1981～2010年北京地区极端降水变化特征

采用北京地区20个常规气象站1981~2010年逐日降水数据,对北京地区极端降水的空间分布特征进行了分析。得到以下主要结论:1981~2010年,北京地区极端降水百分位数(第90、95和99个百分位数)阈值表现出较一致的空间分布特征,以第95个百分位数阈值计算的极端降水日数与降水阈值和降水量的分布有较大差异,极端降水量对总降水量的贡献可达30%~37%,极端降水强度分布与极端降水阈值分布相似。近30年,北京地区多数站点的极端降水量、降水日数和降水强度呈下降趋势,极端降水量以上甸子、怀柔、平谷和观象台下降较为明显,可达到40 mm(10 a)–1以上,极端降水强度以顺义、海淀、观象台、大兴和上甸子等站下降较为显著,每10 a降水强度减小趋势可达4 mm d–1,极端降水日数变化分布与极端降水量变化分布类似,极端降水强度变化与降水量和降水日数变化的分布有明显不同。     

1965—2019年中国夏季分钟降水空间分布与长期趋势分析

短时强降水可引起洪涝等气象灾害,揭示其气候分布和长期变化特征对于防灾、减灾和气候变化应对等工作非常重要。利用较为完整的中国夏季1965—2004年的自记纸（2225站）以及2005—2019年的自动站（2435站）分钟降水数据,分析了中国大陆分钟降水的空间分布和长期变化特征。结果表明,中国夏季分钟降水频率基本呈现从北到南增加的特征,分钟降水强度在西北地区较小,东部平原地区较大。1 min、连续5 min最大一次降水量在内蒙古、华北最多,连续10 min以上的最大一次降水量和年平均最大降水量在华南沿海地区最多,上述指标的最小值都出现在南疆一带。1965—2004年以及2005—2019年,夏季分钟降水频率都存在增加趋势,是引起中国平均夏季降水量增加的主要原因;分钟降水强度则没有明显趋势性变化。1965—2019年数分钟到一日的最大降水量距平百分率都显著增加,其中连续半小时左右的最大降水量增加最快。总体来看,中国大陆每分钟的降水并未趋向于极端化,极端降水量的增加可主要归因于暴雨过程持续时间的增加。      

基于小时降水资料研究广东省降水分布特征

基于2001-2018年广东省86个国家自动气象站逐小时降水资料,分析了广东省不同历时降水的时空分布特征.结果表明：1）除粤北山区外,基本符合年均降水时数越多,累积降水量越大的规律.年均小时降水强度从南部沿海向北部内陆呈减弱趋势.2）汛期降水事件以短历时为主,占全年降水事件65.3%;累积降水量上,长历时降水量占汛期56.7%.前汛期短历时降水多发生在粤西;中历时降水多发生在珠三角两侧和粤西北地区;长历时降水多发生在粤东和粤北地区.后汛期短历时降水多发生在内陆,出现频次自西北向东南递减;中历时降水分布不均;长历时降水多发生在沿海.3）汛期降水时数日变化呈双峰型变化特征,小时降水强度日变化呈单峰型变化特征.小时降水强度峰值易出现在下午的站点多分布在内陆,小时降水强度峰值易出现在下半夜至上午时段的站点则多分布在沿海、部分山区和珠三角地区.     

雄安新区及上游极端降水特征分析

选取1981—2019年河北省雄安新区及上游保定地区共19个国家观测站的逐日降水资料,基于百分位法和线性倾向估计法,对上述地区极端降水的时空分布和演变趋势等进行了分析.结果表明:各百分阈值具有相似性,以第95百分位数作为极端降水阈值得到的各站年平均极端降水量及其在总降水量中所占比例的分布情况大体一致,大值区集中在山区到平原的过渡地区.2001年起极端降水总站次在波动中上升;7—8月极端降水最为频繁,10月平均极端降水日站次最多.年平均极端降水量、日数及其在总降水量中所占比例的变化趋势较为一致,阜平、西南部浅山区以及雄安新区北面的高碑店呈递增趋势;年平均极端降水强度呈增加的站点分布较为广泛.浅山区极端降水强度最大;山区年平均极端降水日数最多;平原年平均极端降水量最小.5—8月浅山区的年均极端降水强度均为各区域中最大;平原次之;山区最小.7月各区域年平均极端降水日数均在1d以上;山区5—9月年平均极端降水日数均为最多.     

