Quantifying a set of suitable physics parameterization schemes for the Weather Research and Forecasting (WRF) model is essential for obtaining highly accurate typhoon forecasts. In this study, a systematic Tukey-based combinatorial optimization method was proposed to determine the optimal physics schemes of the WRF model for 15 typhoon simulations over the Northwest Pacific Ocean, covering all available schemes of microphysics (MP), cumulus (CU), and planetary boundary layer (PBL) physical processes. Results showed that 284 scheme combination searches were sufficient to find the optimal scheme combinations for simulations of track (km), central sea level pressure (CSLP, hPa), and 10 m maximum surface wind (10-m wind, m s⁻¹), compared with the 700 sets of full combinations (i.e., 10 MP × 7 CU × 10 PBL). The decrease in the typhoon simulation error (i.e., root mean square error between simulation and observations) with this optimal scheme combination was 34%, 33.92%, and 25.67% for the track, CSLP, and 10-m wind, respectively. Overall, the results demonstrated that the optimal scheme combination yields reasonable results, and the Tukey-based optimization method is very effective and efficient in terms of computational resources.

Typhoon precipitation and intensity forecasting plays an important role in disaster prevention and mitigation in the typhoon landfall area. However, the issue of 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 consisting of parameter screening and adaptive surrogate modeling-based optimization (ASMO) for screening sensitive parameters. Six of the 25 adjustable parameters from seven physics components of the WRF model were screened by the Multivariate Adaptive Regression Spline (MARS) parameter sensitivity analysis tool. Then the six parameters were optimized using the 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 most significant improvements usually occurred with the maximum precipitation or the highest wind speed. Additional typhoon events from other years were simulated to validate that the WRF optimal parameters were reasonable. The results demonstrated that the improvements in 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 using a numerical weather prediction model.