中国的降水量符合正态分布吗?

利用1961—2017年长江流域700个气象站点逐月降水资料计算长江流域9个子流域面雨量,采用基于Box-Cox正态分布转换后的百分位法对长江流域不同时间长度的极端降水气候事件阈值进行界定。结果表明,在数据序列长度发生变化的情况下,面雨量序列经Box-Cox正态转换后,计算得到的极端降水气候事件阈值的变化相较于常规百分位法明显减小,具有更为稳健的特性,从而使得相应极端降水气候事件个例的挑选更为稳定。根据该方法得到的阈值,对2018年汛期(6—8月)长江各子流域极端降水气候事件进行判定,岷沱江流域发生了极端多雨气候事件,而长江干流重庆-宜昌段、汉江及中游干流区间发生了极端少雨气候事件。

     

Large-scale and spatio-temporal extreme rain events over India: a hydrometeorological study

Frequency, intensity, areal extent (AE) and duration of rain spells during summer monsoon exhibit large intra-seasonal and inter-annual variations. Important features of the monsoon period large-scale wet spells over India have been documented. A main monsoon wet spell (MMWS) occurs over the country from 18 June to 16 September, during which, 26.5 % of the area receives rainfall 26.3 mm/day. Detailed characteristics of the MMWS period large-scale extreme rain events (EREs) and spatio-temporal EREs (ST-EREs), each concerning rainfall intensity (RI), AE and rainwater (RW), for 1 to 25 days have been studied using 1° gridded daily rainfall (1951–2007). In EREs, ‘same area’ (grids) is continuously wet, whereas in ST-EREs, ‘any area’ on the mean under wet condition for specified durations is considered. For the different extremes, second-degree polynomial gave excellent fit to increase in values from distribution of annual maximum RI and RW series with increase in duration. Fluctuations of RI, AE, RW and date of occurrence (or start) of the EREs and the ST-EREs did not show any significant trend. However, fluctuations of 1° latitude–longitude grid annual and spatial maximum rainfall showed highly significant increasing trend for 1 to 5 days, and unprecedented rains on 26–27 July 2005 over Mumbai could be a realization of this trend. The Asia–India monsoon intensity significantly influences the MMWS RW.     

Precipitation climatology over India: validation with observations and reanalysis datasets and spatial trends

Changing rainfall patterns have significant effect on water resources, agriculture output in many countries, especially the country like India where the economy depends on rain-fed agriculture. Rainfall over India has large spatial as well as temporal variability. To understand the variability in rainfall, spatial–temporal analyses of rainfall have been studied by using 107 (1901–2007) years of daily gridded India Meteorological Department (IMD) rainfall datasets. Further, the validation of IMD precipitation data is carried out with different observational and different reanalysis datasets during the period from 1989 to 2007. The Global Precipitation Climatology Project data shows similar features as that of IMD with high degree of comparison, whereas Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation data show similar features but with large differences, especially over northwest, west coast and western Himalayas. Spatially, large deviation is observed in the interior peninsula during the monsoon season with National Aeronautics Space Administration-Modern Era Retrospective-analysis for Research and Applications (NASA-MERRA), pre-monsoon with Japanese 25 years Re Analysis (JRA-25), and post-monsoon with climate forecast system reanalysis (CFSR) reanalysis datasets. Among the reanalysis datasets, European Centre for Medium-Range Weather Forecasts Interim Re-Analysis (ERA-Interim) shows good comparison followed by CFSR, NASA-MERRA, and JRA-25. Further, for the first time, with high resolution and long-term IMD data, the spatial distribution of trends is estimated using robust regression analysis technique on the annual and seasonal rainfall data with respect to different regions of India. Significant positive and negative trends are noticed in the whole time series of data during the monsoon season. The northeast and west coast of the Indian region shows significant positive trends and negative trends over western Himalayas and north central Indian region.     

