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Open AccessArticle

Improving WRF Typhoon Precipitation and Intensity Simulation Using a Surrogate-Based Automatic Parameter Optimization Method

1
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
2
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
3
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
*
Author to whom correspondence should be addressed.
Atmosphere 2020, 11(1), 89; https://doi.org/10.3390/atmos11010089
Received: 7 December 2019 / Revised: 5 January 2020 / Accepted: 8 January 2020 / Published: 10 January 2020
(This article belongs to the Special Issue Evaluation and Optimization of Atmospheric Numerical Models)
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. View Full-Text
Keywords: weather research and forecasting (WRF) model; parameter sensitivity analysis; parameter optimization; typhoon precipitation and intensity simulation weather research and forecasting (WRF) model; parameter sensitivity analysis; parameter optimization; typhoon precipitation and intensity simulation
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MDPI and ACS Style

Di, Z.; Duan, Q.; Shen, C.; Xie, Z. Improving WRF Typhoon Precipitation and Intensity Simulation Using a Surrogate-Based Automatic Parameter Optimization Method. Atmosphere 2020, 11, 89.

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