中国大陆地区小时极端降水阈值的计算与分析

利用广义极值分布和百分位两种阈值确定方法,对我国465个气象站点不同极端程度的小时降水强度阈值进行了分析。广义极值分布结果表明,重现期为2、5、10、50a的降水强度阈值具有一致的空间分布特征：华南沿海阈值最高,长江中下游地区北部、四川盆地西部、华北地区东部次之,云南中西部、华北西部和东北西部阈值相对更低,最小值出现在我国西部地区。百分位法得到的阈值空间分布呈现出与广义极值分布结果较为一致的东南大、西北小的整体特征。考察465站中位数发现,第99．9百分位的强度阈值与二年一遇降水的阈值接近。具体分析各站第99．9百分位降水阈值对应的重现期发现,长江流域及其以南地区重现期大多低于2a;35°N一带重现期长于4a;我国北方和西北部分地区重现期长于8a。     

基于Copula函数的绕阳河流域降水事件危险性评估

利用1961—2021年绕阳河流域周边8个气象站的逐日降水资料,以降水持续时间和降水强度作为判断降水事件的两个要素,运用Copula函数确定降水事件的联合概率和重现期,并构建危险性评估模型,分析绕阳河流域降水事件危险性空间特征。结果表明：绕阳河流域周边不同地区降水事件的持续时间和降水强度的最大值存在显著差异。Lognormal和Logistic函数对两个降水要素的拟合效果较好,Clayton函数适合反映两要素联合下降水发生的概率。随着降水持续时间和降水强度的增加,极端降水事件的危险性快速升高;不同地区降水事件的概率和重现期存在着显著差异,阜新站降水持续7 d,强度达到60 mm的事件每10 a出现一次,而盘锦站和北镇站发生持续6 d以上的降水事件较少,多为20 a一遇;降水事件危险性高值区处于绕阳河流域南部,主要位于盘锦、辽中和北镇。     

北京地区夏季极端降水变化特征及城市化的影响

应用北京地区20站1971—2010年降水记录及城市发展数据,采用百分位方法定义极端降水事件的阈值,分析了北京地区夏季极端降水事件的时空变化特征及城市化的影响。结果表明：(1)北京夏季极端降水阈值及频数存在较强局地性特征,基本沿地形高度分布,极端降水频数多发区与高阈值区不完全对应;(2)近40年极端降水频率及强度均呈现下降趋势,年际及年代际差异显著;(3)城市化发展不同阶段极端降水强度及频数均有不同的分布形态,城市化对城市不同区域极端降水影响不一样,城市化导致城市下风向近郊区极端降水强度、次数均表现为增多趋势;(4)城市对极端降水的影响还与天气过程强度有关,强天气背景下城市对极端降水频数的影响程度高于对降水强度的影响。     

1955—2014年杭州极端气温和降水指数变化特征

根据杭州市1955—2014年降水量、气温逐日资料,采用国际通用的极端天气指数和线性倾向估计、M-K检验等方法,分析了杭州市近60 a极端气温和降水的变化特征。结果表明：1）杭州市近60 a的气温呈一致升高趋势,且变化显著,表现为极端高温阈值和极端低温阈值的升高及极端高温日数的增多;极端冷事件显著减少,暖事件显著增多。2）极端降水指数中只有强降水量的增加较明显,主要贡献为夏季和冬季强降水量的增强。3）冬季平均气温、极端低温阈值、霜冻日数等极端冷事件的突变发生于20世纪80年代初中期,夏季平均气温、极端高温阈值、高温日数等极端暖事件的突变发生于20世纪末21世纪初,与全国范围内的气候增暖进程基本一致。另外,降水强度、极端降水阈值等极端降水指数的突变时间在2008年左右,即2008年后气温升高和降水强度的增加突变期叠加,尤其在夏季和冬季表现更突出,可能诱发更多的异常天气。     

