GPCP和TRMMPR热带月平均降水的差异分析

文中利用GPCP(Global Precipitation Climatology Project)和TRMM(Tropical Rainfall Measuring Missio) PR(Precipitation Radar)资料,分析了1998～2002年热带地区月平均降水的差异及其主要原因.结果表明,GPCP和TRMM PR资料能一致地反映热带降水的主要分布特征,但降水的强度和范围存在着差异;两种资料的差异与雨强有密切关系;平均而言,洋面上的降水差异(0.5 mm/d)大于陆地上的差异(0.1 mm/d).微波发射信号(SSM/I E)的反演结果对洋面降水的高估和地面雨量计的缺乏,是造成两种资料间差异的主要原因.分析结果还表明,洋面上GPCP降水相对于PR降水的最大概率差异随雨强增大呈线性增大;陆地上这种差异则呈非线性关系.文中最后还利用最大概率函数对1979～1997年GPCP气候平均降水的误差进行了分析.

全球共享GPCP数据在长江中下游地区的适用性分析

应用GPCP(全球气候降水计划)数据与地面观测降水资料分析比较了1980年-2006年我国长江中下游地区27年年平均、季平均降水量的空间分布,并给出了1月、4月、7月、10月份两种数据的散点分布图及线性回归方程。结果表明:27年年平均、季平均两种数据具有较好的一致性,能够真实反映长江中下游流域降水的气候变化特征,其中夏季GPCP数据与台站资料差异较大,降水偏少的冬春两季,两者的一致性较高。4个月的相关性分析说明相关系数都在0.9以上,两者具有较好的相似性,7月份的相关系数为0.91低于其他3个月。在验证GPCP数据具有很好的适用性的基础上,分析研究了1980年-2006年长江中下游地区降水演变规律,线性趋势和差积曲线结果表明,20世纪80年代初到中期属丰水期,90年代属枯水期。M-K检验结果说明全流域年降水呈现下降趋势,长江流域内的各二级区年降水无显著变化;四季27年降水量的结果除汉江流域冬季呈显著上升,夏季呈显著下降,太湖流域冬季显著减少,长江中下游流域秋季显著下降外,其它季节无显著性变化。

GPCP与中国台站观测降水的气候特征比较

文中利用GPCP降水资料和中国大陆740个台站雨量计降水观测结果,分析研究了1980—2000年中国大陆范围内两者的异同。平均降水率分布结果表明:在中国台站雨量计分布密集地区(100°E以东)两者的多年年平均、季平均和月平均降水率分布具有很好的一致性,即均能表现自春季至冬季中国大陆降水分布自东南向西北伸展,然后向东南后退的气候变化特点。对长江中下游、华北、东北、西南的降水距平随时间的变化分析表明,两者的月距平随时间变化也具有很好的相似性,相关系数均超过0.8。差异分析还表明,由于中国降水季节性变化和台站雨量计密度不均匀,造成春秋冬三季节GPCP和台站雨量计之间的差异较小,而夏季差异较大。总体上GPCP有过高估计降水率的趋势。在GPCP逐月降水资料既能很好地反映出100°E以东中国大陆降水的气候分布,又能很好地表示出上述地区降水量异常变化的基础上,我们利用GPCP降水资料对青藏高原和中国大陆西北地区西部降水分布及变化进行了分析。结果表明20世纪80年代前期青藏高原西部降水偏多,而90年代后降水量减少;中国西北地区降水总体偏少,但没有明显的变化趋势。

Tropical Precipitation Estimated by GPCP and TRMM PR Observations

In this study, tropical monthly mean precipitation estimated by the latest Global Precipitation Climatology Project （GPCP） version 2 dataset and Tropical Rainfall Measurement Mission Precipitation Radar （TRMM PR） are compared in temporal and spatial scales in order to comprehend tropical rainfall climatologically. Reasons for the rainfall differences derived from both datasets are discussed. Results show that GPCP and TRMM PR datasets present similar distribution patterns over the Tropics but with some differences in amplitude and location. Generally, the average difference over the ocean of about 0.5 mm d^-1 is larger than that of about 0.1 mm d^-1 over land. Results also show that GPCP tends to underestimate the monthly precipitation over the land region with sparse rain gauges in contrast to regions with a higher density of rain gauge stations. A Probability Distribution Function （PDF） analysis indicates that the GPCP rain rate at its maximum PDF is generally consistent with the TRMM PR rain rate as the latter is less than 8 mm d^-1. When the TRMM PR rain rate is greater than 8 mm d^-1, the GPCP rain rate at its maximum PDF is less by at least 1 mm d^-1 compared to TRMM PR estimates. Results also show an absolute bias of less than 1 mm d^-1 between the two datasets when the rain rate is less than 10 mm d^-1. A large relative bias of the two datasets occurs at weak and heavy rain rates.

