中国区域地理、地形因子对降水分布影响的估算和分析

不同与以往基于最小二乘的多元线性回归方法，本文首次尝试将新型的第二代回归分析方法——偏最小二乘回归分析方法应用到中国区域的降水建模中．利用区域内394个气象观测站建站到2000年45年(及以上)的降水资料，建立了一个简单的年、季降水量和地理、地形因子(包括纬度、经度、地形高程、坡度、坡向和遮蔽度)的关系模型，估算了区域降水量中地理、地形的影响部分，并分析了这种影响的特征．结果表明，用此方法建立的模型能够解释70%以上的因变量的变异，相关系数基本都在0.84以上，经交叉有效性检验，模型的回归效果较显著．分析表明，在多元线性回归不适用的情况下，本文基于偏最小二乘法的简单模型能够比较准确地定性、定量地再现实际降水分布．     

起伏地形下天文辐射分布式估算模型

基于数字高程模型（DEM）,建立了起伏地形下天文辐射分布式估算模型.模型全面考虑了地形因子对天文辐射的影响,只需DEM数据作为输入项,适用于遥感图像处理、地理信息系统等数据处理平台.以1km×1km分辨率的DEM数据作为地形的综合反映,计算了我国全年各月天文辐射的空间分布.结果表明：我国年天文辐射总量有明显的纬向分布特点,随着纬度的降低,年天文辐射总量由北向南增加;由于受坡向、坡度和地形遮蔽因子影响,山区天文辐射表现出非地带性分布特征.本文所提供的我国天文辐射数据产品,可作为基础地理数据供相关研究应用.     

近50a淮河流域汛期降水日数和强度的分布与变化特征

选用1961-2010年淮河流域145个地面气象站的观测资料,分析淮河流域汛期(5-9月)降水的时空变化规律.结果表明:淮河流域汛期降水的空间分布不仅受到地理位置和地形的影响,而且与湿度和风速的空间分布具有较好的相关性;在时间变化上,雨日出现频率有下降的趋势,但暴雨日比重和暴雨日平均降水量均有升高的趋势.淮河流域汛期暴雨日出现频率以及各类型雨日的平均降水量均有上升的趋势,强降水时空变化呈现局地性和频发性.     

自然降水变异对人工增雨效果评估的影响

效果评估是人工增雨试验中的关键问题之一.降水在时空分布上往往存在自然变异,使得精确估算自然降水量、评估人工增雨的效果变得比较困难.基于吉林省1997~2007年4~7月飞机人工增雨作业的宏观记录资料和降水量日值数据,运用现代统计模拟方法"bootstrap"分析自然降水变异,并设法控制其对人工增雨效果评估的影响.研究表明,自然降水变异的影响有三种控制方法:增加催化样本量、删除异常点和选取降水结构相似的对比单元.催化样本量越大,自然降水变异的影响和催化效果的检出下限越小.催化样本量为470时,若要检出20%~30%的增雨效果,置信度可达90%.在单次作业的效果检验中,删除强异常点和选取降水结构相似的对比单元,建立数据删失模型,能够有效地控制自然降水变异的影响,提高人工增雨效果评估的效率.结果显示,吉林省人工增雨相对效果的分布主要集中在0~30%,平均11.95%.人工增雨作业的效果,和降水量大小没有直接联系,而其波动幅度随着降水量增加而逐渐越小.     

长江上游地区可利用降水量的气候特征

利用长江上游地区107个观测站1960-2008年气温、降水观测资料,采用陆面蒸发经验模型计算得到各观测站的月蒸发量,再根据水量平衡关系,得到可利用降水量,采用数理统计、REOF分析和M-K突变检验等方法,分析长江上游地区可利用降水量的气候变化特征.结果表明:长江上游可利用降水量季节变化显著,5-9月长江上游可利用降水...     

基于特征分区的建筑物数据空间化研究

针对现有基于多因素的建筑空间分布格网化模型未考虑建筑物空间分布异质性的问题,提出基于特征分区的建筑物数据空间化模型。以四川省雅安市为例,利用影响建筑物空间分布的因子进行建筑物特征一致性分区,选取土地利用、高程、坡度、坡向、河流距离、道路距离、地形起伏度7类影响因子,基于分区结果分别研究建筑物空间分布与各影响因子之间的关系,分区构建基于多因素的建筑物数据空间化模型,生成雅安市300 m格网建筑物空间分布数据。研究结果表明,分区构建的建筑物空间分布格网化模型有效提高了建筑物空间分布数据的精确度与准确性。     

