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Journal of Tropical Meteorology 2024年第2期30卷 189-199页

作者：陈炳洪傅佩玲张羽苏冉田聪聪陈超 Guangzhou meteorological observatory Guangzhou 511430 China China meteorological Administration Tornado Key Laboratory Guangdong Meteorological ServiceFoshanGuangdong 528010 China Collaborative Innovation Center for Smart Meteorology of Guangzhou Guangzhou 511430 China Guangdong meteorological observatory Guangzhou 510640 China

The X-band phased array radar offers faster scanning speed and higher spatial resolution compared to the S-band radar,making it capable of enhancing tornado monitoring and early warning capabilities.This study analyzed the characteristics and nowcasting signals of a tornado case that occurred on June 16,2022 in the Guangzhou region.Our findings indicate that the violent contraction of rotation radius and the dramatic increase in rotation speed were important signal characteristics associated with tornado formation.The X-band phased array radar,with its high temporal and spatial resolution,provided an opportunity to capture early warning signals from polarimetric characteristics.The X-band phased array radar demonstrated noteworthy ability to identify apparent tornado vortex signature(TVS)features in a 10-minute lead time,surpassing the capabilities of the CINRAD/SA radar.Additionally,due to its higher scanning frequency,the Xband phased-array radar was capable of consistently identifying TVS with shorter intervals,enabling a more precise tracking of the tornado's path.The application of professional radars,in this case,provides valuable insights for the monitoring of evolutions of severe local storms and even tornadoes and the issuance of early warning signals.

关键词： tornadoes X-band phased array radar mesocyclone rotation radius

Microphysical Insights into a Tornadic Supercell from Dual-Polarization Radar Observations in Jiangsu, China on 14 May 2021

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Journal of meteorological Research 2024年第2期38卷 303-320页

作者： Chao YUAN Yang BAI Pengfei SUN Hua BAI Lu XIA China meteorological Administration Tornado Key Laboratory Foshan 528315 Institute of Atmospheric Environment China Meteorological AdministrationShenyang 110166 Panjin National Climate observatory Panjin 124010 Yangzhou meteorological Bureau of Jiangsu Province Yangzhou 225100 Yichun meteorological Bureau of Heilongjiang Province Yichun 153000

Tornadoes are incredibly powerful and destructive natural events,yet the microphysical characteristics of the parent storm and its effects on tornadogenesis remain unclear.This study analyzed polarization radar data of a tornadic supercell that occurred in Jiangsu Province of China on 14 May 2021,in comparison with another tornadic supercell and two non-tornadic supercells that occurred in the same region in 2023.The two tornadic supercells exhibited lower differential reflectivity(ZDR)in the hook echo region compared with the non-tornadic supercells,indicating smaller median drop sizes.A distinct increase in ZDR from the melting of frozen hydrometeors,observed between2.5-and 4.0-km altitude in the non-tornadic storms,was absent in the tornadic cases.The non-tornadic supercells also displayed substantially higher specific differential phase(KDP)below the melting level,likely aroused from enhanced melting and cooling.These findings suggest fundamental microphysical contrasts between tornadic and nontornadic supercells.Specifically,tornadic supercells have smaller droplets and may reduce melting in hook echoes.Moreover,greater separation between the ZDR arc and the KDP foot was observed during tornadogenesis.The vertical gradient of KDP related to the cooling pool strength of the hook echo,regulating rear-flank downdraft thermodynamics.Despite the limited number of cases investigated,the findings of this study indicate that monitoring ZDR,KDP,and drop size distribution trends could assist with tornado prediction and warnings.

关键词： tornado polarimetric radar supercell hook echo drop size distribution

Comparative Analysis of Energy Characteristics of Two Southwest Vortices in Sichuan Under Similar Circulation Backgrounds

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Journal of Tropical Meteorology 2024年第2期30卷 168-179页

作者：周春花张驹肖红茹 Sichuan meteorological Disaster Prevention Technology Center Chengdu 610072 China Chengdu meteorological Bureau Chengdu 610072 China Sichuan meteorological observatory Chengdu 610072 China