基于10年GPCP降水资料的全球极端降水分布分析

利用GPCP的1997-2006年全球1°×1°格点降水日资料,对全球年平均的降水,以及夏季极端降水高值区域频次和强度进行了分析,并比较了其与ENSO的ONI指数的关系。取得了以下结果:赤道附近是全年多雨区,极端降水高值区域多分布于此。赤道东太平洋夏季极端降水的频次和强度变化基本是一致的,西太平洋暖池也有相同的特点,但是两者最强最弱年份却刚好相反,且都发生在ENSO冷暖年,但无明显的正负对应关系。我国中东部夏季极端降水最强年(1998年)发生在极强EL Ni觡o年-1997年的次年,最弱年(2001年)也发生在持续24个月的极强的La Ni觡a事件之后,但两者没有明显的正负对应关系。孟加拉湾夏季极端降水频次和强度的年际振荡强弱有相反的特点。长江中下游(6,7月)极端降水频次和赤道东太平洋(7,8月)极端降水频次有一定的滞后相关性,置信度为95%。

中国区域逐日融合降水数据集与国际降水产品的对比评估

中国国家气象信息中心基于2400多个国家级台站观测日降水量和CMORPH卫星反演降水产品,采用概率密度匹配和最优插值相结合的两步数据融合方法,研制了中国区域1998年以来的0.25°×0.25°分辨率的逐日融合降水产品(CMPA_Daily)。通过该数据集与广泛应用于中国天气气候领域的两种国际上降水融合产品TRMM 3B42(Tropical Rainfall Measuring Mission,3B42)和GPCP(Global Precipitation Climatology Project,1 degree daily)的对比评估,考察CMPA_Daily产品的质量,评价其能否合理体现中国降水的天气气候特征。首先利用2008-2010年5-9月独立检验数据定量对比了CMPA_Daily、TRMM 3B42和GPCP三种降水产品的误差,结果表明,在误差的时间变化和空间分布上,CMPA_Daily均具有最高的相关系数和最小的平均偏差及均方根误差,TRMM 3B42其次,GPCP的误差相对较大。CMPA_Daily只低估了大暴雨,TRMM 3B42低估了大雨以上量级的降水,而GPCP低估了除小雨以外的所有降水。CMPA_Daily产品因融入了更多的站点观测信息,不论在中国东部沿海,还是中西部地形复杂区,其精度均优于TRMM 3B42和GPCP产品,即使在站点稀疏的青藏高原地区,CMPA_Daily降水量也更加接近站点观测,呈现明显的高相关。CMPA_Daily与独立检验数据的高相关在地形起伏时效果也较稳定,TRMM和GPCP的相关系数则随着地形变化幅度陡变而非常明显地降低。进一步通过对比分析各降水产品1998—2012年的气候平均降水特征表明,3种资料对中国区域气候平均降水量、降水强度、频率分布以及年际变化的总体描述基本一致,因有效融入了更多的中国站点观测信息,不论降水空间分布还是降水量,CMPA_Daily与地面观测均最为接近,在中国的中东部大部分地区对降水的估计精度明显更高,而在站点分布较稀疏的青藏高原地区,CMPA_Daily的降水分布型与TRMM、GPCP卫星融合资料类似,较地面站点插值产品更能体现出合理的降水分布。对中国强降水事件监测对比表明,CMPA_Daily产品可以更加准确地描述降水的强度变化,细致刻画降水空间分布,在把握降水小尺度特征上具有明显的优势,体现出高分辨率、高精度降水产品的特点。

四种再分析资料与长江中下游地区降水观测资料的对比研究

为了获得对中国区域降水刻画能力较好的再分析资料,以长江中下游地区为例,将PREC/L、CMAP、GPCP及NCEP2四种再分析降水资料与台站降水观测资料进行了多年和逐年平均的年、季、月降水量距平的时空相关分析和方差分析,比较了再分析资料对该区降水刻画能力的差异。首先对该区域的观测资料、CMAP、GPCP及NCEP2资料进行插值,使所有资料具有相同的空间分辨率,在此基础上比较再分析资料与观测资料年、季、月平均的降水变化特征。结果表明,四种再分析资料与观测资料的距平相关系数(均方根误差)由大到小(由小到大)的变化次序为:PREC/L→CMAP→GPCP→NCEP2,四种再分析资料均能较好地刻画出长江中下游地区降水的时空分布特征,尤以PREC/L资料对该区域降水变化的时空演变规律描述得最好。最后,利用PREC/L资料与观测资料对该区域降水的时空特征进行了相关分析、EOF分析和功率谱分析,结果显示夏季平均降水量具有2.3和3.1年的显著周期,EOF分析的前两个模态的时间变化分别具有2.3和2.8年的显著周期,这些显著周期都通过了95%的红噪声检验。

