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A Machine Learning-Based Parameterized Tropical Cyclone Precipitation Model

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International Journal of Disaster Risk Science 2024年第6期15卷 972-985页

作者： Yi Lu Jie Yin peiyan chen Hui Yu Sirong Huang Shanghai Typhoon Institute China Meteorological Administration School of Geographic Sciences East China Normal University Asia-Pacific Typhoon Collaborative Research Center Shanghai Engineering Research Center of Intelligent Education and Bigdata Shanghai Normal University

Current simulation models considerably underestimate local-scale,short-duration extreme precipitation induced by tropical cyclones (TCs).This problem needs to be addressed to establish active response policies for TC-induced *** Shanghai,a coastal megacity,as a study area and based on the observations from 192 meteorological stations in the city during 2005–2018,this study optimized the parameterized Tropical Cyclone Precipitation Model (TCPM) initially designed for TCs at the national scale (China) to the local or regional scales by using machine learning (ML) methods,including the random forest (RF),extreme gradient boosting (XGBoost),and ensemble learning (EL) *** TCPM-ML was applied for multiple temporal scale hazard *** results show that:(1) The TCPM-ML not only improved TCPM performance for simulating hourly extreme precipitations,but also preserved the physical meaning of the results,contrary to ML methods;(2) Machine learning algorithms enhanced the TCPM ability to reproduce observations,although the hourly extreme precipitations remained slightly underestimated;(3) Best performance was obtained with the XGBoost or EL *** the strengths of both XGBoost and RF,the EL algorithm yielded the best overall *** study provides essential model support for TC disaster risk assessment and response at the local and regional scales in China.

关键词： Extreme precipitation Machine learning Parameterized model Shanghai Tropical cyclone

A simplified index to assess the combined impact of tropical cyclone precipitation and wind on China

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Frontiers of Earth Science 2019年第4期13卷 672-681页

作者： peiyan chen Hui YU Ming XU Xiaotu LEI Feng ZENG Shanghai Typhoon Institute China Meteotological AdministrationShanghai 200030China The Joint Laboratory for Typhoon Forecasting Technique Applications between Shanghai Typhoon Institute and Wenzhou Meteorological Bureau Wenzhou 325000China Key Laboratory of Numerical Modeling for Tropical Cyclone China Meteotological AdministrationShanghai 200030China Shanghai Meteorological Service Shanghai 200030China Wenzhou Meteorological Bureau Wenzhou 325000China

Relationships between tropical cyclone(TC)precipitation,wind,and storm damage are analyzed for China based on TCs over the period from 1984 to *** analysis shows that the maximum daily areal precipitation from stations with daily precipitation of ≥50 mm and the sum of wind gusts of ≥13.9 m/s can be used to estimate the main damage caused by TCs,and an index combining the precipitation and wind gust of a TC(IPWT)is defined to assess the severity of the combined impact of precipitation and *** correlation coefficient between IPWT and the damage index for affecting TCs is 0.80,which is higher than that for only precipitation or *** TCs with precipitation and wind affecting China are divided intofive categories,Category 0 to Category 4,based on IPWT,where higher categories refer to higher combined impacts of precipitation and *** combined impact category is closely related to damage category and it can be used to estimate the potential damage category in operational *** are 87.7%,72.9%,69.8%,and 73.4%of cases that have the same or one category difference between damage category and combined impact category for Categories 1,2,3,and 4,*** and its classification can be used to assess the severity of the TC impact and of combined precipitation and wind conveniently and accurately,and the potential damage caused by *** result will be a good supplementary data for TC intensity,precipitation,wind,and *** addition,IPWT can be used as an index to judge the reliability of damage *** analysis of the annual frequency of combined precipitation-wind impact categories reveals no significant increasing or decreasing trend in impact over China over the past 30 years.

