{"title":"基于简单遗传算法(SGA)的台风模拟物理参数化方案组合优化","authors":"Zebin Lu, Jianjun Xu, Zhiqiang Chen, Jinyi Yang, Jeremy Cheuk-Hin Leung, Daosheng Xu, Banglin Zhang","doi":"10.1007/s13351-024-3105-2","DOIUrl":null,"url":null,"abstract":"<p>Each physical process in a numerical weather prediction (NWP) system may have many different parameterization schemes. Early studies have shown that the performance of different physical parameterization schemes varies with the weather situation to be simulated. Thus, it is necessary to select a suitable combination of physical parameterization schemes according to the variation of weather systems. However, it is rather difficult to identify an optimal combination among millions of possible parameterization scheme combinations. This study applied a simple genetic algorithm (SGA) to optimizing the combination of parameterization schemes in NWP models for typhoon forecasting. The feasibility of SGA was verified with the simulation of Typhoon Mujigae (2015) by using the Weather Research and Forecasting (WRF) model and Typhoon Higos (2020) by using the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) modeling system. The results show that SGA can efficiently obtain the optimal combination of schemes. For Typhoon Mujigae (2015), the optimal combination can be found from the 1,304,576 possible combinations by running only 488 trials. Similar results can be obtained for Typhoon Higos (2020). Compared to the default combination proposed by the COAWST model system, the optimal combination scheme significantly improves the simulation of typhoon track and intensity. This study provides a feasible way to search for the optimal combinations of physical parameterization schemes in WRF and COAWST for more accurate typhoon simulation. This can help provide references for future development of NWP models, and for analyzing the coordination and adaptability of different physical process parameterization schemes under specific weather backgrounds.</p>","PeriodicalId":48796,"journal":{"name":"Journal of Meteorological Research","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Combinatorial Optimization of Physics Parameterization Schemes for Typhoon Simulation Based on a Simple Genetic Algorithm (SGA)\",\"authors\":\"Zebin Lu, Jianjun Xu, Zhiqiang Chen, Jinyi Yang, Jeremy Cheuk-Hin Leung, Daosheng Xu, Banglin Zhang\",\"doi\":\"10.1007/s13351-024-3105-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Each physical process in a numerical weather prediction (NWP) system may have many different parameterization schemes. Early studies have shown that the performance of different physical parameterization schemes varies with the weather situation to be simulated. Thus, it is necessary to select a suitable combination of physical parameterization schemes according to the variation of weather systems. However, it is rather difficult to identify an optimal combination among millions of possible parameterization scheme combinations. This study applied a simple genetic algorithm (SGA) to optimizing the combination of parameterization schemes in NWP models for typhoon forecasting. The feasibility of SGA was verified with the simulation of Typhoon Mujigae (2015) by using the Weather Research and Forecasting (WRF) model and Typhoon Higos (2020) by using the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) modeling system. The results show that SGA can efficiently obtain the optimal combination of schemes. For Typhoon Mujigae (2015), the optimal combination can be found from the 1,304,576 possible combinations by running only 488 trials. Similar results can be obtained for Typhoon Higos (2020). Compared to the default combination proposed by the COAWST model system, the optimal combination scheme significantly improves the simulation of typhoon track and intensity. This study provides a feasible way to search for the optimal combinations of physical parameterization schemes in WRF and COAWST for more accurate typhoon simulation. This can help provide references for future development of NWP models, and for analyzing the coordination and adaptability of different physical process parameterization schemes under specific weather backgrounds.</p>\",\"PeriodicalId\":48796,\"journal\":{\"name\":\"Journal of Meteorological Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-03-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Meteorological Research\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1007/s13351-024-3105-2\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"METEOROLOGY & ATMOSPHERIC SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Meteorological Research","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s13351-024-3105-2","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
Combinatorial Optimization of Physics Parameterization Schemes for Typhoon Simulation Based on a Simple Genetic Algorithm (SGA)
Each physical process in a numerical weather prediction (NWP) system may have many different parameterization schemes. Early studies have shown that the performance of different physical parameterization schemes varies with the weather situation to be simulated. Thus, it is necessary to select a suitable combination of physical parameterization schemes according to the variation of weather systems. However, it is rather difficult to identify an optimal combination among millions of possible parameterization scheme combinations. This study applied a simple genetic algorithm (SGA) to optimizing the combination of parameterization schemes in NWP models for typhoon forecasting. The feasibility of SGA was verified with the simulation of Typhoon Mujigae (2015) by using the Weather Research and Forecasting (WRF) model and Typhoon Higos (2020) by using the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) modeling system. The results show that SGA can efficiently obtain the optimal combination of schemes. For Typhoon Mujigae (2015), the optimal combination can be found from the 1,304,576 possible combinations by running only 488 trials. Similar results can be obtained for Typhoon Higos (2020). Compared to the default combination proposed by the COAWST model system, the optimal combination scheme significantly improves the simulation of typhoon track and intensity. This study provides a feasible way to search for the optimal combinations of physical parameterization schemes in WRF and COAWST for more accurate typhoon simulation. This can help provide references for future development of NWP models, and for analyzing the coordination and adaptability of different physical process parameterization schemes under specific weather backgrounds.
期刊介绍:
Journal of Meteorological Research (previously known as Acta Meteorologica Sinica) publishes the latest achievements and developments in the field of atmospheric sciences. Coverage is broad, including topics such as pure and applied meteorology; climatology and climate change; marine meteorology; atmospheric physics and chemistry; cloud physics and weather modification; numerical weather prediction; data assimilation; atmospheric sounding and remote sensing; atmospheric environment and air pollution; radar and satellite meteorology; agricultural and forest meteorology and more.