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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 AdministrationZhengzhou450003China State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesChina Meteorological AdministrationBeijing100081China henan meteorological observatory Zhengzhou450003China

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 *** 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 to 624.1 *** 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 *** 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(K_(dp))and differential reflectivity(Z_(dr))near surface over Zhengzhou Station from 1500 to 1600 *** 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 *** 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 above 20.0 mm h^(−1),as compared with the fixed Z-R *** 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)

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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 *** 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 *** 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 *** non-tornadic supercells also displayed substantially higher specific differential phase(KDP)below the melting level,likely aroused from enhanced melting and *** findings suggest fundamental microphysical contrasts between tornadic and nontornadic ***,tornadic supercells have smaller droplets and may reduce melting in hook ***,greater separation between the ZDR arc and the KDP foot was observed during *** vertical gradient of KDP related to the cooling pool strength of the hook echo,regulating rear-flank downdraft *** 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

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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 *** this paper,two deep learning-based QPE networks including a single-parameter network and a multi-parameter network are ***,a self-defined loss function(SLF)is proposed during *** 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 *** 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)***,12 deep learning-based QPE models are trained according to the input radar parameters,the precipitation datasets,and whether an SLF was adopted,*** 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 *** 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%,4..55%,and 4..4.%compared with the synthesis ***,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

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Impact of EnKF assimilating Himawari-8 all-sky infrared radiance on the forecasting of a warm-sector rainstorm event

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Science China Earth Sciences 2024年第10期67卷 3110-3127页

作者： Shanshan LOU Lei ZHU Xuexing QIU Guangzhou CHEN Song YUAN Shengnan ZHOU Anhui meteorological observatory Hefei 230031China Key Laboratory of meteorological Disaster Ministry of Education(KLME)Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)Joint International Research Laboratory of Climate and Environment Change(ILCEC)Nanjing University of Information Science&TechnologyNanjing 210044China Huaihe River Basin meteorological Center Hefei 230031China Shouxian National Climatology observatory Huaihe River Basin Typical Farm Eco-Meteorological Experiment Field of CMAShouxian National Comprehensive Meteorological Observation Special Experimental OutfieldShouxian 232200China Anhui province Key Laboratory of Atmospheric Science and Satellite Remote Sensing Hefei 230031China

Warm-sector rainstorms are highly localized events, with weather systems and triggering mechanisms are not obvious,leading to limited forecasting capabilities in numerical models. Based on the ensemble Kalman filter(PSU-En KF) assimilation system and the regional mesoscale model WRF, this study conducted a simulation experiment assimilating all-sky infrared(IR)radiance for a warm-sector rainstorm in East China and investigated the positive impact of assimilating the Himawari-8 moisture channel all-sky IR radiance on the forecast of the rainstorm. Results indicate that hourly cycling assimilation of all-sky IR radiance can significantly improve the forecast accuracy of this warm-sector rainstorm. There is a notable increase in the Threat Score(TS), with the simulated location and intensity of the 3-hour precipitation aligning more closely with observations. These improvements result from the assimilation of cloud-affected radiance, which introduces more mesoscale convective information into the model's initial fields. The adjustments include enhancements to the moisture field, such as increased humidity and moisture transport, and modifications to the wind field, including the intrusion of mid-level cold air and the strengthening of lowlevel convergent shear. These factors are critical in improving the forecast of this warm-sector rainstorm event.

