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Improving WRF Typhoon Precipitation and Intensity Simulation Using a Surrogate-based Automatic Parameter Optimization Method
狄振华
北京师范大学
The typhoon precipitation and intensity forecasting plays an important role in disaster prevention and mitigation for the typhoon landfall area. However, the issue on improving forecast accuracy is very challenging. In this study, the Weather Research and Forecasting (WRF) model typhoon simulations on precipitation and central 10-m maximum wind speed (10-m wind) were improved using a systematic parameter optimization framework that consists of parameter screening and adaptive surrogate modelling-based optimization (ASMO) for screened sensitive parameters. Results showed that six of twenty-five adjustable parameters from seven physics of WRF model were screened by the Multivariate Adaptive Regression Spline (MARS) parameter sensitivity analysis. Then, the six parameters were optimized by ASMO method, and after 178 runs, the 6-hourly precipitation and 10-m wind simulations were finally improved by 6.83% and 13.64%, respectively. The significant improvements usually occurred where the maximum precipitation or the highest wind speed. The additional typhoon events from other years were simulated to validate the reasonability of the WRF optimal parameters. The results demonstrated that the improvements for 6-hourly precipitation and 10-m wind were 4.78% and 8.54%, respectively. Overall, the ASMO optimization method is an effective and highly efficient way to improve typhoon precipitation and intensity simulation by a numerical weather prediction model.