Based on ERA5 reanalysis data,the present study analyzed the thermal energy development mechanism and kinetic energy conversion characteristics of two extreme rainstorm processes in relation to the shallow southwest vortex in the warm-sector during a“rain-generated vortex”process and the deep southwest vortex in a“vortex-generated rain”process.The findings were as follows:(1)During the extreme rainstorm on August 11,2020(hereinafter referred to as the“8·11”process),intense surface heating and a high-energy unstable environment were observed.The mesoscale convergence system triggered convection to produce heavy rainfall,and the release of latent condensation heat generated by the rainfall promoted the formation of a southwest vortex.The significant increase(decrease)in atmospheric diabatic heating and kinetic energy preceded the increase(decrease)in vorticity.By contrast,the extreme rainstorm on August 16,2020(hereinafter referred to as the“8·16”process)involved the generation of southwest vortex in a low-energy and highhumidity environment.The dynamic uplift of the southwest vortex triggered rainfall,and the release of condensation latent heat from rainfall further strengthened the development of the southwest vortex.The significant increase(decrease)in atmospheric diabatic heating and kinetic energy exhibited a delayed progression compared to the increase(decrease)in vorticity.(2)The heating effect around the southwest vortex region was non-uniform,and the heating intensity varied in different stages.In the“8·11”process,the heating effect was the strongest in the initial stage,but weakened during the vortex's development.On the contrary,the heating effect was initially weak in the“8·16”process,and intensified during the development stage.(3)The available potential energy of the“8·11”process significantly increased in kinetic energy converted from rotational and divergent winds through baroclinic action,and the divergent wind energy continued to convert into rotational wind energy.By contrast,the“8·16”process inv

关键词： southwest vortex similar circulation background diabatic heating kinetic energy spatial non-uniform heating effect

Study on Quantitative Precipitation Estimation by Polarimetric Radar Using Deep Learning

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Advances in Atmospheric Sciences 2024年第6期41卷 1147-1160页

作者： Jiang HUANGFU Zhiqun HU Jiafeng ZHENG Lirong WANG Yongjie ZHU State Key Lab of Severe Weather Chinese Academy of Meteorological SciencesBeijing 100081China meteorological observatory of Jiangxi Province Nanchang 330096China Hebei meteorological Disaster Prevention and Environmental meteorological Center Shijiazhuang 050021China School of Atmospheric Sciences Chengdu University of Information TechnologyChengdu 610225China

Accurate radar quantitative precipitation estimation(QPE)plays an essential role in disaster prevention and mitigation.In this paper,two deep learning-based QPE networks including a single-parameter network and a multi-parameter network are designed.Meanwhile,a self-defined loss function(SLF)is proposed during modeling.The dataset includes Shijiazhuang S-band dual polarimetric radar(CINRAD/SAD)data and rain gauge data within the radar’s 100-km detection range during the flood season of 2021 in North China.Considering that the specific propagation phase shift(KDP)has a roughly linear relationship with the precipitation intensity,KDP is set to 0.5°km^(-1 )as a threshold value to divide all the rain data(AR)into a heavy rain(HR)and light rain(LR)dataset.Subsequently,12 deep learning-based QPE models are trained according to the input radar parameters,the precipitation datasets,and whether an SLF was adopted,respectively.The results suggest that the effects of QPE after distinguishing rainfall intensity are better than those without distinguishing,and the effects of using SLF are better than those that used MSE as a loss function.A Z-R relationship and a ZH-KDP-R synthesis method are compared with deep learning-based QPE.The mean relative errors(MRE)of AR models using SLF are improved by 61.90%,51.21%,and 56.34%compared with the Z-R relational method,and by 38.63%,42.55%,and 47.49%compared with the synthesis method.Finally,the models are further evaluated in three precipitation processes,which manifest that the deep learning-based models have significant advantages over the traditional empirical formula methods.

关键词： polarimetric radar quantitative precipitation estimation deep learning single-parameter network multi-parameter network

Characteristics of Lightning Activity in Southeast China and its Relation to the Atmospheric Background

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Journal of Tropical Meteorology 2024年第1期30卷 76-88页

作者：支树林朱杰刘岩毛梦妮 Jiangxi meteorological observatory Nanchang 330096 China Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites National Satellite Meteorological Center(National Center for Space Weather)China Meteorological AdministrationBeijing 100081 China Shanghai Typhoon Institute China Meteorological AdministrationShanghai 200030 China Innovation Center for FengYun meteorological Satellite(FYSIC) Beijing 100081 China Key Laboratory of Transportation Meteorology of China meteorological Administration Nanjing Joint Institute for Atmospheric SciencesNanjing 210041 China Key Laboratory of meteorological Disaster(KLME) Ministry of Education&Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CICFEMD)Nanjing University of Information Science&TechnologyNanjing 210041 China Jiujiang observatory JiujiangJiangxi 332000 China