关键词： Ensemble Kalman filter WRF model Himawari-8 All-sky radiance Warm-sector rainstorm

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Characterization and Application of S-Band Polarimetric Radar and X-Band Phased Array Radar for a Tornadic Storm Event on June 16,2022

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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 *** study analyzed the characteristics and nowcasting signals of a tornado case that occurred on June 16,2022 in the Guangzhou *** findings indicate that the violent contraction of rotation radius and the dramatic increase in rotation speed were important signal characteristics associated with tornado *** X-band phased array radar,with its high temporal and spatial resolution,provided an opportunity to capture early warning signals from polarimetric *** 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 ***,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 *** 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

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Assessment of ECMWF’s Precipitation Forecasting Performance for China from 2017 to 2022

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Journal of Tropical Meteorology 2024年第3期30卷 257-274页

作者： PAN Liu-jie ZHANG Hong-fang LIANG Mian LIU Jia-huimin DAI Chang-ming Shaanxi meteorological observatory Xi’an 710014 China Key Laboratory of Eco-Environment and Meteorology for the Qinling Mountains and Loess Plateau Xi’an 710014 China Shaanxi meteorological Service Centre Xi’an 710014 China

This study used the China meteorological Administration(CMA)three-source fusion gridded precipitation analysis data as a reference to evaluate the precipitation forecast performance of the European Centre for Medium-Range Weather Forecasts(ECMWF)model for China from 2017 to *** main conclusions are as *** precipitation forecast capability of the ECMWF model for China has gradually improved from 2017 to *** scores such as bias,equitable threat score(ETS),and Fractions Skill Score(FSS)showed improvements for different categories of *** bias of light rain forecasts overall adjusted towards smaller values,and the increase in forecast scores was greater in the warm season than in the cold *** ETS for torrential rain more intense categories significantly increased,although there were large fluctuations in bias across different *** model exhibited higher precipitation bias in most areas of North China,indicating overprediction,while it showed lower bias in South China,indicating *** ETSs indicate that the model performed better in forecasting precipitation in the northeastern part of China without the influence of climatic background *** of the differences between the first period and the second period of the forecast shows that the precipitation amplitude in the ECMWF forecast shifted from slight underestimation to overestimation compared to that of CMPAS05,reducing the likelihood of missing extreme precipitation *** improvement in ETS is mainly due to the reduction in bias and false alarm rates and,more importantly,an increase in the hit *** 2017 to 2022,the area coverage error of model precipitation forecast relative to observations showed a decreasing trend at different scales,while the FSS showed an increasing trend,with the highest FSS observed in *** ETS followed a parabolic trend with increasing neighborhood radius,with the better ETS neighborhood radius generally

关键词： ECMWF forecasting verification neighborhood verification FSS

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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-4.(FY-4.)Lightning Mapping Imager(FY-4./LMI)and the Lightning Imaging Sensor(LIS)on the International Space Station(ISS),we extract the“event”type data as the lightning detection *** observations are then compared with the cloud-to-ground(CG)lightning observation data from the China meteorological *** study focuses on the characteristics of lightning activity in Southeast China,primarily in Jiangxi province and its adjacent areas,from April to September,2017–*** 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 *** findings indicate that the lightning frequency and density of the FY-4./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 *** general,the high-value areas of lightning density for the FY-4./LMI are located in inland mountainous *** lower the latitude is,the higher the CG lightning density ***-value areas of the CG lightning density are more likely to be located in eastern Fujian and southeastern Zhejiang ***,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 ***,the mountainous terrain is closely related to the high-value areas of the lightning *** locations of the high-value areas of the lightning density for the FY-4./LMI correspond well with those for the CG observations,and the seasonal variations are also *** contrast,the distribution of the high-value areas of the lightning density for the ISS-LIS is more *** positions of the peak frequency of the FY-4./LMI lightnin

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

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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 *** 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 *** 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 *** 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 *** reasons for this are as *** 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 ***,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 ***,by combining the appropriate anomalous features with multi-model fusion,an integrated improvement of the forecast of the rainfall intensity and location is ***,this study is a novel exploration to improve the refined forecasting of heavy precipitation with extreme intensity and high variability,

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

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Evolution Mechanism of a Severe Squall Line Triggered by the Coupling of a Sea Breeze Front and a Gust Front

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Journal of Tropical Meteorology 2024年第2期30卷 200-210页

