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【目的】热带气旋降水模拟评估是热带气旋预警和风险评估的基础。针对热带气旋降水模拟方法差异不清等问题，【方法】通过文献分析，从热带气旋数值天气预报模型、统计模型、物理模型和机器学习模型角度，系统综述了四类模型的特点、进展、适用性以及代表模型，并进行对比分析，最后对四类模型的发展做出建议与展望。【结果】结果表明：数值天气预报模型模拟信度较高，适用于热带气旋降水的预报工作；统计模型能够构建大量仿真热带气旋，适用于热带气旋降水重现期的估计；物理模型能在简化计算的基础上，较好地解释热带气旋降水物理机制；机器学习模型灵活性强，能和其他模型组合使用，具有较高的发展潜力。【结论】未来除进一步完善相关模型外，还需加强降水与次生灾害之间的协同以及新技术在降水模拟上的应用，实现热带气旋降水的快速与精准预估，更好地为区域热带气旋预警和风险防范提供支撑。
Abstract：[Objective]The modelling and assessment of tropical cyclone precipitation serves as the foundation for tropical cyclone warning and risk evaluation. This paper aims to address issues such as unclear differences in tropical cyclone precipitation modelling method.[Methods]Through literature review, this paper provides a systematic review of the characteristics, progress, applicability, and representative models of four types of models, including numerical weather prediction models, statistical models, physical models, and machine learning models. A comparative analysis is conducted, followed by suggestions and prospects for the development of these four types of models.[Results]The results show that numerical weather prediction models have high reliability and are suitable for forecasting tropical cyclone precipitation. Statistical models can generate numerous simulated tropical cyclones, making them suitable for estimating precipitation return periods. Physical models, based on simplified calculations, provide a good explanation for the mechanisms of tropical cyclone precipitation. Machine learning models exhibit strong flexibility and can be integrated with other models, showing significant potential for future development.[Conclusion]In the future, in addition to further improving relevant models, it is essential to strengthen the synergy between precipitation and secondary disasters, as well as the application of new technologies in precipitation modelling. This can enable rapid and accurate estimation of tropical cyclone precipitation, providing better support for regional tropical cyclone warning and risk prevention.
[1] 陶凯，廖新利，苏鹏，等.热带气旋多致灾因子综合风险评估研究进展[J].水利水电技术(中英文),2024,55(9):1-9.TAO Kai,LIAO Xinli,SU Peng,et al.Progress in multi-hazard risk assessment for tropical cyclone[J].Water Resources and Hydropower Engineering,2024,55(9):1-9.
[2] LUITEL B,VILLARINI G,VECCHI G A.Verification of the skill of numerical weather prediction models in forecasting rainfall from U.S.landfalling tropical cyclones[J].Journal of Hydrology,2018,556:1026-1037.
[3] FRAME D J,WEHNER M F,NOY I,et al.The economic costs of Hurricane Harvey attributable to climate change[J].Climatic Change,2020,160(2):271-281.
[4] BLACK JR T E,GRAHAM G S,BLACK S P,et.al.Hurricane Harvey:National Hurricane Center Tropical Cyclone Report[R].Washington,DC:National Oceanic and Atmospheric Administration,2018.
[5] WING O E J,SAMPSON C C,BATES P D,et al.A flood inundation forecast of Hurricane Harvey using a continental-scale 2D hydrodynamic model[J].Journal of Hydrology X,2019,4:100039.
[6] CHANG Y,WANG J,LI S,et al.A comprehensive review on the modeling of tropical cyclone boundary layer wind field[J].Physics of Fluids,2024,36(3):035165.
[7] YAN D,ZHANG T.Research progress on tropical cyclone parametric wind field models and their application[J].Regional Studies in Marine Science,2022,51:102207.
[8] 岳彩军.Q矢量、螺旋度、位涡及位涡反演在台风暴雨研究中的应用进展[J].暴雨灾害，2014,33(3):193-201.YUE Caijun.Progress in application study of Q vector,helicity,potential vorticity and its inversion to torrential rainfall associated with typhoon [J].Torrential Rain and Disasters,2014,33(3):193-201.