江西持续性冻雨的行星尺度特征分析

应用常规气象地面观测资料和NECP 2.5°×2.5°的再分析资料,对1959—2008年期间发生在江西的持续5 d及以上的冻雨天气过程进行行星尺度的特征分析,结果表明:江西持续性冻雨天气期间,500 hPa高纬度地区的阻塞高压、中纬度的低槽以及低纬度的分裂小槽和地面沿中国中东部侵入的强冷空气有利于江西持续性冻雨天气的维持;同时,700 hPa有来自低纬地区的稳定的"湿舌"和西南急流存在,不仅有利于中层逆温结构的形成,也是水汽输送的主要机制。距平相关分析得到,持续性强冻雨天气发生期间,海平面气压、1 000~500 hPa和1 000~925 hPa厚度场的距平相关系数基本都在0.7以上,多数情况下达到0.8以上,表明江西持续性强冻雨天气期间高层和底层的天气系统是稳定少变的。     

区域暴雨过程中两次龙卷风事件分析

利用新一代多普勒雷达资料、常规观测和NCEP等资料对2010年7月17和19日河南两次龙卷过程进行了详细分析.结果如下:(1)龙卷发生的天气背景是:两次龙卷均发生在副热带高压边缘西南气流影响河南出现区域暴雨和大暴雨过程中,高层为青藏高压脊北侧和高空急流入口区右后侧强辐散区,中低层有低涡、切变线、急流,龙卷发生在地面β中低压气旋的东南象限,距气旋中心约50 km处.(2)龙卷发生的环境场特征:对流有效位能大于1000J/kg,大气层结不稳定,K指数大于36℃,发生强龙卷的SWEAT指数在400左右,0-1.5 km垂直风矢量切变达15m/s,而抬升凝结高度很低(0-300 m).(3)雷达回波和特征参数分析结果为:两次龙卷均发生在低涡东南侧的β中尺度螺旋雨带上,该回波带强度50 dBz左右,顶高9-12 km,龙卷是由该回波带中部的微型超级单体产生,垂直剖面上低层有明显的弱回波上升气流区,螺旋雨带中部向东凸起的强降水下沉气流和上升入流交界处是龙卷易出现的关键区域.速度图上,γ中尺度气旋系列先后经历了三维相关切变、中气旋、龙卷涡旋特征的演变过程.中气旋提前于龙卷发生前0.5-1 h出现,这对估计和预警龙卷很有意义.中气旋和龙卷涡旋特征参数分析结果是:中气旋和龙卷涡旋特征(TVS)底的高度都在1 km以下,TVS底和中气旋底高度相当或略低一些,F2级龙卷底高＜0.5 km,TVS顶的高度一般在2-4 km,中气旋顶高一般2-3 km;从最大切变值来看,中气旋最大切变一般在(1.0-4.0)×10-2 s-1,TVS最大切变值一般为(2.0-5.0)×10-2 s-1,最大切变高度平均出现在0.8-0.9 km,F2级龙卷最大切变高度一般在0.5 km.就F1和F2两次龙卷过程比较看,F2龙卷特征底和顶的高度都低于F1龙卷,最大切变值F2龙卷比F1龙卷大一倍,出现在低层大的切变更容易造成严重的龙卷灾害.根据局限于低层中气旋和TVS系列性、移动性、持续性的特点明确了区域暴雨中预警龙卷的思路.最后对区域暴雨过程中出现龙卷的原因进行了探讨.     

Intense rainfall events over the west coast of India

Summary The west coast of the Indian peninsula receives very heavy rainfall during the summer Monsoon (June–September) season with average rainfall over some parts exceeding 250 cm. Heavy rainfall events with rainfall more than 15 cm day⁻¹ at one or more stations along the west coast of India occur frequently and cause considerable damage. A special observational programme, Arabian Sea Monsoon Experiment, was carried out during the monsoon season of 2002 to study these events. The spatial and temporal distributions of intense rainfall events, presented here, were used for the planning of this observational campaign. The present study using daily rainfall data for summer monsoon season of 37 years (1951–1987) shows that the probability of getting intense rainfall is the maximum between 14° N–16° N and near 19° N. The probability of occurrence of these intense rainfall events is high from mid June to mid August, with a dip in early July. It has been believed for a long time that offshore troughs and vortices are responsible for these intense rainfall events. However, analysis of the characteristics of cloud systems associated with the intense rainfall events during 1985–1988 using very high resolution brightness temperature data from INSAT-IB satellite shows that the cloud systems during these events are characterized by large spatial scales and high cloud tops. Further study using daily satellite derived outgoing longwave radiation (OLR) data over a longer period (1975–1998) shows that, most of these events (about 62%) are associated with systems organized on synoptic and larger scales. We find that most of the offshore convective systems responsible for intense rainfall along the west coast of India are linked to the atmospheric conditions over equatorial Indian Ocean.     