作者：万夫敬孙敏庞华基赵海军赵传湖 Qingdao meteorological Bureau QingdaoShandong 266003 China Shanghai Central meteorological observatory Shanghai 200030 China Linyi meteorological Bureau LinyiShandong 276037 China Frontier Science Center for Deep Ocean Multispheres and Earth System(FDOMES)and Physical Oceanography Laboratory Ocean University of ChinaQingdaoShandong 266101 China

In the present study, a severe squall line(SL) was analyzed by using intensive observational surface data and radar monitoring products. In this process, mesoscale convergence lines, such as the sea breeze front(SBF), gust front and dry line, served as the main triggering and strengthening factors. The transition from convection triggering to the formation of the initial shape was mainly affected by the convergence line of the SBF, which combined with thermal convection to form the main parts of the SL. In the later stage, the convergence line of the gust front merged with other convergence lines to form a series of strong convective cells. The SBF had good indicative significance in terms of severe convective weather warnings. The suitable conditions of heat, water vapor and vertical wind shear on the Shandong Peninsula were beneficial to the maintenance of the SL. Before SL occurrence, tropopause folding strengthened, which consequently enhanced the baroclinic property in the middle and upper troposphere. The high sensible heat flux at the surface easily produced a positive potential vorticity anomaly in the low layer, resulting in convective instability, which was conducive to the maintenance of these processes. In the system, when precipitation particles passed through the unsaturated air layer, they underwent strong evaporation, melting or sublimation, and the cooling effect formed negative buoyancy, which accelerated the sinking of the air and promoted the sustained development of the surface gale. Together with the development of lowlevel mesocyclones, the air pressure decreased rapidly, which was conducive to gale initiation.

关键词： squall line sea breeze front gust front cold pool thunderstorm gale

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Machine Learning-Based Temperature and Wind Forecasts in the Zhangjiakou Competition Zone during the Beijing 2022 Winter Olympic Games

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Journal of meteorological Research 2024年第4期38卷 664-679页

作者： Zhuo SUN Jiangbo LI Ruiqiang GUO Yiran ZHANG Gang ZHU Xiaoliang YANG China meteorological Administration Xiong'an Atmospheric Boundary Layer Key Laboratory Xiong'an New Area 071800 Hebei Key Laboratory of Meteorology and Ecological Environment Shijiazhuang 050021 Hebei meteorological observatory Shijiazhuang 050021 College of Computer and Cyber Security Hebei Normal UniversityShijiazhuang 050024

Weather forecasting for the Zhangjiakou competition zone of the Beijing 2022 Winter Olympic Games is a challenging task due to its complex *** weather prediction models generally perform poorly for cold air pools and winds over complex terrains,due to their low spatiotemporal resolution and limitations in the description of dynamics,thermodynamics,and microphysics in mountainous *** study proposes an ensemble-learning model,named ENSL,for surface temperature and wind forecasts at the venues of the Zhangjiakou competition zone,by integrating five individual models—linear regression,random forest,gradient boosting decision tree,support vector machine,and artificial neural network(ANN),with a ridge regression as meta *** ENSL employs predictors from the high-resolution ECMWF model forecast(ECMWF-HRES) data and topography data,and targets from automatic weather station *** categories of predictors(synoptic-pattern related fields,surface element fields,terrain,and temporal features) are fed into *** results demonstrate that ENSL achieves better performance and generalization than individual *** root-mean-square error(RMSE) for the temperature and wind speed predictions is reduced by 4..2% and 28.5%,respectively,relative to *** the gust speed,the performance of ENSL is consistent with ANN(best individual model) in the whole dataset,whereas ENSL outperforms on extreme gust samples(4..7% compared with 38.7% obtained by ECMWF-HRES in terms of RMSE reduction).Sensitivity analysis of predictors in the four categories shows that ENSL fits their feature importance rankings and physical explanations effectively.

关键词： machine learning ensemble learning post-processing cold air pools mountain Beijing 2022 Winter Olympic Games

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