关键词： tropical cyclone impact precipitation wind

A Logistic-growth-equation-based Intensity Prediction Scheme for Western North Pacific Tropical Cyclones

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Advances in Atmospheric Sciences 2021年第10期38卷 1750-1762页

作者： Yanchen ZHOU Jiuwei ZHAO Ruifen ZHAN peiyan chen Zhiwei WU Lan WANG Department of Atmospheric and Ocean Sciences Institute of Atmospheric SciencesFudan UniversityShanghai 200438China Fujian Key Laboratory of Severe Weather Fuzhou 350001China Shanghai Typhoon Institute of China Meteorological Administration Shanghai 200030China Big Data Institute for Carbon Emission and Environmental Pollution Fudan UniversityShanghai 200438China

Accurate prediction of tropical cyclone(TC)intensity remains a challenge due to the complex physical processes involved in TC intensity changes.A seven-day TC intensity prediction scheme based on the logistic growth equation(LGE)for the western North Pacific(WNP)has been developed using the observed and reanalysis *** the LGE,TC intensity change is determined by a growth term and a decay *** two terms are comprised of four free parameters which include a time-dependent growth rate,a maximum potential intensity(MPI),and two *** 33 years of training samples,optimal predictors are selected first,and then the two constants are determined based on the least square method,forcing the regressed growth rate from the optimal predictors to be as close to the observed as *** estimation of the growth rate is further refined based on a step-wise regression(SWR)method and a machine learning(ML)method for the period 1982−*** the LGE-based scheme,a total of 80 TCs during 2015−17 are used to make independent *** show that the root mean square errors of the LGE-based scheme are much smaller than those of the official intensity forecasts from the China Meteorological Administration(CMA),especially for TCs in the coastal regions of East ***,the scheme based on ML demonstrates better forecast skill than that based on *** new prediction scheme offers strong potential for both improving the forecasts for rapid intensification and weakening of TCs as well as for extending the 5-day forecasts currently issued by the CMA to 7-day forecasts.

关键词： tropical cyclone intensity prediction western North Pacific logistic growth equation

PREDICTION OF FATIGUE LIVES OF RC BEAMS STRENGTHENED WITH CFL UNDER RANDOM LOADING

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Acta Mechanica Solida Sinica 2008年第4期21卷 359-363页

作者： Rongwei Lin peiyan Huang chen Zhao Xinyan Guo Xiaohong Zheng College of Traffic and Communications South China University of Technology Guangzhou 510640 China

The investigation on fatigue lives of reinforced concrete （RC） structures strength- ened with fiber laminate under random loading is important for the repairing or the strengthening of bridges and the safety of the traffic. In this paper, two methods are developed for predicting the fatigue lives of RC structures strengthened with carbon fiber [aminate （CFL） under random loading based on a residual life and a residual strength model. To discuss the efficiency of the model, 12 RC beams strengthened with CFL are tested under random loading by the MTS810 testing system. The predicted residual strength approximately agrees with test results.

关键词： carbon fiber laminate predicted method fatigue life random load RC structure

Impact of physical representations in CALMET on the simulated wind field over land during Super Typhoon Meranti(2016)

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Frontiers of Earth Science 2019年第4期13卷 744-757页

作者： Sui HUANG Shengming TANG Hui YU Wenbo XUE Pingzhi FANG peiyan chen Chinese Academy of Meteorological Sciences Beijing 100081China Shanghai Typhoon Institute China Meteorological AdministrationShanghai 200030China Key Laboratory of Numerical Modeling for Tropical Cyclones China Meteorological Administration Shanghai 200030China The Joint Laboratory for Typhoon Forecasting Technique Applications between Shanghai Typhoon Institute and Wenzhou Meteorological Bureau Wenzhou 325000China

A WRF(Weather Research and Forecasting Model)/CALMET(California Meteorological Model)coupled system is used to investigate the impact of physical representations in CALMET on simulations of the near-surface wind field of Super Typhoon Meranti(2016).The coupled system is configured with a horizontal grid spacing of 3 km in WRF and 500 m in CALMET,*** model performance of the coupled WRF/CALMET system is evaluated by comparing the results of simulations with observational data from 981 automatic surface stations in Fujian *** root mean square error(RMSE)of the wind speed at 10 m in all CALMET simulations is significantly less than the WRF simulation by 20%^30%,suggesting that the coupled WRF/CALMET system is capable of representing more realistic simulated wind speed than the mesoscale model *** impacts of three physical representations including blocking effects,kinematic effects of terrain and slope flows in CALMET are examined in a specified local region called Shishe *** results show that before the typhoon landfall in Xiamen,a net downslope flow that is tangent to the terrain is generated in the west of Shishe Mountain due to blocking effects with magnitude exceeding 10 m/***,the blocking effects seem to take no effect in the strong wind area after typhoon *** being affected by the typhoon strong wind or not,the slope flows move downslope at night and upslope in the daytime due to the diurnal variability of the local heat flux with magnitude smaller than 3 m/*** kinematic effects of terrain,which are speculated to play a significant role in the typhoon strong wind area,can only be applied to atmospheric flows in stable conditions when the wind field is quasinondivergent.

