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An Initial Perturbation Method for the Multiscale Singular Vector in Global Ensemble Prediction

Advances in Atmospheric Sciences 2024年第3期41卷 545-563页

作者： Xin LIU Jing CHEN Yongzhu LIU Zhenhua HUO Zhizhen XU Fajing CHEN Jing WANG Yanan MA Yumeng HAN School of Atmospheric Sciences Nanjing University of Information Science&TechnologyNanjing 210044China Chinese Academy of meteorological Sciences Beijing 100081China CMA Earth System Modeling and Prediction Centre(CEMC) Beijing 100081China Key Laboratory of Earth System Modeling and Prediction of China meteorological Administration Beijing 100081China tianjin meteorological observatory Tianjin 300074China China meteorological Administration Xiong’an Atmospheric Boundary Layer Key Laboratory Hebei 071799China

Ensemble prediction is widely used to represent the uncertainty of single deterministic Numerical Weather Prediction(NWP) caused by errors in initial conditions(ICs). The traditional Singular Vector(SV) initial perturbation method tends only to capture synoptic scale initial uncertainty rather than mesoscale uncertainty in global ensemble prediction. To address this issue, a multiscale SV initial perturbation method based on the China meteorological Administration Global Ensemble Prediction System(CMA-GEPS) is proposed to quantify multiscale initial uncertainty. The multiscale SV initial perturbation approach entails calculating multiscale SVs at different resolutions with multiple linearized physical processes to capture fast-growing perturbations from mesoscale to synoptic scale in target areas and combining these SVs by using a Gaussian sampling method with amplitude coefficients to generate initial perturbations. Following that, the energy norm,energy spectrum, and structure of multiscale SVs and their impact on GEPS are analyzed based on a batch experiment in different seasons. The results show that the multiscale SV initial perturbations can possess more energy and capture more mesoscale uncertainties than the traditional single-SV method. Meanwhile, multiscale SV initial perturbations can reflect the strongest dynamical instability in target areas. Their performances in global ensemble prediction when compared to single-scale SVs are shown to(i) improve the relationship between the ensemble spread and the root-mean-square error and(ii) provide a better probability forecast skill for atmospheric circulation during the late forecast period and for short-to medium-range precipitation. This study provides scientific evidence and application foundations for the design and development of a multiscale SV initial perturbation method for the GEPS.

关键词： multiscale uncertainty singular vector initial perturbation global ensemble prediction system

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Improving the Forecasts of Coastal Wind Speeds in tianjin,China Based on the WRF Model with Machine Learning Algorithms

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Journal of meteorological Research 2024年第3期38卷 570-585页

作者： Weihang ZHANG Meng TIAN Shangfei HAI Fei WANG Xiadong AN Wanju LI Xiaodong LI Lifang SHENG College of Oceanic and Atmospheric Sciences Ocean University of ChinaQingdao266100China tianjin Key Laboratory for Oceanic Meteorology Tianjin Institute of Meteorological ScienceTianjin300074China CMA Earth System Modeling and Prediction Centre China Meteorological Administration(CMA)Beijing100081China Qingdao meteorological Bureau Qingdao266003China

Characterized by sudden changes in strength,complex influencing factors,and significant impacts,the wind speed in the circum-Bohai Sea area is relatively challenging to *** the western side of Bohai Bay,as the economic center of the circum-Bohai Sea,tianjin exhibits a high demand for accurate wind *** this study,three machine learning algorithms were employed and compared as post-processing methods to correct wind speed forecasts by the Weather Research and Forecast(WRF)model for *** results showed that the random forest(RF)achieved better performance in improving the forecasts because it substantially reduced the model bias at a lower computing cost,while the support vector machine(SVM)performed slightly worse(especially for stronger winds),but it required an approximately 15 times longer computing *** back propagation(BP)neural network produced an average forecast significantly closer to the observed forecast but insufficiently reduced the *** regard to wind speed frequency forecasting,the RF method commendably corrected the forecasts of the frequency of moderate(force 3)wind speeds,while the BP method showed a desirable capability for correcting the forecasts of stronger(force>6)*** addition,the 10-m u and v components of wind(u_(10)and v_(10)),2-m relative humidity(RH_(2))and temperature(T_(2)),925-hPa u(u925),sea level pressure(SLP),and 500-hPa temperature(T_(500))were identified as the main factors leading to bias in wind speed forecasting by the WRF model in tianjin,indicating the importance of local dynamical/thermodynamic processes in regulating the wind *** study demonstrates that the combination of numerical models and machine learning techniques has important implications for refined local wind forecasting.

关键词： machine learning Weather Research and Forecast(WRF)model wind speed forecasting coastal region

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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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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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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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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”*** 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 *** 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 *** significant increase(decrease)in atmospheric diabatic heating and kinetic energy preceded the increase(decrease)in *** 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 *** dynamic uplift of the southwest vortex triggered rainfall,and the release of condensation latent heat from rainfall further strengthened the development of the southwest *** 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 *** the“8·11”process,the heating effect was the strongest in the initial stage,but weakened during the vortex's *** 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 rotatio

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

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Construction of daily precipitation series and the observational characteristics of extreme precipitation in tianjin,China during 1888-2022

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Advances in Climate Change Research 2024年第1期15卷 52-61页

作者： Peng SI Qing-Xiang LI Xiao-Yang CHEN Min WANG Chuan-Jun LUO tianjin meteorological Information Center Tianjin Meteorological BureauTianjin 300074China Research Centerfor Ecology and Environment of Central Asia Chinese Academy of SciencesWulumuqi 830011China School of Atmospheric Sciences Sun Yat-sen UniversityZhuhai 528478China Key Laboratory of Tropical Amosphere-Ocean System Ministry of EducationZhuhai 528478China Shaoguan meteorological Service Shaoguan 512028China

Given the difficulties in rescuing and ensuring the quality of long-term climate data,current studies on century-scale climate change are usually limited to annual and monthly data,resulting in the poor detection of extreme climate events and their changes before *** this study,we reconstructed a daily precipitation series for tianjin from 15 September 1887 to 31 December 2022 on the basis of the most comprehensive daily precipitation records collected from the tianjin meteorological Archive,China,and in reference to the precipitation analysis results based on the datasets developed by the Climatic Research Unit Time-Series version 4.06,Global Precipitation Climatology Centre and University of Delaware along with the application of various homogenisation methods for climate *** approach provides a complete and reliable century-long daily precipitation series for the study of regional or local extreme weather and climate *** reconstructed daily dataset reveals that the annual precipitation amount and R95 intensity in tianjin during 1888-2022 lack significant trends and have values of 0.74±6.99 and-1.84±3.22 mm per decade,*** the annual and seasonal scales,the precipitation amount and R95 intensity,particularly those in autumn,have increased since the latter half of the 20th century relative to those in ***,the increase in precipitation amount and R95 intensity is relatively limited compared with that in atmospheric water vapour content due to surface warming,indicating the highly sensitive response of extreme precipitation events to *** addition,the estimates for the return periods of 5,10,20,50 and 100 years covering 1888-2022,1888-1950 and 1951-2022 depict that the intensity of heavy rain and above magnitude was highest in 1888-1950 and decreased in 1951-2022.

关键词： Over a century-long timescale Daily precipitation series Extreme precipitation Return period

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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%,42.55%,and 47.49%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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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 *** 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-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 *** general,the high-value areas of lightning density for the FY-4A/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-4A/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-4A/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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