南海及其邻近地区几种常用降雨产品的相互比较

对南海及其邻近区域内3种比较常用的降雨产品进行了比较和分析。结果表明,在南海地区3种降雨量在空间上具有一致的大尺度分布特征,都能够很好地反映行星尺度雨带的季节性迁移特征;在细节上,三者之间存在明显差异,比较而言,GPCP 1DD(GlobalPrecipitationClimatologyProject,1DegreeDailyPrecipitationEstimate)和TRMM PR(TopicalRainfallMeasurmentMission,PrecipitationRadar)具有更为一致的空间分布特征,更能够反映降雨量在高大山地地形两侧的分布差异;3种资料都能够反映由地面台站资料反映出的局地区域降雨量季节和年际变化的基本特征,总体而言,GPCP 1DD和CMAP(CPCMergedAnalysisofPrecipitation)与地面观测结果更为接近,但是在反映极端降雨事件方面TRMM PR略优于其它2种资料。

GMS-5卫星估计中国西部地区月降水

在气候降水数据集中,中国西部地区的降水数据精度一直偏低,如全球降水气候项目数据集(GPCP)数据1998年年平均相对误差在100°E以西达到100%以上,由于这一地区人烟稀少,缺少足够的地面雨量站,极轨卫星时间覆盖率低,GMS静止卫星高度角偏低,给这一地区的降水测量和估计带来困难。本文尝试将GOES卫星降水指数(GPI)算法拓展应用到这一区域,同时,为了消除卫星高度角偏低造成的影响,利用当地气候资料,引入相对湿度修正因子。结果表明,用GMS静止卫星云图结合气候资料,可以有效估计中国西部地区的月降水。

青藏高原东南侧南风演变特征及其与中国东部春季降水的关系分析

使用1979—2008年NCEP/NCAR再分析数据集资料分析了全年各月经向风特征,发现青藏高原东南侧存在一个全年盛行南风的区域(22.5°～30°N、105°～110°E),即常年南风区。该区域南风呈现冬弱夏强的演变特征,尤其在春夏时期呈现双峰值状态,峰值分别出现在15候和37候左右。进一步分析表明,常年南风区南风与我国南方春季降水有着较好的对应关系。15候左右常年南风区南风第一次增强并达到峰值,持续的强南风使得我国南方地区降水随之有突然增加的趋势,进入春雨期。高原东南侧常年南风区南风两个峰值出现的原因并不相同。15候左右出现的绕流南风大值是由于高原的突然加热产生的低空气旋性环流叠加在绕流西风上,从而造成了南风的加强,湿润的偏南风给华南地区带来持续的降水,江南春雨开始。而37候左右出现的绕流南风大值是由于南海夏季风爆发后,孟加拉湾槽前强大的西南风加强了该处的绕流南风,使得南风势力变得更为强大,推进到我国长江中下游地区。

多种卫星降水产品在中国的精度评估

高时空分辨率的卫星降水产品对于研究区域降水时空分布特点起着非常重要的作用,但卫星降水遥感产品精度仍有待系统评估。以2008—2015年中国地面站点实测降水量数据为参考,采用四种统计指标分别对TRMM(Tropical Rainfall Measuring Mission)卫星降水产品、CMORPH(CPC MORPHing Technique)卫星降水产品以及GPCP(Global Precipitation Climatology Project)降水产品在四季和九大流域尺度上进行对比评估。结果表明在季节尺度上,春夏秋三季TRMM产品和CMORPH产品降水Threat Score (TS)评分较高,但冬季GPCP产品TS评分最高,在东南部地区TS评分达到0.3~0.4。CMORPH产品在春秋冬三季的相关系数最高,均能接近或超过0.4。TRMM产品在春夏秋三季的结果相比CMORPH稍逊,两种产品在冬季均出现降水总量异常高估的情况(Bias分别为57.3%和32.76%)。GPCP在春夏秋三季的一致性低于前两种产品,但冬季精度和一致性较高(相关系数为0.38,均方根误差为15.93mm/d)。从九大流域尺度上看,TRMM产品和CMORPH产品对中到大雨的反演在珠江、东南、长江流域较为准确,部分地区的TS评分甚至达到了0.5,但小雨监测可靠性较差,特别在东南部,流域TS评分不足0.2。GPCP产品虽然在小雨情况下的监测可靠性和前两种相近,但中到大雨事件监测可靠性差,在九大流域整体上TS评分不到0.2。从降水评估的精度和相关性来看,CMORPH产品的在三种产品中表现最佳,TRMM产品稍逊,而GPCP产品的精度和相关性明显落后于前两种产品。