[9] LAMERS A,DEVI.S S,SHARMA M,et al.Forecasting tropical cyclone rainfall and flooding hazards and impacts[J].Tropical Cyclone Research and Review,2023,12(2):100-112.
[10] 程正泉，陈联寿，徐祥德，等.近10年中国台风暴雨研究进展[J].气象，2005(12):3-9.CHENG Zhengquan,CHEN Lianshou,XU Xiangde,et al.Progress in studies on typhoon heavy rainfalls in China in the recent decade [J].Meteorological Monthly,2005(12):3-9.
[11] 李颖，方伟华.热带气旋降水模拟研究进展[J].地理科学进展，2013,32(4):606-617.LI Ying,FANG Weihua.Progress in simulation studies of tropical cyclone precipitation [J].Progress in Geographic,2013,32(4):606-617.
[12] WANG Z,ZHAO J,HUANG H,et al.A review on the application of machine learning methods in tropical cyclone forecasting[J].Frontiers in Earth Science,2022,10:902596.
[13] YI L,PEIYAN C,HUI Y,et al.Parameterized tropical cyclone precipitation model for catastrophe risk assessment in China[J].Journal of Applied Meteorology and Climatology,2022,61(9):1291-1303.
[14] LIU Y,LI L,LIU Y,et al.Estimation of precipitation induced by tropical cyclones based on machine-learning-enhanced analogue identification of numerical prediction[J].Meteorological Applications,2021,28(2):e1978.
[15] YANG J,DUAN Z,CHEN Y,et al.Assessing parametric rainfall models in reproducing tropical cyclone rainfall characteristics[J].Atmospheric Research,2023,288:106726.
[16] XI D,LIN N,SMITH J.Evaluation of a physics-based tropical cyclone rainfall model for risk assessment[J].Journal of Hydrometeorology,2020,21(9):2197-2218.
[17] SCHULTZ M G,BETANCOURT C,GONG B,et al.Can deep learning beat numerical weather prediction?[J].Philosophical Transactions of the Royal Society A:Mathematical,Physical and Engineering Sciences,2021,379(2194):20200097.
[18] NEDERHOFF K,VAN ORMONDT M,VEERAMONY J,et al.Accounting for uncertainties in forecasting tropical-cyclone-induced compound flooding[J].Geoscientific Model Development,2024,17(4):1789-1811.
[19] 任福民，杨慧.1949年以来我国台风暴雨及其预报研究回顾与展望[J].暴雨灾害，2019,38(5):526-540.REN Fumin,YANG Hui.An overview of advances in typhoon rainfall and its forecasting researches in China during the past 70 years and future prospects [J].Torrential Rain and Disasters,2019,38(5):526-540.
[20] National Centers for Environmental Prediction.Global Forecast System model[CP/OL].[2024-04-17].https://www.ncei.noaa.gov/products/weather-climate-models/global-forecast.
[21] European Centre for Medium-Range Weather Forecasts (ECMWF).European Centre for Medium-Range Weather Forecasts model[CP/OL].[2024-04-17].https://www.ecmwf.int/.
[22] SAITO K,FUJITA T,YAMADA Y,et al.The operational JMA nonhydrostatic mesoscale model[J].Monthly Weather Review,2006,134(4):1266-1298.
[23] FAURE G,CHAMBON P,BROUSSEAU P.Operational implementation of the AROME model in the tropics:Multiscale validation of rainfall forecasts[J].Weather and Forecasting,2020,35(2):691-710.
[24] Geophysical Fluid Dynamics Laboratory.Geophysical Fluid Dynamics Laboratory model[CP/OL].[2024-04-17].https://www.gfdl.noaa.gov/model-development/.
[25] Colorado State University.Regional Atmospheric Modeling System[CP/OL].[2024-04-17].https://rams.atmos.colostate.edu/rams-description.html.
[26] Pennsylvania State University.The PSU/NCAR mesoscale model[CP/OL].[2024-04-17].https://a.atmos.washington.edu/～ovens/newwebpage/mm5-home.html.
[27] National Oceanic and Atmospheric Administration,National Centers for Environmental Prediction.The Rapid Update Cycle model[CP/OL].(2024-01-24).https://ruc.noaa.gov/ruc/.
