In February 2024, Hunan encountered two regional rainfall/snowfall and freezing events with low temperatures, one in northern Hunan from February 2 to 6 (hereinafter referred to as the“2.02”event), and the other across the whole province from February 21 to 26 (hereinafter referred to as the“2.21”event). Using conventional upper-air and surface weather observations, regional automatic weather station data, and NCEP reanalysis data, we conducted a comparative analysis of the circulation background, cooling mechanism, and water vapor transport characteristics of the two events. The results are as follows. (1) There are obvious differences in the circulation background between the two events. The“2.02”event occurred in the background with eastward movement of both the southern branch trough and westerly trough overlapping in phase. However, the“2.21”event occurred under the cold wave caused by the collapse of the blocking high, with the transition of rain and snow phases appearing multiple times due to the small influence on the southern branch trough. (2) There were the strong low-level frontal zone in both events. A rare north-south temperature gradient greater than 26 ℃ was observed in the“2.21”event, which may be an important indication of this extreme cold wave weather. (3) Temperature advection played an important role in the transition of rain and snow phases. In the“2.02”event, the strong cold advection intrusion in the middle level was driven by the westerly trough, causing a temperature decrease across the whole layer. In the“2.21”event, the continuous input of the ultra-low-level strong cold advection was one of the important cooling mechanisms. In addition, the strongest transport layer of warm advection corresponded to the negative center of vertical velocity, and the stronger vertical wind shear in the middle and lower troposphere resulted in stronger rain and snow intensity. (4) The Arabian Sea was the main source of water vapor for both events. Southwest warm and wet airflow in the middle troposphere moved with the low trough at 500 hPa or the small trough from it, with the highest contribution of specific humidity and water vapor flux to both events. The low troughs provided both favorable dynamical conditions and abundant water vapor for the occurrence of rain and snow weather. In the “2.21”event, the warm and wet air carried by three combined channels from the South China Sea channel, the Arabian Sea channel and the low latitude inland regions provided water vapor and warm layer conditions for the freezing area in the southern Hunan, which was an important reason for the severe freezing in this area.

Based on the daily precipitation data and ERA5 reanalysis data of 40 years from 1981 to 2018 in the middle Yangtze River Valley (MYRV), the climatic characteristics of extreme precipitation are analyzed using statistical methods. The multivariate empirical orthogonal functions and spectral clustering methods are used to classify and synthesize the extreme precipitation weather. The results show that: (1) The spatial distribution of the extreme precipitation threshold is uneven due to the regional topography. The spatial distribution of the average precipitation and frequency of extreme precipitation days is characterized by the north-south antiphase distribution. (2) According to the main influencing systems, the 215 regional extreme precipitation days in the MYRV in the past 40 years can be classified into three types: southwest vortex type, typhoon type, and cold trough shear line type. (3) The southwest vortex type of extreme precipitation occurs in the deep warm and humid airflow in front of the southwest vortex trough, but the typhoon type has better thermal dynamic conditions, and the cold and warm airflow convergence of the cold trough shear line type is more obvious. The rainfall area of three types of extreme precipitation is the result of the synergistic effect of the system.