Interannual Variability of Rainfall over the West Africa Sahel

Interannual variability of the precipitation over West Africa Sahel is analyzed based on 32 years (1979-2010) from monthly and daily database of the Global Precipitation Climatology Project (GPCP). In this region, we found that there is a link between the West Africa Monsoon (WAM) and the daily means of the precipitation in the summer, unseasonal rains can occur in the transition seasons and even in the heart of the dry season. Rainfall is the most important element for agro-pastoral activities in this region. The 850-hPa wind and wind divergence structure show a maximum convection over Mountain region (Fouta-Djalon and Mont-Cameroon) which corresponds to the high precipitation and OLR observed in these regions. The trend and empirical orthogonal function (EOF) of the precipitation are presented, including the mid-July variability of the precipitation. The dominant EOF of GPCP precipitation accounts for around 25.3% of the variance with slightly large amplitude in the north while relatively small in the equatorial band respectively. The second and third EOF which accounts for 20.5% and 14%, describes a longitudinal contrast with a zonal gradient.

Influence of Sea Level Pressure on Inter-Annual Rainfall Variability in Northern Senegal in the Context of Climate Change

This study examines the inter-annual variability of rainfall and Mean Sea Level Pressure (MSLP) over west Africa based on analysis of the Global Precipitation Climatology Project (GPCP) and National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis respectively. An interconnection is found in this region, between Mean Sea Level Pressure (MSLP) anomaly (over Azores and St. Helena High) and monthly mean precipitation during summer (June to September: JJAS). We also found that over northern Senegal (15°N - 17°N;17°W - 13°W) the SLP to the north is strong;the wind converges at 200 hPa corresponding to the position of the African Easterly Jet (AEJ) the rotational wind 700 hPa (corresponding to the position of the African Easterly Jet (AEJ) coming from the north-east is negative. In this region, the precipitation is related to the SLP to the north with the opposite sign. The Empirical Orthogonal Functions (EOF) of SLP is also presented, including the mean spectrum of precipitation and pressures to the north (15°N - 40°N and 50°W - 25°W) and south (40°S - 10°S and 40°W - 0°E). The dominant EOF of Sea Level Pressures north and south of the Atlantic Ocean for GPCP represents about 62.2% and 69.4% of the variance, respectively. The second and third EOFs of the pressure to the north account for 24.0% and 6.5% respectively. The second and third EOFs of the pressure to the south represent 12.5% and 8.9% respectively. Wet years in the north of Senegal were associated with anomalous low-pressure areas over the north Atlantic Ocean as opposed to the dry years which exhibited an anomalous high-pressure area in the same region. On the other hand, over the South Atlantic, an opposition is noted. The wavelet analysis method is applied to the SLP showings to the north, south and precipitation in our study area. The indices prove to be very consistent, especially during intervals of high variance.

Climatology Comparison Studies of Precipitations Between GPCP and Rain Gauges in China

The Global Precipitation Climatology Project （GPCP） monthly rainfall data and the rainfall records observed by 740 rain gauges in the mainland of China are used to analyze similarities and differences of the precipitation in China in the period from January 1980 to December 2000. Results expose significantly consistent rainfall distributions between the both data in multi-year mean, multi-year seasonal mean, and multi-year monthly mean. Departures of monthly rainfall for each dataset also show a high correlation with an over 0.8 correlation coefficient. Analysis indicates small differences of both datasets during autumn, winter, and spring, but relative large ones in summer. Generally, the GPCP has trend of overestimating the rainfall rate. Based on above good relationship of both datasets, the GPCP data, are used to represent distributions and variations of precipitation in the Tibetan Plateau and Northwest China. Results indicate positive departures of precipitation in summer in the west part of Tibetan Plateau in the 1980s and negative departures after the 1980s. For the west part of Northwest China, analysis illustrates precipitation decreases a little, but no clear variation tendency.

An examination of summer precipitation over Asia based on TRMM/TMI

A 6-year dataset of summer monthly mean precipitation derived from Tropical Precipitation Measure-ment Mission (TRMM)-Microwave Imager (TMI) was used to delineate the spatial distribution patterns of precipitation throughout Asian areas, which indicates that there are three rainfall centers located at the northern coast of the Bay of Bengal, the South China Sea and the western equatorial Pacific Warm Pool, respectively. Based upon the analysis of horizontal distribution, the capability of TMI for characterizing terrestrial and maritime precipitation has been evaluated and compared with Global Precipitation Climatology Project (GPCP) dataset. It was found that TMI and GPCP are well consistent with each other, while a few significant differences occur at several regions over land. By investigating rainfall esti-mates over six specific locations in Asia, a systematic underestimation of TMI was demonstrated, which could be explained by the inherent deficiency within TMI terrestrial algorithm relying on scat-tering signal from ice particles in a precipitation system. A further analysis shows that the highly in-homogeneous distribution of rain gauges employed by GPCP contributes a great deal to the significant discrepancy between GPCP and TMI, especially over regions surrounding the Tibetan Plateau where rain gauges are quite scarce.