[28] The Earth Observing Laboratory.DYNAMO model[CP/OL].[2024-04-17].https://atmos.uw.edu/～houze/DYNAMO-AMIE/.
[29] FREITAS S R,PANETTA J,LONGO K M,et al.The Brazilian developments on the regional atmospheric modeling System (BRAMS 5.2):An integrated environmental model tuned for tropical areas[J].Geoscientific Model Development,2017,10(1):189-222.
[30] National Centre for Meteorological Research.Aire Limitée Adaptation dynamique Développement InterNational model[CP/OL].(2023-01-24).https://www.umr-cnrm.fr/spip.php?article125&lang=fr.
[31] Finnish Meteorological Institute.High Resolution Limited Area Model[CP/OL].[2024-04-17].https://registry.opendata.aws/hirlam/.
[32] NADIMPALLI R,OSURI K K,MOHANTY U C,et al.Forecasting tropical cyclones in the Bay of Bengal using quasi-operational WRF and HWRF modeling systems:an assessment study[J].Meteorology and Atmospheric Physics,2020,132(1):1-17.
[33] HSIAO L F,CHEN D S,HONG J S,et al.Improvement of the numerical tropical cyclone prediction system at the weather bureau of Taiwan:TWRF (Typhoon WRF)[J].Atmosphere,2020,11(6):657.
[34] CHAN S T,FONG C H,CHAN M Y.An analog forecast system for precipitation forecasting[C]//Hong Kong Observatory.28th Guangdong-Hong Kong-Macao Seminar on Meteorological Science and Technology.Hong Kong:Hong Kong Observatory,2014.
[35] EMANUEL K,SUNDARARAJAN R,WILLIAMS J.Hurricanes and global warming:Results from downscaling IPCC AR4 simulations[J].Bulletin of the American Meteorological Society,2008,89(3):347-368.
[36] EMANUEL K A.Downscaling CMIP5 climate models shows increased tropical cyclone activity over the 21st century[J].Proceedings of the National Academy of Sciences,2013,110(30):12219-12224.
[37] 方伟华，石先武.面向灾害风险评估的热带气旋路径及强度随机模拟综述[J].地球科学进展，2012,27(8):866-875.FANG Weihua,SHI Xianwu.A review of stochastic modeling of tropical cyclone track and intensity for disaster risk assessment[J].Advances in Earth Science,2012,27 (8):866875.
[38] ZHU L,FRAUENFELD O W,QUIRING S M.Seasonal tropical cyclone precipitation in Texas:A statistical modeling approach based on a 60 year climatology[J].Journal of Geophysical Research:Atmospheres,2013,118(16):8842-8856.
[39] ZHANG W,LUO M,GAO S,et al.Compound hydrometeorological extremes:drivers,mechanisms and methods[J].Frontiers in Earth Science,2021,9:673495.
[40] TULEYA R E,DEMARIA M,KULIGOWSKI R J.Evaluation of GFDL and Simple Statistical Model Rainfall Forecasts for U.S.Landfalling Tropical Storms[J].Weather and Forecasting,2007,22(1):56-70.
[41] LONFAT M,MARKS F D,CHEN S S.Precipitation distribution in tropical cyclones using the tropical rainfall measuring mission (TRMM) microwave imager:A global perspective[J].Monthly Weather Review,2004,132(7):1645-1660.
[42] LONFAT M,ROGERS R,MARCHOK T,et al.A parametric model for predicting hurricane rainfall[J].Monthly Weather Review,2007,135(9):3086-3097.
[43] GEOGHEGAN K M,FITZPATRICK P,KOLAR R L,et al.Evaluation of a synthetic rainfall model,P-CLIPER,for use in coastal flood modeling[J].Natural Hazards,2018,92(2):699-726.
[44] BAGTASA G.Analog forecasting of tropical cyclone rainfall in the Philippines[J].Weather and Climate Extremes,2021,32:100323.
[45] KIM J S,CHEN A,LEE J,et al.Statistical prediction of typhoon-induced rainfall over China using historical rainfall,tracks,and intensity of typhoon in the Western North Pacific[J].Remote Sensing,2020,12(24):4133.