东亚区域极端气候事件变化的数值模拟试验

使用ResCM2区域气候模式,嵌套澳大利亚CSIRO R21L9全球海气耦合模式,进行了温室效应（二氧化碳加倍）对东亚（主要是中国区域）极端气候事件影响的数值试验。控制试验的结果表明,区域模式能够较好地模拟中国区域的极端气候事件。对温室效应引起的它们的变化进行了信度检验,分析结果表明,温室效应将引起日最高和最低气温增加,日较差减小;使得高温天气增多,低温日数减少。降水日数和大雨日数在一些地区将增加。同时还会引起影响中国的台风活动的变化。     

近54a湖南区域暴雨的时空分布特征

基于1960—2013年湖南88个台站逐日降水数据,采取线性趋势分析等方法分析了近54 a湖南区域暴雨的时空分布特征。从时间变化上看,近54 a湖南区域暴雨日以6月208 d为最多,1月0 d为最少;夏季、春季、秋季及冬季区域暴雨日数占总日数的百分比依次为60%、29%、10%及1%。暴雨日数、暴雨强度均值突变点分别为1994年、1995年,暴雨初日的均值突变点为1983、1994年,暴雨终日无均值突变;暴雨日数与暴雨强度(暴雨发生终日)总体上呈上升(后延)趋势。基于突变点分段线性趋势分析表明,仅暴雨日数在1994—2013年及暴雨强度在1960—1994年期间呈显著下降趋势。从空间分布上看,区域暴雨强度及其与非区域暴雨强度的差值、区域暴雨持续2日或以上的暴雨强度及其与单日暴雨强度的差值的大值区主要位于湘西北及湘东南,小值区主要位于湘西南-湘东北的带状区域;全部站点的区域暴雨强度均大于非区域暴雨强度,89%的台站持续2日或以上的区域暴雨强度大于单日区域暴雨强度。区域暴雨、总体暴雨的台站暴雨最长持续日数分别为1~4 d、2~4 d,均集中在2~3 d且其站数占总站数的百分比分别为97.7%、96.6%。     

Spatial asymmetry of temperature trends over India and possible role of aerosols

Dissimilarities in temperature trends in space and time over the Indian region have been examined to look for signatures of aerosols’ influence. Separate temperature time series for North and South India were constructed for dry (November–May) and wet (June–October) seasons. Temperature trend for the entire period 1901–2007 and different subperiods of 1901–1950, 1951–1990, 1971–2007, and 1991–2007 have been examined to isolate the aerosol and other greenhouse gas influences on temperatures. Maximum (daytime) temperatures during dry season corresponding to North and South India show significant warming trend of 0.8 and 1.0?°C per hundred years during the period 1901–2007, while minimum temperature shows nebulous trend of 0.2 and 0.3?°C per hundred years over North and South India, respectively. During the wet season, maximum temperature shows nearly half of dry season maximum temperature warming trend. However, asymmetry is observed in dry season maximum temperature trend during post-industrial period 1951–1990 wherein the North/South India shows decreasing/increasing trends, while during the recent period 1991–2007 trends are uniformly positive for both the regions. Spatial and temporal asymmetry in observed trends clearly point to the role of aerosols in lowering temperature trends over northern India. Atmospheric aerosols could cause a negative climate forcing that can modulate the regional surface temperature trends in a significant way. As this forcing acts differentially on day and night temperatures, trends in diurnal temperature range (DTR) provide a direct assessment of impacts of aerosols on temperature trends. Time series of diurnal temperature range for dry and wet seasons have been examined separately for North and South India. Over North India, the DTR for dry season has increased gradually during the period 1901–1970 and thereafter showed decreasing trend, while trends in temperature range over Southern India were almost opposite in phase with North India. The aerosol and greenhouse gases seem to play an important role in the spatial and temporal variability of temperature range over India.     