Based on the lightning observation data from the Fengyun-4A(FY-4A)Lightning Mapping Imager(FY-4A/LMI)and the Lightning Imaging Sensor(LIS)on the International Space Station(ISS),we extract the“event”type data as the lightning detection results.These observations are then compared with the cloud-to-ground(CG)lightning observation data from the China meteorological Administration.This study focuses on the characteristics of lightning activity in Southeast China,primarily in Jiangxi Province and its adjacent areas,from April to September,2017–2022.In addition,with the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis data,we further delved into the potential factors influencing the distribution and variations in lightning activity and their primary related factors.Our findings indicate that the lightning frequency and density of the FY-4A/LMI,ISS-LIS and CG data are higher in southern and central Jiangxi,central Fujian Province,and western and central Guangdong Province,while they tend to be lower in eastern Hunan Province.In general,the high-value areas of lightning density for the FY-4A/LMI are located in inland mountainous areas.The lower the latitude is,the higher the CG lightning density is.High-value areas of the CG lightning density are more likely to be located in eastern Fujian and southeastern Zhejiang Province.However,the high-value areas of lightning density for the ISS-LIS are more dispersed,with a scattered distribution in inland mountainous areas and along the coast of eastern Fujian.Thus,the mountainous terrain is closely related to the high-value areas of the lightning density.The locations of the high-value areas of the lightning density for the FY-4A/LMI correspond well with those for the CG observations,and the seasonal variations are also consistent.In contrast,the distribution of the high-value areas of the lightning density for the ISS-LIS is more dispersed.The positions of the peak frequency of the FY-4A/LMI lightning and CG lightning contrast with local altitudes

关键词： lightning satellite and ground detections atmospheric background Southeast China

Improved forecasting via physics-guided machine learning as exemplified using“21·7”extreme rainfall event in Henan

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Science China Earth Sciences 2024年第5期67卷 1652-1674页

作者： Qi ZHONG Zhicha ZHANG Xiuping YAO Shaoyu HOU Shenming FU Yong CAO Linguo JING China meteorological Administration Training Center Beijing 100081China State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesBeijing 100081China Zhejiang meteorological observatory Hangzhou 310017China Hebei Provincial Weather Modification Center Shijiazhuang 050020China Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China National meteorological Center Beijing 100081China School of Computer Science Chengdu University of Information TechnologyChengdu 610225China

As a natural disaster,extreme precipitation is among the most destructive and influential,but predicting its occurrence and evolution accurately is very challenging because of its rarity and uniqueness.Taking the example of the“21·7”extreme precipitation event(17–21 July 2021)in Henan Province,this study explores the potential of using physics-guided machine learning to improve the accuracy of forecasting the intensity and location of extreme precipitation.Three physics-guided ways of embedding physical features,fusing physical model forecasts and revised loss function are used,i.e.,(1)analyzing the anomalous circulation and thermodynamical factors,(2)analyzing the multi-model forecast bias and the associated underlying reasons for it,and(3)using professional forecasting knowledge to design the loss function,and the corresponding results are used as input for machine learning to improve the forecasting accuracy.The results indicate that by learning the relationship between anomalous physical features and heavy precipitation,the forecasting of precipitation intensity is improved significantly,but the location is rarely adjusted and more false alarms appear.Possible reasons for this are as follows.The anomalous features used here mainly contain information about large-scale systems and factors which are consistent with the model precipitation deviation;moreover,the samples of extreme precipitation are sparse and so the algorithm used here is simple.However,by combining“good and different”multi models with machine learning,the advantages of each model are extracted and then the location of the precipitation center in the forecast is improved significantly.Therefore,by combining the appropriate anomalous features with multi-model fusion,an integrated improvement of the forecast of the rainfall intensity and location is achieved.Overall,this study is a novel exploration to improve the refined forecasting of heavy precipitation with extreme intensity and high variability,and provides a reference for the deep fusion of

关键词： Extreme precipitation event Refined assessment Anomalous physical features Multi-model fusion Machine learning

Raindrop Size Distributions in the Zhengzhou Extreme Rainfall Event on 20 July 2021: Temporal–Spatial Variability and Implications for Radar QPE

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Journal of meteorological Research 2024年第3期38卷 489-503页

作者： Liman CUI Haoran LI Aifang SU Yang ZHANG Xiaona LYU Le XI Yuanmeng ZHANG Henan Key Laboratory of Agrometeorological Support and Applied Technique China Meteorological Administration State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesChina Meteorological Administration Henan meteorological observatory