SRES情景下多模式集合对淮河流域未来气候变化的预估

采用偏差修正/空间降尺度方法处理后的IPCC AR4中8个全球海气耦合模式的集合平均结果,分析了SRESA2、A1B和B1情景下淮河流域未来30 a(2011 2040年)相对于现状(1961 1990年)地面温度和降水的可能变化.结果表明:(1)多模式集合能较好地反映流域现状年、季温度和降水的大尺度空间分布特征;对温度和降水的年内分配过程模拟较好,各月温度集合平均与观测值相差0.2℃左右(冬季各月除外),而降水集合平均与观测值相对误差在5%左右(9月除外).(2)不同情景下未来流域年、季温度一致增加,年温度增加幅度在0.85～1.12℃之间;冬、春季温度增加相对明显,而夏、秋季温度增加并不显著;年际和年代际温度增加趋势显著.(3)不同情景下未来流域年降水有增加趋势,增加幅度为0.13%～5.24%,增幅不明显;降水季节变化有增有减,季节、年际和年代际降水变化较为复杂,不同情景下降水空间变化差异显著.     

复杂地形下黄河流域月平均气温分布式模拟

通过对常规气象站月平均气温资料的分析,发现在影响气温的众多因素中,海拔高度、太阳总辐射、地表长波有效辐射对气温具有显著影响．建立了月平均气温的物理经验统计模型,结合复杂地形下太阳总辐射分布式模拟结果,提出了依托常规地面气象观测资料实现复杂地形下月平均气温分布式模拟的方法,生成了黄河流域1km×lkm分辨率月平均气温、月平均最高气温、月平均最低气温的空间分布图．分析表明,模拟结果能较好地反映气温的宏观分布趋势和局地分布特征．交叉验证结果表明,模型具有很好的稳定性,各月平均气温、月平均最高气温、月平均最低气温的模拟误差平均为0．19～0．35℃;加密站验证和个例年验证表明,模型具有良好的空间维和时间维模拟能力．提出的月平均气温分布式模型立足于常规地面气象观测资料,不依赖于山地野外考察资料,可以方便地在广大地区推广应用．     

复杂地形下黄河流域月平均气温分布式模拟

通过对常规气象站月平均气温资料的分析,发现在影响气温的众多因素中,海拔高度、太阳总辐射、地表长波有效辐射对气温具有显著影响.建立了月平均气温的物理经验统计模型,结合复杂地形下太阳总辐射分布式模拟结果,提出了依托常规地面气象观测资料实现复杂地形下月平均气温分布式模拟的方法,生成了黄河流域1km×1km分辨率月平均气温、月平均最高气温、月平均最低气温的空间分布图.分析表明,模拟结果能较好地反映气温的宏观分布趋势和局地分布特征.交叉验证结果表明,模型具有很好的稳定性,各月平均气温、月平均最高气温、月平均最低气温的模拟误差平均为0.19~0.35℃;加密站验证和个例年验证表明,模型具有良好的空间维和时间维模拟能力.提出的月平均气温分布式模型立足于常规地面气象观测资料,不依赖于山地野外考察资料,可以方便地在广大地区推广应用.     

新疆北部降水的气候分布特征及其对ENSO的响应

分析研究了新疆北部地区近50年(1951～2000年)全年各月降水的气候分布特征和各季降水的年际变化规律,重点揭示了北疆多雨季节(4～7月)及其各月降水量对赤道东太平洋的海温SST和南方涛动指数SOI的显著响应关系,并用前期SST和SOI作为预报因子,建立了北疆地区雨季降水量的预报方程.该方程可用于北疆地区雨季降水量的长期预报.     

卫星遥感藏北积雪分布及影响因子分析

利用1993~2004年SSM/I被动微波辐射仪反演的雪深资料,1996~2004年NOAA/AVHRR可见光和红外反演的积雪覆盖面积资料,1966~2003年藏北地区6个地面台站的积雪观测资料来检验卫星资料的可用性,并研究近年来藏北积雪的时空分布和影响因素.结果表明,SSM/I, NOAA/AVHRR和实际观测的积雪资料具一致性.从积雪时间变化看：季节尺度上,藏北地区秋冬季积雪迅速增加,但春季(3~5月)融雪速度不快,呈现正反馈特征;年际尺度上,藏北地区20世纪60年代末期起积雪开始减少,80年代积雪增加,90年代起到2003年积雪总体上减少,呈现出减少—增加—减少趋势.采用小波分析发现积雪振荡周期存在着一个准2~3年,准9年和13年的周期,从20世纪70年代初到90年代中期还有一个5年的周期.积雪空间上看,藏北地区积雪主要集中在东部地区,该区每个冬春年积雪覆盖旬数超过15旬,显著高于西部少雪区,大部分积雪集中在4900~5600 m的高度左右;藏北高原积雪变动的显著区位于藏北中东部的安多和聂荣地区.利用藏北地区1966~2003年的地面温度和降水资料建立回归方程模拟年累积雪日,结果表明模拟值与实测值之间的相关系数达0.74.积雪时空分布受温度、降水等因子影响明显.1998~2003年藏北积雪的减少与全球变暖有关,但降水的减少可能是导致近年来藏北积雪减少的更主要因素.     