关键词： physical representations CALMET wind field Super Typhoon Meranti

A Potential Risk Index Dataset for Landfalling Tropical Cyclones over the Chinese Mainland(PRITC dataset V1.0)

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Advances in Atmospheric Sciences 2021年第10期38卷 1791-1802页

作者： peiyan chen Hui YU Kevin K.W.CHEUNG Jiajie XIN Yi LU Shanghai Typhoon Institute China Meteorological AdministrationShanghai 200030China Key Laboratory of Numerical Modeling for Tropical Cyclone of China Meteorological Administration Shanghai 20030China The Joint Laboratory for Typhoon Forecasting Technique Applications between Shanghai Typhoon Institute and Wenzhou Meteorological Bureau Wenzhou 325027China Climate and Atmospheric Science NSW Department of Planning Industry and EnvironmentSydney 2000Australia

A dataset entitled“A potential risk index dataset for landfalling tropical cyclones over the Chinese mainland”(PRITC dataset V1.0)is described in this paper,as are some basic statistical *** the severity of the impacts of tropical cyclones(TCs)that make landfall on the Chinese mainland based on observations from 1401 meteorological stations was proposed in a previous study,including an index combining TC-induced precipitation and wind(IPWT)and further information,such as the corresponding category level(CAT_IPWT),an index of TC-induced wind(IWT),and an index of TC-induced precipitation(IPT).The current version of the dataset includes TCs that made landfall from 1949-2018;the dataset will be extended each ***-term trend analyses demonstrate that the severity of the TC impacts on the Chinese mainland have increased,as embodied by the annual mean IPWT values,and increases in TC-induced precipitation are the main contributor to this *** Winnie(1997)and TC Bilis(2006)were the two TCs with the highest IPWT and IPT values,*** PRITC V1.0 dataset was developed based on the China Meteorological Administration’s tropical cyclone database and can serve as a bridge between TC hazards and their social and economic impacts.

关键词： tropical cyclone risk dataset China

Evaluating the Influence of Multisource Typhoon Precipitation Data on Multiscale Urban Pluvial Flood Modeling

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International Journal of Disaster Risk Science 2022年第6期13卷 974-986页

作者： Yi Lu Jie Yin Dandan Wang Yuhan Yang Hui Yu peiyan chen Shuai Zhang Key Laboratory of Geographic Information Science(Ministry of Education) East China Normal UniversityShanghai 200241China Shanghai Typhoon Institute China Meteorological AdministrationShanghai 200030China Key Laboratory of Numerical Modeling for Tropical Cyclone of China Meteorological Administration Shanghai 20030China Institute of Eco-Chongming East China Normal UniversityShanghai 202162China Research Center for China Administrative Division East China Normal UniversityShanghai 202162China National Disaster Reduction Center of China Beijing 100124China State Key Laboratory of Estuarine and Coastal Research East China Normal UniversityShanghaiChina

Based on station precipitation observations,radar quantitative precipitation estimates(QPE), and radar fusion data during Typhoon Fitow(2013), the influence of multisource precipitation data on multiscale urban typhoon pluvial flood modeling is studied. Using Shanghai, China,as the study area, a simplified 2D hydrodynamic model is applied to simulations. Combined with actual flood incidents reported by the public and soil moisture data, we perform multiscale verifications and determine the applicability of three precipitation datasets in the modeling. The results are as follows:(1) At the city scale, although QPE have higher spatial resolution, these estimates are lower than station observations. Radar fusion data have both high accuracy and high spatial resolution. For flood depths above 5 cm, the radar fusion precipitation scenario can improve the matching probability by 6%.(2) At the neighborhood scale, the radar fusion precipitation scenario can effectively mitigate the problems of an uneven spatial distribution of stations and a weak QPE to accurately capture pluvial details.(3)One fixed-point assessment shows that different precipitation data have little influence on the temporal characteristics of the modeling result-all three types of data can accurately reflect flood occurrence times. This work can provide a scientific basis for constructing effective urban pluvial flood monitoring systems.