In this study,a regional Parsivel OTT disdrometer network covering urban Zhengzhou and adjacent areas is employed to investigate the temporal–spatial variability of raindrop size distributions （DSDs） in the Zhengzhou extreme rainfall event on 20 July 2021.The rain rates observed by disdrometers and rain gauges from six operational sites are in good agreement,despite significant site-to-site variations of 24-h accumulated rainfall ranging from 198.3 to624.1 mm.The Parsivel OTT observations show prominent temporal–spatial variations of DSDs,and the most drastic change was registered at Zhengzhou Station where the record-breaking hourly rainfall of 201.9 mm over 1500–1600 LST （local standard time） was reported.This hourly rainfall is characterized by fairly high concentrations of large raindrops,and the mass-weighted raindrop diameter generally increases with the rain rate before reaching the equilibrium state of DSDs with the rain rate of about 50 mm h-1.Besides,polarimetric radar observations show the highest differential phase shift (Kdp) and differential reflectivity (Zdr) near surface over Zhengzhou Station from 1500to 1600 LST.In light of the remarkable temporal–spatial variability of DSDs,a reflectivity-grouped fitting approach is proposed to optimize the reflectivity–rain rate （Z–R） parameterization for radar quantitative precipitation estimation （QPE）,and the rain gauge measurements are used for validation.The results show an increase of mean bias ratio from 0.57 to 0.79 and a decrease of root-mean-square error from 23.69 to 18.36 for the rainfall intensity above20.0 mm h-1,as compared with the fixed Z–R parameterization.This study reveals the drastic temporal–spatial variations of rain microphysics during the Zhengzhou extreme rainfall event and warrants the promise of using reflectivity-grouped fitting Z–R relationships for radar QPE of such events.

关键词： extreme rainfall raindrop size distribution radar quantitative precipitation estimation (QPE)

Influence of Irregular Coastlines on a Tornadic Mesovortex in the Pearl River Delta during the Monsoon Season. Part Ⅰ:Pre-storm Environment and Storm Evolution

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Advances in Atmospheric Sciences 2024年第6期41卷 1115-1131页

作者： Lanqiang BAI Dan YAO Zhiyong MENG Yu ZHANG Xianxiang HUANG Zhaoming LI Xiaoding YU Foshan Tornado Research Center and China Meteorological Administration Tornado Key LaboratoryGuangdong Meteorological Serviceand Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Guangdong 528000China meteorological Observation Centre CMABeijing 100081China Department of Atmospheric and Oceanic Sciences and China Meteorological Administration Tornado Key LaboratorySchool of PhysicsPeking UniversityBeijing 100871China Guangzhou meteorological observatory CMAGuangdong 511430China Foshan Tornado Research Center and China Meteorological Administration Tornado Key LaboratoryCMAGuangdong 528000China China meteorological Administration Training Center CMABeijing 528000China

The Pearl River Delta(PRD),a tornado hotspot,forms a distinct trumpet-shaped coastline that concaves toward the South China Sea.During the summer monsoon season,low-level southwesterlies over the PRD’s sea surface tend to be turned toward the west coast,constituting a convergent wind field along with the landward-side southwesterlies,which influences regional convective weather.This two-part study explores the roles of this unique land–sea contrast of the trumpet-shaped coastline in the formation of a tornadic mesovortex within monsoonal flows in this region.Part I primarily presents observational analyses of pre-storm environments and storm evolutions.The rotating storm developed in a lowshear environment(not ideal for a supercell)under the interactions of three air masses under the influence of the land–sea contrast,monsoon,and storm cold outflows.This intersection zone(or“triple point”)is typically characterized by local enhancements of ambient vertical vorticity and convergence.Based on a rapid-scan X-band phased-array radar,finger-like echoes were recognized shortly after the gust front intruded on the triple point.Developed over the triple point,they rapidly wrapped up with a well-defined low-level mesovortex.It is thus presumed that the triple point may have played roles in the mesovortex genesis,which will be demonstrated in Part II with multiple sensitivity numerical simulations.The findings also suggest that when storms pass over the boundary intersection zone in the PRD,the expected possibility of a rotating storm occurring is relatively high,even in a low-shear environment.Improved knowledge of such environments provides additional guidance to assess the regional tornado risk.