AbstractIn early 2022, there were four low‐temperature weather processes with rain and snow in Hunan Province, China. Two processes occurred on January 28–29 (referred to as the “0128” process) and February 6–7 (referred to as the “0206” process), and they have overlapping areas of heavy snowfall and high intensity of short‐term snowfall. Multi‐source observation data and the National Centers for Environmental Prediction (NCEP) reanalysis data are used to analyze the characteristics of circulation background and mesoscale. In addition, the causes of heavy snowfall processes under the influence of the southern branch trough are discussed based on the dual‐polarization radar products at Changsha station. The results show that two processes are characterized by the rapid phase transformation of rain and snow, concentrated snowfall periods, and heavy snowfall at night. The short‐term snowfall intensity of the “0206” process is greater than that of the “0128” process. The high‐latitude blocking high of the “0206” process is stronger than that of the “0128” process, and the water vapor transport of the southerly jet in low levels in the “0206” process is also stronger. The organized development of cold cloud clusters from the meso‐β scale to the meso‐α scale indicates that the snowfall intensifies, and the maximum blackbody temperature gradient corresponds well to the center of heavy snowfall. The propagation that is similar to the train effect is an important reason for the heavy snowfall process. The vertical variation of the ZH and the bright band of dual‐polarization parameters can determine the phase transformation between rain and snow. When the ZH and ZDR bright bands are 1–3 km away from the ground, the phase state is rain if the ZH near the ground is greater than 0 dBZ and the CC is close to 1; the phase state is the rain‐snow mixed phase if the CC is less than 0.95. When the bottom of the ZH bright band decreases, the CC/ZDR bright band disappears, the near‐surface CC is greater than 0.99 and the ZDR is less than 1 dB, the rain turns to snow. Compared with the “0128” process, the characteristics of the bright ring during the rainfall period of the “0206” process are more obvious, the precipitation intensity judged from the larger ZH and KDP is larger, and the phase transformation is faster due to more significant cooling effect caused by precipitation.

An extreme rainstorm weather process occurred in Hunan during the Dragon Boat Festival from June 1 to June 5, 2022, which was the strongest ‘Dragon Boat Water’ in the past 20 years, causing serious secondary disasters. Based on the conventional observation data, NCEP reanalysis data, FY-4 satellite TBB data and HYSPLITv4.9 trajectory tracking model, the prediction deviation and causes of the rainstorm are analyzed from the large-scale background, mesoscale characteristics and water vapor transport. The results show that: (1) Extreme ‘Dragon Boat Water’ can be divided into two distinct rainfall stages: warm-sector torrential rain and frontal torrential rain. In the first stage, the shear line weakened at daytime and strengthened at night increased the difficulty of forecast. In the second stage, the cold air and shear made several β, γ scale clouds develop and merge into medium α scale clouds, resulting in systematic heavy rainfall. (2) The intensity of water vapor convergence in the first stage was greater than that in the second stage, which was one of the factors that the maximum hourly rainfall intensity appears in the first stage. (3) There were four water vapor transport channels of the extreme rainstorm, the main was the Arabian Sea. There was a rare short-distance cold and humid water vapor transport channel near the Hetao area (accounting for 20

【Objective】The high-resolution hydrographic datasets have significantly influence on hydrological simulation， such as runoff in flood inundation modeling. 【Method】In this study， we derived a new raster hydrography map （flow direction， flow accumulation， drainage area and flow distance etc.） by using GIS information system and python programs in Dongting River basin from MERIT DEM data （～90 m） which is a latest released digital elevation model. The new results show robust performance in visual inspection， basin area， flow distance and model evaluation. 【Result】The new hydrographic databases show some big difference in terms of DEM， flow direction， river network structure and river length compared to the traditional databases （HydroSHEDS）. The results of the hydrological model show that the daily and monthly Nash coefficients of the MERIT datasets are 0.41 and 0.52， respectively， which are better than HydroSHEDS. 【Conclusion】The MERIT can more realistically reflect the location of the river in the study area and provide for many applications to reduce uncertainties. The new hydrography map will be available for academic research and education purpose.

为准确评估湖南省长沙县暴雨灾害危险性,通过调查1951～2020年历史气象数据、灾情数据和最新的承灾体信息,建立风险普查数据库,综合考虑暴雨灾害多项气象致灾因子和地形、河网水系等孕灾环境的影响,开展暴雨基本特征分析和灾害危险性评估,形成危险性评估模型.结果表明,长沙县地质灾害主要出现在长沙县东北部区域;城市内涝灾害主要影响县城和周边城镇化较高地区;山洪灾害主要影响长沙县中部和北部地区.长沙县暴雨灾害危险性分布自东南、东北向中部呈减弱趋势,开慧镇、高桥镇、黄花镇为高风险,暴雨事件在这三个乡镇容易造成次生灾害;全县存在两个相对高值中心,分别位于东南部黄花镇、江背镇及北部开慧镇、高桥镇;中部的安沙镇、果园镇、春华镇和县城附近暴雨灾害危险性等级低.