Artificial neural network models for forecasting monthly precipitation in Jordan

Forecasting precipitation in arid and semi-arid regions, in Jordan in the Middle East for example, has particular importance since precipitation is the unique source of water in such regions. In this study, 1-month ahead precipitation forecasts are made using artificial neural network (ANN) models. Feed forward back propagation (FFBP), radial basis function (RBF) and generalized regression type ANNs are used and compared with a simple multiple linear regression (MLR) model. The models are tested on monthly total precipitation recorded at three meteorological stations (Baqura, Amman and Safawi) from different climatological regions in Jordan. For the three stations, it is found that the best calibrated model is FFBP with respect to all performance criteria used in the study, including determination coefficient, mean square error, mean absolute error, the slope and the intercept in the best-fit linear line of the scatter diagram. In the validation stage, FFBP is again the best model in Baqura and Amman. However, in Safawi, the driest station, not only FFBP but also RBF and MLR perform equally well depending on the performance criterion under consideration.     

Application of adaptive fuzzy rule-based models for reconstruction of missing precipitation events

Abstract

This paper describes a fuzzy rule-based approach applied for reconstruction of missing precipitation events. The working rules are formulated from a set of past observations using an adaptive algorithm. A case study is carried out using the data from three precipitation stations in northern Italy. The study evaluates the performance of this approach compared with an artificial neural network and a traditional statistical approach. The results indicate that, within the parameter sub-space where its rules are trained, the fuzzy rule-based model provided solutions with low mean square error between observations and predictions. The problems that have yet to be addressed are overfitting and applicability outside the range of training data.     

Estimation and spatial interpolation of rainfall intensity distribution from the effective rate of precipitation

Great emphasis is being placed on the use of rainfall intensity data at short time intervals to accurately model the dynamics of modern cropping systems, runoff, erosion and pollutant transport. However, rainfall data are often readily available at more aggregated level of time scale and measurements of rainfall intensity at higher resolution are available only at limited stations. A distribution approach is a good compromise between fine-scale (e.g. sub-daily) models and coarse-scale (e.g. daily) rainfall data, because the use of rainfall intensity distribution could substantially improve hydrological models. In the distribution approach, the cumulative distribution function of rainfall intensity is employed to represent the effect of the within-day temporal variability of rainfall and a disaggregation model (i.e. a model disaggregates time series into sets of higher solution) is used to estimate distribution parameters from the daily average effective precipitation. Scaling problems in hydrologic applications often occur at both space and time dimensions and temporal scaling effects on hydrologic responses may exhibit great spatial variability. Transferring disaggregation model parameter values from one station to an arbitrary position is prone to error, thus a satisfactory alternative is to employ spatial interpolation between stations. This study investigates the spatial interpolation of the probability-based disaggregation model. Rainfall intensity observations are represented as a two-parameter lognormal distribution and methods are developed to estimate distribution parameters from either high-resolution rainfall data or coarse-scale precipitation information such as effective intensity rates. Model parameters are spatially interpolated by kriging to obtain the rainfall intensity distribution when only daily totals are available. The method was applied to 56 pluviometer stations in Western Australia. Two goodness-of-fit statistics were used to evaluate the skill—daily and quantile coefficient of efficiency between simulations and observations. Simulations based on cross-validation show that kriging performed better than other two spatial interpolation approaches (B-splines and thin-plate splines).     

全球降水计划多卫星降水联合反演IMERG卫星降水产品在中国大陆地区的多尺度精度评估

以中国气象局逐小时地面降水数据集为参考基准,采用8种统计评价指标综合评估对比了美国NASA研发的全球降水计划（GPM）多卫星降水联合反演IMERG（Integrated Multi-satellitE Retrievals for GPM）卫星降水产品的三个不同版本的Final数据,分析了三套卫星降水在中国大陆地区多时空尺度下的反演精度,探讨了IMERG最新版本V5数据的改进情况及反演中仍然存在的问题.结果表明：IMERG数据能够准确地捕捉到中国大陆地区的降水区域特征,但是在中国西北部地面站点稀疏地区误差较大,精度较低,难以精确估测该地区的实际降水值.最新版本V5数据精度整体上优于先前的V3和V4数据,V5与地面观测数据的相关系数为0.75,均方根误差为7.03 mm/d,较V3、V4有明显提高,改善了V3、V4在中国西北部出现的降水低估问题;但是V5在冬季表现较差且没有解决前期版本存在的高估问题,整体上相对实际降水仍处于高估状态;同时V5在对高雨强事件的捕捉监测能力方面还存在一定的不足,因此建议在强降雨事件监测中需谨慎使用卫星降水IMERG数据集.目前V5系统中的校正算法还存在部分缺陷：为消除全球降水系统性低估问题,目前的校正算法整体性抬升了卫星降水值,从而导致卫星降水反演在中国地区高雨强事件下出现高误报以及高估问题,进而影响到IMERG数据回推以及后续再生数据的精度.     