[46] REN F,DING C,ZHANG D L,et al.A dynamical-statistical-analog ensemble forecast model:Theory and an application to heavy rainfall forecasts of landfalling tropical cyclones[J].Monthly Weather Review,2020,148(4):1503-1517.
[47] Interagency Performance Evaluation Task Force (IPET).Performance evaluation of the New Orleans and Southeast Louisiana Hurricane Protection System draft final report of the Interagency Performance Evaluation Task Force volume VIII - engineering and operational risk and reliability analysis[R].USA:Interagency Performance Evaluation Task Force (IPET),2006.
[48] ZHU L,QUIRING S M,EMANUEL K A.Estimating tropical cyclone precipitation risk in Texas[J].Geophysical Research Letters,2013,40(23):6225-6230.
[49] LANGOUSIS A,VENEZIANO D.Theoretical model of rainfall in tropical cyclones for the assessment of long-term risk[J].Journal of Geophysical Research:Atmospheres,2009,114(D2):2008JD010080.
[50] GRIESER J,JEWSON S.The RMS TC-Rain Model[J].Meteorologische Zeitschrift,2012,21(1):79-88.
[51] EMANUEL K.Assessing the present and future probability of Hurricane Harvey’s rainfall[J].Proceedings of the National Academy of Sciences,2017,114(48):12681-12684.
[52] LU P,LIN N,EMANUEL K,et al.Assessing hurricane rainfall mechanisms using a physics-based model:Hurricanes isabel (2003) and irene (2011)[J].Journal of the Atmospheric Sciences,2018,75(7):2337-2358.
[53] ALPAYDIN E.Introduction to Machine Learning[M].Cambridge,U.S.:MIT Press,2020.
[54] LIN G,CHEN G,WU M,et al.Effective forecasting of hourly typhoon rainfall using support vector machines[J].Water Resources Research,2009,45(8):2009WR007911.
[55] LIN G F,JHONG B C.A real-time forecasting model for the spatial distribution of typhoon rainfall[J].Journal of Hydrology,2015,521:302-313.
[56] WANG J Y,WONA R K W,JUN M,et al.Statistical and machine learning methods applied to the prediction of different tropical rainfall types[J].Environmental Research Communications,2021,3(11):111001.
[57] ZHU L,AGUILERA P.Evaluating variations in tropical cyclone precipitation in eastern mexico using machine learning techniques[J].Journal of Geophysical Research:Atmospheres,2021,126(7):e2021JD034604.
[58] HONG W C,PAI P F.Potential assessment of the support vector regression technique in rainfall forecasting[J].Water Resources Management,2007,21:495-513.
[59] SU H,WU L,JIANG J H,et al.Applying satellite observations of tropical cyclone internal structures to rapid intensification forecast with machine learning[J].Geophysical Research Letters,2020,47(17):e2020GL089102.
[60] HUANG Y,JIN L,ZHAO H sheng,et al.Fuzzy neural network and LLE Algorithm for forecasting precipitation in tropical cyclones:comparisons with interpolation method by ECMWF and stepwise regression method[J].Natural Hazards,2018,91(1):201-220.
[61] LIN G F,WU M C.A hybrid neural network model for typhoon-rainfall forecasting[J].Journal of Hydrology,2009,375(3-4):450-458.
[62] WANG C K.Simulate typhoon rainfall with artificial neural network[D].Taiwan:Cheng Kung University,2007.
[63] VARALAKSHMI P,VASUMATHI N,VENKATESAN R.Tropical Cyclone prediction based on multi-model fusion across Indian coastal region[J].Progress in Oceanography,2021,193:102557.
[64] LIN G F,JHONG B C,CHANG C C.Development of an effective data-driven model for hourly typhoon rainfall forecasting[J].Journal of Hydrology,2013,495:52-63.
[65] BAKI H,CHINTA S,BALAJI C,et al.Parameter calibration to improve the prediction of tropical cyclones over the bay of bengal using machine learning-based multiobjective optimization[J].Journal of Applied Meteorology and Climatology,2022,61(7):819-837.
[66] BAKI H,CHINTA S,BALAJI C,et al.Determining the sensitive parameters of the Weather Research and Forecasting (WRF) model for the simulation of tropical cyclones in the Bay of Bengal using global sensitivity analysis and machine learning[J].Geoscientific Model Development,2022,15(5):2133-2155.