Using meteorological analysis, composite analysis and water vapor trajectory analysis, the extreme value rainstorm process in northwest Hunan was analyzed. The results show that three types are summarized: the Southwest Vortex and Warm Shear Line Pattern (SVWSLP), the Subtropical High Edge Pattern (SHEP) and the Cold Trough and Shear Line Pattern (CTSLP). The main influence systems are upper trough, southwest vortex, shear line, low-level jet and subtropical high edge. For SVWSLP, the water vapor transport channels are only from the low-latitude ocean whether it is affected by long-distance typhoons. For SHEP, the main water vapor channel comes from the long-distance ocean and is finally transported to northwestern Hunan around 650 hPa in the form of warm and wet airflow, whether it is affected by long-distance typhoons. The CTSLP appears a significant water vapor confrontation between the north and the south and the baroclinicity of the atmosphere in the rainstorm area. The southern and western boundaries are the input boundary, while the eastern and northern boundaries are the outflow boundary. Therefore, one of the three types of weather systems appears in northwestern Hunan in May–August, with strong water vapor transport from the ocean surface, which is likely to cause extreme rainstorm.

针对近年来湖南省基层管理部门所遇到的影响公众生活和社会安全的突发事件,将突发事件分类为气象灾害影响与非气象灾害影响2类,重点从雨雪冰冻和暴雨强对流实例分析着手,探讨了如何采取人性化新型管理模式,及时、有效地应对可能发生的突发事件,并提出了具体的防御措施.

该文利用地面降水资料、气象灾情上报资料,结合前期暴雨、强对流及低温雨雪冰冻研究成果,开展 3 类气象灾害特征及影响分析.结果表明,5 月中下旬—7 月上旬受西南涡东移、高空低槽、中低层切变,副高边缘等系统影响,暴雨大暴雨过程频繁,为湖南雨水集中期,湖南区域性特强暴雨过程主要发生在 6 月下旬—8 月中旬;强对流天气主要发生在春夏季,受西南涡、地面倒槽及台风登陆台前飚线等影响,4 月雷雨大风最多,3 月冰雹多发,短时强降水多发生在 6-8 月,降水强度强,影响范围广;低温雨雪冰冻天气主要发生在 12-2 月,中高纬为乌拉尔山阻塞高压,地面有强冷空气影响背景下,当南支槽东移,西南气流旺盛,并满足一定的温度层结条件,有暴雪冰冻发生;人员伤亡主要由暴雨引发的洪涝和地质灾害造成,其次是强对流天气造成,经济损失主要与发生灾害的次数和影响时间、范围有关.

Due to the unique topography and geographical location, severe snowfall is the main disastrous weather in winter in the Hunan Province of China. Based on the daily precipitation data in Hunan Province from 1961 to 2021, the regional heavy snowfall processes are classified by using the synoptic diagnostic method. In addition, the water vapor transport characteristics of typical heavy snowfall processes are analyzed by the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) air mass backward trajectory model. Then, the responses of the differences in water vapor transport to heavy snowfall under different weather situations are discussed. The results show that the spatial distribution of climatic mean heavy snowfall days in Hunan Province is extremely uneven, and the heavy snowfall days decrease from north to south, with the most in the Dongting Lake area and the least in the Nanling Mountains. In the past decades, snowstorms mainly occur in local areas, and there are fewer widespread snowstorms. The frequency of heavy snowfall days generally shows a decreasing trend, with three peaks all appearing before 1990. After the 2010s, the number of days and stations of heavy snowfall decreased noticeably, and so did the number of regional heavy snowfall processes. This result indicates that global warming has remarkable effects on the snowstorm events in Hunan Province. Heavy snowfall mainly occurs from December to February, and peaks from mid-January to early February. Over the past 61 years, more than 50% of heavy snowstorm events occurred after 2000. According to the main weather systems affecting regional heavy snowfall processes, these weather processes in Hunan Province can be classified into three categories: southern branch trough (SBT) type, blocking high collapse (BHC) type, and stepped trough type. Among them, the SBT type accounts for more than 60% of the heavy snowfall events in Hunan. In terms of the SBT type and the stepped trough type, the water vapor from the high-latitude inland and low-latitude sea surface accounts for a comparable proportion, each accounting for nearly 50%. For the SBT type, the proportion of the water vapor from warm-humid airflows is slightly higher than that from cold-humid airflows. However, in terms of the stepped trough type, the water vapor transported by cold-humid airflows from the north contributes more than that by warm-humid airflows. For the BHC type, the specific humidity and the water vapor from the high-latitude inland contribute 70% of heavy snowfall processes. In addition, the contribution of the two southwesterly water vapor channels to heavy snowfall processes is small. The water vapor sources differ remarkably for different heavy snowfall types, but all of them are dominated by the water vapor transport in the middle and lower troposphere, which is the main reason why the formation of snowfall areas under different weather types is obviously different.