关键词： tornado mesovortex surface boundary land–sea contrast monsoon

Sub-Seasonal Predictability of the Northeast China Cold Vortex in BCC and ECMWF S2S Model Forecasts for 2006–2021

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Journal of meteorological Research 2024年第3期38卷 453-468页

作者： Yiqiu YU Jie WU Yihe FANG Chunyu ZHAO Zongjian KE Yitong LIN Liaoning Provincial Climate Centre Liaoning Provincial Meteorological Administration Key Opening Laboratory for Northeast China Cold Vortex Research China Meteorological Administration Panjin National Climate observatory China meteorological Administration Key Laboratory for Climate Prediction Studies National Climate Centre China Meteorological Administration Jilin Provincial Climate Centre Jilin Provincial Meteorological Administration

As an important atmospheric circulation system in the mid–high latitudes of East Asia, the Northeast China cold vortex(NCCV) substantially influences weather and climate in this region. So far, systematic assessment on the performance of numerical prediction of the NCCVs has not been carried out. Based on the Beijing Climate Centre(BCC) and the ECMWF model hindcast and forecast data that participated in the Sub-seasonal to Seasonal(S2S) Prediction Project, this study systematically examines the performance of both models in simulating and forecasting the NCCVs at the sub-seasonal timescale. The results demonstrate that the two models can effectively capture the seasonal variations in the intensity, active days, and spatial distribution of NCCVs; however, the duration of NCCVs is shorter and the intensity is weaker in the models than in the observations. Diagnostic analysis shows that the differences in the intensity and location of the East Asian subtropical westerly jet and the wave train pattern from North Atlantic to East Asia may be responsible for the deficient simulation of NCCV events in the S2S models. Nonetheless, in the deterministic forecasts, BCC and ECMWF provide skillful prediction on the anomalous numbers of NCCV days and intensity at a lead time of 4–5(5–6) pentads, and the skill limit of the ensemble mean is 1–2 pentads longer than that of individual members. In the probabilistic forecasts of daily NCCV activities, BCC and ECMWF exhibit a forecasting skill of approximately 7 and 11 days, respectively; both models show seasonal dependency in the simulation performance and forecast skills of NCCV events, with better performance in winter than in summer.The results from this study provide helpful references for further improvement of the S2S prediction of NCCVs.

关键词： Northeast China cold vortex (NCCV) predictability sub-seasonal to seasonal (S2S) prediction deterministic forecast probabilistic forecast

Growth and Interactions of Multi-Source Perturbations in Convection-Allowing Ensemble Forecasts

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Journal of Tropical Meteorology 2024年第2期30卷 118-131页

作者：张璐闵锦忠庄潇然王世璋魏莉青 Key Laboratory of meteorological Disaster of Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of meteorological Disasters Nanjing University of Information Science and TechnologyNanjing 210044 China Jiangsu meteorological observatory Jiangsu Meteorological ServiceNanjing 210008 China Nanjing Joint Institute for Atmospheric Sciences Nanjing 210000 China

This study investigated the growth of forecast errors stemming from initial conditions(ICs),lateral boundary conditions(LBCs),and model(MO)perturbations,as well as their interactions,by conducting seven 36 h convectionallowing ensemble forecast(CAEF)experiments.Two cases,one with strong-forcing(SF)and the other with weak-forcing(WF),occurred over the Yangtze-Huai River basin(YHRB)in East China,were selected to examine the sources of uncertainties associated with perturbation growth under varying forcing backgrounds and the influence of these backgrounds on growth.The perturbations exhibited distinct characteristics in terms of temporal evolution,spatial propagation,and vertical distribution under different forcing backgrounds,indicating a dependence between perturbation growth and forcing background.A comparison of the perturbation growth in different precipitation areas revealed that IC and LBC perturbations were significantly influenced by the location of precipitation in the SF case,while MO perturbations were more responsive to convection triggering and dominated in the WF case.The vertical distribution of perturbations showed that the sources of uncertainties and the performance of perturbations varied between SF and WF cases,with LBC perturbations displaying notable case dependence.Furthermore,the interactions between perturbations were considered by exploring the added values of different source perturbations.For the SF case,the added values of IC,LBC,and MO perturbations were reflected in different forecast periods and different source uncertainties,suggesting that the combination of multi-source perturbations can yield positive interactions.In the WF case,MO perturbations provided a more accurate estimation of uncertainties downstream of the Dabie Mountain and need to be prioritized in the research on perturbation development.

关键词： convection-allowing ensemble forecast forcing background perturbation growth interactions added value