Aerosol, clouds and rainfall: inter-annual and regional variations over India

Inter-annual and regional variations in aerosol and cloud characteristics, water vapor and rainfall over six homogeneous rainfall zones in India during the core monsoon month of July from 2000 to 2010, and their correlations are analyzed. Aerosol optical depth (AOD) and aerosol absorbing index (AAI) in July 2002, a drought year are higher over India when compared to normal monsoon years. The drier conditions that existed due to deficient rainfall in July 2002 could be responsible for raising more dust and smoke resulting in higher AODs over India. In addition, over India precipitation is not uniform and large-scale interruptions occur during the monsoon season. During these interruptions aerosols can build up over a region and contribute to an increase in AODs. This finding is supported by the occurrence of higher anomalies in AOD, AAI and rainfall over India in July 2002. Aerosol characteristics and rainfall exhibit large regional variations. Cloud effective radius (CER), cloud optical thickness and columnar water vapor over India are the lowest in July 2002. CER decreases as AOD and AAI increase, providing an observational evidence for the indirect effect of aerosols. Eighty percent of CER in northwest India, and 30% of CER over All India in July 2002 are <14 μm, the precipitation threshold critical cloud effective radius. Northeast India shows contrasting features of correlation among aerosols, clouds and rainfall when compared to other regions. These results will be important while examining the inter-annual variation in aerosols, cloud characteristics, rainfall and their trends.     

1998年7月2日恩施全州性暴雨分析

利用GMS红外云图、雷达回波资料及逐时自记雨量资料对1998年7月2日恩施全州性暴雨从云团演变、回波及雨团活动诸方面进行了分析,揭示了山区暴雨的某些中尺度特征,讨论了恩施特定的地形条件对暴雨天气的发生及时空分布的重要作用.     

Spatial distribution of temperature trends and extremes over Maharashtra and Karnataka States of India

Earth surface temperatures are changing worldwide together with the changes in the extreme temperatures. The present study investigates trends and variations of monthly maximum and minimum temperatures and their effects on seasonal fluctuations at different climatological stations of Maharashtra and Karnataka states of India. Trend analysis was performed on annual and seasonal mean maximum temperature (TMAX) and mean minimum temperature (TMIN) for the period 1969 to 2006. During the last 38 years, an increase in annual TMAX and TMIN has occurred. At most of the locations, the increase in TMAX was faster than the TMIN, resulting in an increase in diurnal temperature range. At the same time, annual mean temperature (TM) showed a significant increase over the study area. Percentiles were used to identify extreme temperature indices. An increase in occurrence of warm extremes was observed at southern locations, and cold extremes increased over the central and northeastern part of the study area. Occurrences of cold wave conditions have decreased rapidly compared to heat wave conditions.     

结构分析法在突发区域性暴雨预报中的应用

用结构分析方法分析了2006年6月3日西安突发性暴雨探空信息的大气垂直结构特征。结果表明:暴雨发生前和暴雨过程中垂直探空信息具有独特的结构特征,说明突发性的核心问题在于结构特征的转折性;大气的超低温、滚流方向等均是大气结构转折变化的先兆信息。揭示了突发灾害天气可预测性,为区域性暴雨的预测提供了一种新方法。     