新疆天山山区夏季极端降水的日变化特征

基于天山山区11个国家气象站2012—2018年夏季（6—8月）逐小时降水资料，使用百分位法计算极端降水阈值，分析极端降水特征量（包括极端降水量、极端降水频次、极端降水强度、极端降水贡献和极端降水量最大值）的日变化特征，揭示极端降水与海拔高度的关系。结果表明：87°E以东地区，极端降水量最大值出现的时间大致都存在自西向东顺时针变化的特点。极端降水主要以短持续性为主，极端降水贡献和极端降水量最大值的谷值都出现在白天。极端降水与海拔密切相关，总极端降水频次更多发生在高海拔地区，在海拔2 000 m左右存在一个极端降水最大值带。     

1961—2015年通辽市极端降水量的变化分析

利用通辽市9个站1961—2015年逐日降水资料,统计分析极端降水量、降水强度和年最大日降水量的空间分布特征和时间变化趋势。结果表明,通辽市极端降水量、极端降水强度和年最大日降水量近55a来没有明显增加或减少变化趋势,但存在明显的年代际特征;三者的空间特征基本相似,由南向北总体上呈高—低—高的分布特征;通辽市年降水总量主要来自极端降水量的贡献。     

Duration and Seasonality of Hourly Extreme Rainfall in the Central Eastern China

Compared with daily rainfall amount, hourly rainfall rate represents rainfall intensity and the rainfall process more accurately, and thus is more suitable for studies of extreme rainfall events. The distribution functions of annual maximum hourly rainfall amount at 321 stations in China are quantified by the Generalized Extreme Value（GEV） distribution, and the threshold values of hourly rainfall intensity for 5-yr return period are estimated. The spatial distributions of the threshold exhibit significant regional diferences, with low values in northwestern China and high values in northern China, the mid and lower reaches of the Yangtze River valley, the coastal areas of southern China, and the Sichuan basin. The duration and seasonality of the extreme precipitation with 5-yr return periods are further analyzed. The average duration of extreme precipitation events exceeds 12 h in the coastal regions, Yangtze River valley, and eastern slope of the Tibetan Plateau. The duration in northern China is relatively short. The extreme precipitation events develop more rapidly in mountain regions with large elevation diferences than those in the plain areas. There are records of extreme precipitation in as early as April in southern China while extreme rainfall in northern China will not occur until late June. At most stations in China, the latest extreme precipitation happens in August–September. The extreme rainfall later than October can be found only at a small portion of stations in the coastal regions, the southern end of the Asian continent, and the southern part of southwestern China.     

夏季中国中东部不同历时降水时空分布特征

利用1961~2012年台站逐时降水资料,分析了夏季中国中东部不同历时降水的主要气候分布和长期变化特征,为深入认识其时空变化规律和形成机理奠定基础。分析结果表明,降水事件的平均历时由南向北呈"短—长—短"分布型,华南和北方地区以6 h以下的短历时降水为主;而中部地区(28°N~37°N)6 h以上长历时降水占总降水量60%以上。随着降水历时的增加,小雨事件(0.1~1.0 mm/h)的发生概率降低,中雨事件(1.1~10.0 mm/h)的发生概率升高;大雨、暴雨事件(10.0 mm/h)更易出现在35°N以南历时偏短的降水事件中。1961~2012年,中国中东部总降水量呈"南升北降"的趋势分布,夏季南方大部分地区降水强度、时数和事件数均呈上升趋势;而北方地区降水时数和事件数显著减少,不过降水强度呈增强趋势。中东部降水历时总体呈上升趋势,尤其以我国长江与黄河之间的中部地区变化最为显著。同时,该地区短历时(1~6 h)降水无显著的年代际转折,长历时(6 h)降水的年代际增加是20世纪70年代末至20世纪90年代初降水增多的主要原因。20世纪90年代初期以来,南方地区降水的年代际增多则是长、短降水共同作用的结果,但超过6 h降水的影响范围更广,且影响中心较短历时降水偏北。     

Changes in Regional Heavy Rainfall Events in China during 1961–2012

A new technique for identifying regional climate events, the Objective Identification Technique for Regional Extreme Events(OITREE), was applied to investigate the characteristics of regional heavy rainfall events in China during the period1961–2012. In total, 373 regional heavy rainfall events(RHREs) were identified during the past 52 years. The East Asian summer monsoon(EASM) had an important influence on the annual variations of China's RHRE activities, with a significant relationship between the intensity of the RHREs and the intensity of the Mei-yu. Although the increase in the frequency of those RHREs was not significant, China experienced more severe and extreme regional rainfall events in the 1990 s. The middle and lower reaches of the Yangtze River and the northern part of South China were the regions in the country most susceptible to extreme precipitation events. Some stations showed significant increasing trends in the southern part of the middle and lower reaches of the Yangtze River and the northern part of South China, while parts of North China, regions between Guangxi and Guangdong, and northern Sichuan showed decreasing trends in the accumulated intensity of RHREs.The spatial distribution of the linear trends of events' accumulated intensity displayed a similar so-called "southern flooding and northern drought" pattern over eastern China in recent decades.     