[67] LOUKAS A,VASILIADES L.Streamflow simulation methods for ungauged and poorly gauged watersheds[J].Natural Hazards and Earth System Sciences,2014,14(7):1641-1661.
[68] YOUNG C C,LIU W C.Prediction and modelling of rainfall-runoff during typhoon events using a physically-based and artificial neural network hybrid model[J].Hydrological Sciences Journal,2015,60(12):2102-2116.
[69] JEON S,KIM J.Artificial intelligence to predict climate and weather change[J].JMST Advances,2024:6(1):67-73.
[70] LAM R,SANCHEZ-GONZALEZ A,WILLSON M,et al.Learning skillful medium-range global weather forecasting[J].Science,2023,382(6677):1416-1421.
[71] JING R,GAO J,CAI Y,et al.TC-GEN:Data-driven tropical cyclone downscaling using machine learning-based high-resolution weather model[J].Authorea Preprints,2024,16(10):e2023MSO04203.
[72] DING J,CHEN W,CHEN J,et al.Spatiotemporal inhomogeneity of accuracy degradation in AI weather forecast foundation models:A GNSS perspective[J].International Journal of Applied Earth Observation and Geoinformation,2025,139:104473.
[73] ROGERS R,MARKS F,MARCHOK T.Encyclopedia of Hydrological Sciences[M].Chichester,U.K.:John Wiley & Sons,Ltd.,2009.
[74] KANASE R D,DESHPANDE M S,KRISHNA R P M,et al.Evaluation of convective parameterization schemes in simulation of tropical cyclones by Climate Forecast System model:Version 2[J].Journal of Earth System Science,2020,129:1-18.
[75] SINGH K S,TYAGI B,VERMA V K,et al.Assessing the performance evaluation of different convective parameterization schemes in simulating the intensity of severe cyclonic storms over the Bay of Bengal region[J].Meteorological Applications,2019,26(4):597-609.
[76] 陈德辉，薛纪善.数值天气预报业务模式现状与展望[J].气象学报，2004(5):623-633.CHEN Dehui,XUE Jishan.Current status and prospects of operational numerical weather prediction models [J].Acta Meteorologica Sinica,2004(5):623-633.
[77] BAUER P,THORPE A,BRUNET G.The quiet revolution of numerical weather prediction[J].Nature,2015,525(7567):47-55.
[78] BI K,XIE L,ZHANG H,et al.Accurate medium-range global weather forecasting with 3D neural networks[J].Nature,2023,619(7970):533-538.
[79] LU Y,YIN J,WANG D,et al.Evaluating the influence of multisource typhoon precipitation data on multiscale urban pluvial flood modeling[J].International Journal of Disaster Risk Science,2022,13(6):974-986.
[80] LIU M,FANG S,DONG H,et.al.Review of digital twin about concepts,technologies,and industrial applications[J].Journal of Manufacturing Systems,2021,58:346-361.
[81] BRAIK A M,KOLIOU M.A novel digital twin framework of electric power infrastructure systems subjected to hurricanes[J].International Journal of Disaster Risk Reduction,2023,97:104020.
[82] FAN C,ZHANG C,YAHJA A,et al.Disaster city digital twin:A vision for integrating artificial and human intelligence for disaster management[J].International Journal of Information Management,2021,56:102049.
基本信息:
DOI：10.13928/j.cnki.wrahe.2025.05.010
中图分类号:P444;P426.6
引用信息:
[1]苏鹏,徐伟,陶凯,等.热带气旋降水模拟研究进展及展望[J].水利水电技术(中英文),2025,56(05):123-133.DOI:10.13928/j.cnki.wrahe.2025.05.010.
基金信息:
国家自然科学联合基金“基于天空地一体化多源多模态遥感数据的城市风险精细监测与评估技术研究”(U22B2011); 国家重点研发计划“台风、洪涝巨灾链主要承灾体保险技术研究与应用示范”(2023YFC3008505); 教育部-国家外国专家局高等学校学科创新引智计划“北京师范大学综合灾害风险管理创新引智基地”(BP0820003)
2024-07-03
2024-07-03
2024-07-03