关键词： City and neighborhood scale Flood validation Multisource precipitation data Pluvial food modeling Shanghai

A Physics-informed Deep-learning Intensity Prediction Scheme for Tropical Cyclones over the Western North Pacific

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Advances in Atmospheric Sciences 2024年第7期41卷 1391-1402页

作者： Yitian ZHOU Ruifen ZHAN Yuqing WANG peiyan chen Zhemin TAN Zhipeng XIE Xiuwen NIE Department of Atmospheric and Oceanic Sciences&Institute of Atmospheric Sciences Fudan UniversityShanghai 200438China Shanghai Typhoon Institute of China Meteorological Administration Shanghai 200030China Department of Atmospheric Sciences and International Pacific Research Center University of Hawaii at ManoaHonolulu 96822USA Key Laboratory of Mesoscale Severe Weather/MOE and School of the Atmospheric SciencesNanjing UniversityNanjing 210023China School of Computer Science Fudan UniversityShanghai 200438China

Accurate prediction of tropical cyclone(TC)intensity is challenging due to the complex physical processes ***,we introduce a new TC intensity prediction scheme for the western North Pacific(WNP)based on a time-dependent theory of TC intensification,termed the energetically based dynamical system(EBDS)model,together with the use of a long short-term memory(LSTM)neural *** time-dependent theory,TC intensity change is controlled by both the internal dynamics of the TC system and various environmental factors,expressed as environmental dynamical *** LSTM neural network is used to predict the environmental dynamical efficiency in the EBDS model trained using besttrack TC data and global reanalysis data during 1982–*** transfer learning and ensemble methods are used to retrain the scheme using the environmental factors predicted by the Global Forecast System(GFS)of the National Centers for Environmental Prediction during 2017–*** predicted environmental dynamical efficiency is finally iterated into the EBDS equations to predict TC *** new scheme is evaluated for TC intensity prediction using both reanalysis data and the GFS prediction *** intensity prediction by the new scheme shows better skill than the official prediction from the China Meteorological Administration(CMA)and those by other state-of-art statistical and dynamical forecast systems,except for the 72-h *** at the longer lead times of 96 h and 120 h,the new scheme has smaller forecast errors,with a more than 30%improvement over the official forecasts.

关键词： tropical cyclones western North Pacific intensity prediction EBDS LSTM

The impact of Typhoon Lekima (2019) on East China: a postevent survey in Wenzhou City and Taizhou City

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Frontiers of Earth Science 2022年第1期16卷 109-120页

作者： Cong ZHOU peiyan chen Shifang YANG Feng ZHENG Hui YU Jie TANG Yi LU Guoming chen Xiaoqing LU Xiping ZHANG Jing SUN Shanghai Typhoon Institute of China Meteorological Administration Shanghai 200030China Department of Atmospheric and Oceanic Sciences Fudan UniversityShanghai 200438China Key Laboratory of Numerical Modeling for Tropical Cyclones China Meteorological Administration Shanghai 200030China The Joint Laboratory for Typhoon Forecasting Technique Applications between Shanghai Typhoon Institute and Wenzhou Meteorological Bureau Wenzhou 325000China Taizhou Meteorological Bureau Taizhou 318000China Wenzhou Meteorological Bureau Wenzhou 325000China

Typhoon Lekima(2019)struck Zhejiang Province on 10 August 2019 as a supertyphoon,which severely impacted Zhejiang *** typhoon killed 45 people and left three others missing,and the total economic loss reached 40.71 billion *** paper reports a postdisaster survey that focuses on the storm precipitation,flooding,landslides,and weather services associated with Typhoon Lekima(2019)along the southeastern coastline of Zhejiang *** survey was conducted by a joint survey team from the Shanghai Typhoon Institute and local meteorological bureaus from 26 to 28 August,2019,approximately two weeks after the *** on this survey and subsequent analyses of the results,we hope to develop countermeasures to prevent future tragedies.

关键词： Typhoon Lekima(2019) Zhejiang Province disaster assessment postdisaster survey