Trends in the annual extreme rainfall events of 1 to 3 days duration over India

Summary In this paper, the annual extreme rainfall series in the time scale of 1 to 3 days duration at 316 stations, well distributed over the Indian region, covering 80-years of rainfall data from 1901 to 1980 were analysed for trend and persistence using standard statistical tests. It has been found that the annual extreme rainfall records of most stations are free from trend and persistence. However, the extreme rainfall series at stations over the west coast north of 12°N and at some stations to the east of the Western Ghats over the central parts of the Peninsula showed a significant increasing trend at 95% level of confidence. Stations over the southern Peninsula and over the lower Ganga valley have been found to exhibit a decreasing trend at the same level of significance. The data series of the stations which showed trends were subjected to a 10-year moving average and the resulting smoothed series have been discussed. It may be said that this increasing or decreasing trend in the annual extreme rainfall events at a few places will have tremendous implications in the hydrologic studies and dam design projects.With 9 Figures     

N2O emissions of India: an assessment of temporal, regional and sector trends

This paper estimates the national level trend of India’s N₂O emissions from 1985–2005 and detailed sub-regional (594 districts) level and sector emissions for the year 2005. N₂O emissions are estimated using the latest methodologies (IPCC 2006), disaggregated activity data and indigenized emission factors. The estimates show that India’s N₂O emissions have grown from 144?Gg in 1985 to 267?Gg in 2005 exhibiting a compounded annual growth rate of 3.1%, which has been gradually declining from 4.7% over 1985–1990 to 2.4% over 2000–2005. N-fertilizer application contributed most to N₂O emissions, a 49% share in 2005 compared to 40% in 1985. Sub-regional (district-level) distribution of N₂O emissions showed rising mean and spread over the years, with average emissions per districts increasing from 305?ton N₂O per year in 1990 to 450?tons in 2005. The main reason being increased use of N-fertilizer. However crop selection plays an important role in N₂O emissions and there are crops providing high economic returns but low N-fertilizer requirements. Agriculture sector could contribute considerably to GDP even with very low N₂O emissions. Indian agriculture practices vary widely in input applications and crop yields across states. The gradual transition from traditional to modern agriculture over past two decades has enhanced the intensity of inputs like N-fertilizer. A simple correlation based on 1985–2005 trends shows that, ceteris paribus, a 10% increase in total crop production is accompanied with a 12.4% increase in N-fertilizer application and a 9.7% increase in total N₂O emissions from India.     

Lightning activity with rainfall during El Nino and La Nina events over India

Theoretical and Applied Climatology - This paper appraises the association of lightning flash count (FC) with rainfall using the satellite-borne Lightning Imaging Sensor’s (LIS) data along...     

TRMM-observed summer warm rain over the tropical and subtropical Pacific Ocean: Characteristics and regional differences

Based on the merged measurements from the TRMM Precipitation Radar and Visible and Infrared Scanner, refined characteristics (intensity, frequency, vertical structure, and diurnal variation) and regional differences of the warm rain over the tropical and subtropical Pacific Ocean (40ffiS-40ffiN, 120ffiE-70ffiW) in boreal summer are investigated for the period 1998-2012. The results reveal that three warm rain types (phased, pure, and mixed) exist over these regions. The phased warm rain, which occurs during the developing or declining stage of precipitation weather systems, is located over the central to western Intertropical Convergence Zone, South Pacific Convergence Zone, and Northwest Pacific. Its occurrence frequency peaks at midnight and minimizes during daytime with a 5.5-km maximum echo top. The frequency of this warm rain type is about 2.2%, and it contributes to 40% of the regional total rainfall. The pure warm rain is characterized by typical stable precipitation with an echo top lower than 4 km, and mostly occurs in Southeast Pacific. Although its frequency is less than 1.3%, this type of warm rain accounts for 95% of the regional total rainfall. Its occurrence peaks before dawn and it usually disappears in the afternoon. For the mixed warm rain, some may develop into deep convective precipitation, while most are similar to those of the pure type. The mixed warm rain is mainly located over the ocean east of Hawaii. Its frequency is 1.2%, but this type of warm rain could contribute to 80% of the regional total rainfall. The results also uncover that the mixed and pure types occur over the regions where SST ranges from 295 to 299 K, accompanied by relatively strong downdrafts at 500 hPa. Both the mixed and pure warm rains happen in a more unstable atmosphere, compared with the phased warm rain.