An integrated statistical approach to identify the nonlinear trend of runoff in the Hotan River and its relation with climatic factors

A number of studies have indicated a transition from warm-dry to warm-wet climate in Northwest China after the 1980s. This transition was characterized by an increase in temperature and precipitation, added river runoff volume, increased lake water surface elevation and area, and elevated groundwater table. However, some literatures showed that the Hotan River has presented a contrary situation, i.e. the runoff decreased, whereas temperature and precipitation increased. In order to discover the nonlinear runoff trend and its causes in the Hotan River, based on the related data from hydrological stations, ground and air sounding meteorological stations, this study applied a comprehensive method combing correlation analysis, wavelet analysis and regression analysis to investigate the runoff change in the Hotan River with its relevant climatic factors over the past decades. The main findings are: (a) the hydrological process of the Hotan River is a nonlinear system, with a periodicity of 24 year cycle, and it shows different nonlinear trends at different time scales; (b) the data from the ground meteorological stations in the Hotan area shows a false appearance that there is almost no correlation between runoff and temperature, and a little negative correlation between runoff and precipitation; (c) but the data from air sounding meteorological stations shows the truth that there is a close relation between the runoff in the Hotan River and the 0°C level height in summer on the north slope of Kunlun Mountains. The two variables present a same periodicity, i.e. 24-year cycle, having similar nonlinear trends and significant correlations at different time scales.     

Finding the most appropriate precipitation probability distribution for stochastic weather generation and hydrological modelling in Nordic watersheds

Six precipitation probability distributions (exponential, Gamma, Weibull, skewed normal, mixed exponential and hybrid exponential/Pareto distributions) are evaluated on their ability to reproduce the statistics of the original observed time series. Each probability distribution is also indirectly assessed by looking at its ability to reproduce key hydrological variables after being used as inputs to a lumped hydrological model. Data from 24 weather stations and two watersheds (Chute‐du‐Diable and Yamaska watersheds) in the province of Quebec (Canada) were used for this assessment. Various indices or statistics, such as the mean, variance, frequency distribution and extreme values are used to quantify the performance in simulating the precipitation and discharge. Performance in reproducing key statistics of the precipitation time series is well correlated to the number of parameters of the distribution function, and the three‐parameter precipitation models outperform the other models, with the mixed exponential distribution being the best at simulating daily precipitation. The advantage of using more complex precipitation distributions is not as clear‐cut when the simulated time series are used to drive a hydrological model. Although the advantage of using functions with more parameters is not nearly as obvious, the mixed exponential distribution appears nonetheless as the best candidate for hydrological modelling. The implications of choosing a distribution function with respect to hydrological modelling and climate change impact studies are also discussed. Copyright © 2012 John Wiley & Sons, Ltd.     

Homogeneity analysis of Turkish meteorological data set

The missing value interpolation and homogeneity analysis were performed on the meteorological data of Turkey. The data set has the observations of six variables: the maximum air temperature, the minimum air temperature, the mean air temperature, the total precipitation, the relative humidity and the local pressure of 232 stations for the period 1974–2002. The missing values on the monthly data set were estimated using two methods: the linear regression (LR) and the expectation maximization (EM) algorithm. Because of higher correlations between test and reference series, EM algorithm results were preferred. The homogeneity analysis was performed on the annual data using a relative test and four absolute homogeneity tests were used for the stations where non‐testable series were found due to the low correlation coefficients between the test and the reference series. A comparison was accomplished by the graphics where relative and absolute tests provided different outcomes. Absolute tests failed to detect the inhomogeneities in the precipitation series at the significance level 1%. Interestingly, most of the inhomogeneities detected on the temperature variables existed in the Aegean region of Turkey. It is considered that theseinhomogeneities were mostly caused by non‐natural effects such as relocation. Because of changes at topography at short distance in this region intensify non‐random characteristics of the temperature series when relocation occurs even in small distances. The marine effect, which causes artifical cooling effect due to sea breezes has important impact on temperature series and the orograhpy allows this impact go through the inner parts in this region. Copyright © 2010 John Wiley & Sons, Ltd.