initial values and lateral boundary conditions. 22) Consequently, seeking the appropriate combination of schemes for the parameterization option is important.…”

“…They pointed out the importance of setting appropriate schemes by showing the improved accuracy of the forecasting with the optimal combinations. The targets of Baki et al 17,18) Di et al 23,24) and Shenoy 25) are cyclones or typhoons. They selected wind speed and precipitation as objective variables, and found appropriate schemes for 8, 6 and 3 parameterization options, respectively.…”