Temporal and Spatial Characteristics of Extreme Hourly Precipitation over Eastern China in the Warm Season

Based on hourly precipitation data in eastern China in the warm season during 1961-2000,spatial distributions of frequency for 20 mm h 1 and 50 mm h 1 precipitation were analyzed,and the criteria of short-duration rainfall events and severe rainfall events are discussed.Furthermore,the percentile method was used to define local hourly extreme precipitation;based on this,diurnal variations and trends in extreme precipitation were further studied.The results of this study show that,over Yunnan,South China,North China,and Northeast China,the most frequent extreme precipitation events occur most frequently in late afternoon and/or early evening.In the Guizhou Plateau and the Sichuan Basin,the maximum frequency of extreme precipitation events occurs in the late night and/or early morning.And in the western Sichuan Plateau,the maximum frequency occurs in the middle of the night.The frequency of extreme precipitation (based on hourly rainfall measurements) has increased in most parts of eastern China,especially in Northeast China and the middle and lower reaches of the Yangtze River,but precipitation has decreased significantly in North China in the past 50 years.In addition,stations in the Guizhou Plateau and the middle and lower reaches of the Yangtze River exhibit significant increasing trends in hourly precipitation extremes during the nighttime more than during the daytime.     

2016年汛期中国降水极端特征及与1998年对比

本文利用国家气象信息中心提供的1961—2016年全国2341个气象观测站日和小时降水量资料,分析了2016年中国汛期降水的极端特征,并与1998年进行比较。主要结论如下：2016年汛期全国平均降水量为1961年以来历史同期最多,共有140站汛期降水量突破1961年以来历史同期极大值,有112站出现历史次极大值,比1998年分别偏多54和47站。1998年降水极值主要出现在东北和长江中上游地区,2016年主要出现在华东地区,而且范围更加集中。共出现6972站次暴雨,其中大暴雨1251站次,为1961年以来最多。44次大范围暴雨过程持续时间达90 d,总体呈现“中间强、前后弱”的特征。有417站出现日降水量极端事件,其中88站突破历史纪录,创1961年以来新高;最大小时降水量共有113站突破历史极值,比1998年偏多29站。从空间分布来看,日降水量极端事件2016年主要位于华东和华北地区,1998年集中在中部地区;破纪录小时降水2016年主要在西部地区,而1998年东部地区更为突出。     

Spatial characteristics of extreme rainfall over China with hourly through 24-hour accumulation periods based on national-level hourly rain gauge data

Hourly rainfall measurements of 1919 national-level meteorological stations from 1981 through 2012 are used to document,for the first time,the climatology of extreme rainfall in hourly through 24-h accumulation periods in China. Rainfall amounts for 3-,6-,12- and 24-h periods at each station are constructed through running accumulation from hourly rainfall data that have been screened by proper quality control procedures. For each station and for each accumulation period,the historical maximum is found,and the corresponding 50-year return values are estimated using generalized extreme value theory. Based on the percentiles of the two types of extreme rainfall values among all the stations,standard thresholds separating Grade I,Grade II and Grade III extreme rainfall are established,which roughly correspond to the 70th and 90th percentiles for each of the accumulation periods. The spatial characteristics of the two types of extreme rainfall are then examined for different accumulation periods. The spatial distributions of extreme rainfall in hourly through 6-h periods are more similar than those of 12- and 24-h periods. Grade III rainfall is mostly found over South China,the western Sichuan Basin,along the southern and eastern coastlines,and in the large river basins and plains. There are similar numbers of stations with Grade III extreme hourly rainfall north and south of 30°N,but the percentage increases to about 70% south of 30°N as the accumulation period increases to 24 hours,reflecting richer moisture and more prolonged rain events in southern China. Potential applications of the extreme rainfall climatology and classification standards are suggested at the end.     