2022年2月6—7日(简称"2.6"过程)和21—22日(简称"2.22"过程)湖南出现了两次灾害性大暴雪天气过程,利用多源观测资料与再分析资料,通过HYSPLIT v4.9(空气质点轨迹追踪模式)及聚类算法,从环流背景、温度条件及水汽特征探讨两次强降雪特征及成因差异.分析表明:①"2.6"过程降雪持续时间短,降雪时段集中,小时雪量大;"2.22"过程强降雪持续时间长,累积降雪量大,部分站点雪深破历史极值,具有显著极端性.②两次过程均受南支槽影响,其中"2.22"过程南支槽更深厚,地面上两个强大的高压中心持续补充冷空气,造成长时间的地面低温.③温度平流差异明显,"2.6"过程主要受高空和地面冷平流共同作用造成温度下降,"2.22"过程由中层冷平流驱动冷空气下传,长时间维持深厚冷垫和10 m/s以上的边界层东北回流是造成持续性强降雪的重要成因.④两次大暴雪过程的主要水汽通道均是来自阿拉伯海的西南输送带,另外,还有一支来自西太平洋的偏东水汽输送带,经南海转为偏南路径向北输送至暴雪区,这也是湖南冬季暴雪需要特别关注的水汽传输路径.

Based on conventional observation data from the China Meteorological Administration (CMA) and reanalysis data from the American National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) between 2012 and 2021, combined with the meteorological analysis, composite synthesis, and water vapor trajectory analysis, the weather circulations of typical rainstorms during the 10 years can be divided into 4 categories: Static Front Pattern (SFP), Subtropical High Edge Pattern (SHEP), Northeast Cold Vortex Pattern (NCVP), and Low-Level Vortex and Shear Pattern (LLVSP). The SHEP and SFP rainstorms have the characteristics of long duration and wide range, while the NCVP rainstorms are characterized by mobility and disaster weather accompaniment. The daily precipitation of LLVSP cases has extremity feature. The occurrence and development of rainstorms are well coordinated with the systems on lower levels. The main water vapor channel in lower layers of the SFP cases is from the South China Sea, while it is from Bohai for the NCVP cases and the Bay of Bengal for the SHEP and LLVSP cases. The main water vapor channel in middle layers is from the Bay of Bengal because of the affection of the southwest air flow. The south boundary of the MLYRB is the most important water vapor input boundary, followed by the west boundary, while the East and North boundaries are the outflow boundaries. During the rainstorms, the low-level water vapor is exuberant with low-level water vapor convergence much stronger than the high-level divergence. Among the four types of rainstorms, the NCVP cases provide the most abundant low-level water vapor convergence, resulting in the strongest short-term precipitation among the four types. Combined with water vapor transportation and convergence, the refined spatial conceptual models of the four types of rainstorms can better judge the process intensity and falling area and provide reference for disastrous weather forecast and early warning.