Regional trends in recent precipitation indices in China

Summary Regional characteristics of recent precipitation indices in China were analyzed from a daily rainfall dataset based on 494 stations during 1961 to 2000. Some indices such as precipitation percentiles, precipitation intensity, and precipitation persistence were used and their inter-decadal differences were shown in this study. Over the last 40 years, precipitation indices in China showed increasing and decreasing trends separated into three main regions. A decreasing trend of annual precipitation and summer precipitation was observed from the southern part of northeast China to the mid-low Yellow River valley and the upper Yangtze River valley. This region also showed a decreasing trend in precipitation intensity and a decreasing trend in the frequency of persistent wet days. On the other hand, increasing trends in precipitation intensity were found in the Xinjiang region (northwest China), the northern part of northeast China, and southeast China, mainly to the south of the mid-low Yangtze River. The indices of persistent wet days and strong rainfall have contributed to the increasing frequency of floods in southeast China and the Xinjiang region in the last two decades. Persistent dry days and weakening rainfall have resulted in the increasing frequency of drought along the Yellow River valley including North China. Regional precipitation characteristics and trends in precipitation indices indicate the climate state variations in the last four decades. A warm-wet climate state was found in northwest China and in the northern part of northeast China. A warm-dry climate state extends from the southern part of northeast China to the Yellow River valley, while a cool-wet summer was found in southeast China, particularly in the mid-low Yangtze River valley over the last two decades.     

云南小时降水的时空分布变化研究

利用2005—2018年125个国家级台站小时降水观测数据研究云南小时降水时空分布特征。结果表明:云南年总降水量、不同持续时间降水量、极端强降水量及降水日变化空间分布差异很大。年降水量自西北向南增加,雨强自北向南增强,降水时长西部大于东部、南部略大于北部,年降水量受降水时长和雨强共同影响,降水时长影响最强,雨强影响较弱,这种特征在滇西北最突出,但滇东北的降水量与雨强相关更好。云南大部夜雨量多于昼雨量,滇东北和北部边缘夜雨特征最显著;降水日变化特征在云南北部为夜间单峰,西部边缘为清晨单峰,中部为夜间与午后峰值相当的双峰,南部也为夜间和午后双峰,但南部不同区域间主峰和次峰出现时间不同。云南南部降水贡献以短、中历时降水为主,北部则以长、超长历时降水为主。云南短时强降水发生次数的空间分布表现为自西北向东南增加;年发生站次数具有增加趋势,日变化特征为显著单峰,多在傍晚至入夜出现,且极端短时强降水更易在凌晨出现。这些小时降水时空分布特征很大程度上代表了低纬高原地区的降水特征。由于低值天气系统多影响低纬高原中北部,热带天气系统多影响南部,且低纬高原地形复杂,局地热力条件差异明显,这些因素造成该区域小时降水时空分布特征差异显著。     

四川地区短时强降水事件时空演变特征研究

利用四川地区自动气象站逐小时降水观测资料,分析了2010~2019年5~9月短时强降水事件24h累计降水量、频次和强度的时空分布特征,探讨了短时强降水事件发生的频次、极值分布及其与地形、海拔高度等的关系。结果表明:四川地区平均24h累计降雨量基本在50mm以上,盆地东北部、西南部、南部及阿坝州东部甚至超过100mm,最大值出现在广安,达175mm。四川地区短时强降水事件开始时间的日变化特征表现为“V”型结构的夜间峰值位相,事件持续时段多为傍晚至凌晨,时长可达10h以上,最长甚至可持续22h。在强降水事件极值的日变化上,极大值频次和降水量呈单峰结构,在03时达到最大,其后逐渐减小至15时达到谷值,而后再次增大;降水强度呈弱双峰结构,分别在04时和16时达到谷值,13时和18时达到峰值,其日变化呈“增-减-增-减”的特征。四川短时强降水事件与复杂地形有密切的关系,5~6月事件活跃区在四川盆地中部,7月在盆地西部的龙门山脉一带,8月在雅安、乐山附近,9月在盆地北部且频次明显减少;短时强降水事件的最大小时雨强可达80mm以上,出现在7~8月的盆地西部龙门山一带和南部地区。短时强降水事件随着海拔高度的增加,发生频次和日数逐渐减少,海拔2000m以上地区基本无强降水发生日出现( 峨眉山气象站例外)。