4种再分析月降水数据在内蒙古地区的适用性分析

利用1981—2020年气象台站的实测降水数据,对CRA40、ERA5、JRA55和MERRA2这4种再分析月降水数据在内蒙古地区的降水分布特征,与实测降水的相关性和误差进行分析。结果表明：(1)内蒙古地区4种再分析降水数据的空间分布与台站降水基本一致,误差分析表明CRA40与MERRA2的数据质量较高,ERA5次之,而JRA55数据质量相对较差。(2)CRA40和MERRA2在1983—1986年存在明显的降水低估,ERA5在2005年之后的内蒙古中东部出现明显的降水低估,JRA55在115°E以东存在明显降水高估,在115°E以西则以降水低估为主。(3)4种再分析月降水数据的年内最大均方根误差和绝对误差均集中在6—8月,与台站降水相关系数的年内最小值均出现在7月,内蒙古夏季汛期是再分析降水误差产生的主要时段。     

Spatial analysis of monthly precipitation in Turkey

Summary  The principal objectives of this paper are to develop and validate an optimum interpolation method for the spatial analysis of monthly precipitation in Turkey. A two-dimensional optimum interpolation objective analysis scheme has been developed for the spatial analysis of precipitation. The model is developed for generating statistically optimum interpolation based on the irregular distribution of meteorological stations. One question that affects the optimum interpolation method and, indeed, all such techniques, is how many observations should be allowed to influence a given grid point? The method developed in this paper addresses this question. For the implementation of the method, 52 stations are considered for Turkey, with 30 years of monthly data at each point. It is observed that each monthly average spatial correlation function shows a monotonically decreasing pattern based on 15 km interval averages. The method provides high estimation accuracy in dense station locations such as in northwestern Turkey. Precipitation contour maps obtained by the optimum interpolation method indicate two spatial trends over Turkey which have not been identified in any previous study. Received June 24, 1999/Revised April 26, 2000     

降水时间随机性对月总降水量的影响

降水量在时间分布上呈现较大的随机性,极端降水事件尤为如此。受此影响,月初(月末) 1～5 d之内的累积降水量很可能会超过当月总降水量的50%乃至更多。对1961—2017年中国2 400多站点资料统计分析结果发现,月初(月末) 1～5 d累积降水量对当月总降水量显著影响事件的出现频次,在季节和空间分布上都有鲜明特征。主要包括:1)月初累积降水量对秋冬季中各月的总降水量影响更大,月末累积降水量对1—4月的月总降水量影响较大。2)受月初累积降水量的影响,显著站点数在某些年份的某些月份出现极大值;受单次事件显著影响的站点数占全国总站点数的30%～50%,此即对应着一次全国大范围的极端降水事件。3)受月初(月末)累积降水量显著影响的站点空间分布随季节变化呈现出明显空间集聚特征。     

Secular change of extreme monthly precipitation in Europe

Summary ¶Temporal changes in the occurrence of extreme events in time series of observed precipitation are investigated. The analysis is based on a European gridded data set and a German station-based data set of recent monthly totals (1896/1899–1995/1998). Two approaches are used. First, values above certain defined thresholds are counted for the first and second halves of the observation period. In the second step time series components, such as trends, are removed to obtain a deeper insight into the causes of the observed changes. As an example, this technique is applied to the time series of the German station Eppenrod. It arises that most of the events concern extreme wet months whose frequency has significantly increased in winter. Whereas on the European scale the other seasons also show this increase, especially in autumn, in Germany an insignificant decrease in the summer and autumn seasons is found. Moreover it is demonstrated that the increase of extreme wet months is reflected in a systematic increase in the variance and the Weibull probability density function parameters, respectively.Received July 18, 2002; revised January 24, 2003; accepted February 1, 2003 Published online September 10, 2003     

The QBO signal in monthly precipitation fields over Europe

Summary Areal monthly precipitation totals of 24 West and Central European regions and of 74 regions of the European part of the former USSR are studied in respect to the westerly and easterly phases of the QBO for the longest available period from 1953 to the 1980s. A significant QBO signal has been detected in September and October in the region of the British Isles, in the Central European region and in Byelorussia (in the easterly phase more precipitation and its lower variability in September and higher variability in October), but also in small region of the south Ukraine in August and in October. Regions of the eastern Ukraine and adjoined regions of Russia had the significant QBO signal in May (more precipitation and its prevalent lower variability in the westerly phase). The observed effects are explained by circulation differences in the westerly and easterly phases of the QBO.With 2 Figures     

贵州年降水量和年最大月降水量多年一遇的极值计算

根据贵州省84个气象站1961-2007年的月降水资料,用耿贝尔极值I型分布分别对贵州省年降水量和年最大月降水量2种变量进行拟合,进而计算它们在不同重现期下的极值.结果表明:贵州省的降水量集中分布在东南部地区,其中贵州的安顺及黔东南地区降水量较为突出,晴隆15、30、50a一遇的年最大月、年降水量分别为559.8mm、617.1mm、658.9mm和2 019.6mm、2 177.9mm、2 293.2mm,都匀15、30、50a一遇的年最大月、年降水量分别为557mm、626.6mm、677.3mm和1 892.8mm、2 044.9mm、2 155.8mm,对拟合结果的评判采用相对均方根误差进行检验,其年降水量平均相对均方根误差为6%,年最大月降水量平均相对均方根误差为9%,可见拟合良好,对其不同重现期下的极值估计结果可信.     

Observed monthly precipitation trends in China 1951–2002

Summary Monthly precipitation trends of 160 stations in China from 1951–2002 have been analysed and interpolated. The Mann-Kendall trend test was applied to examine the monthly precipitation data. Significant positive and negative trends at the 90, 95, and 99 percent confidence levels were detected for numerous stations. The number, distribution, and direction of the trends varied from month to month.The detected trends were spatially interpolated by applying the Inverse Distance Weighted (IDW) interpolation method. The spatial presentation of the detected precipitation trends gives a better understanding of climatic changes or variations in China during the last 50 years. This is especially the case for highlighting the spatial structure of precipitation trends.A clustering of trends is observed in certain months, including distinct trend belts especially in east and north-east China. Nevertheless, positive as well as negative monthly trends can be noted simultaneously in different areas. The spatial interpolation of precipitation trend analysis results is a useful approach to give further understanding of the regional pattern of precipitation trends in China.     

月尺度降水可预报性的实验研究

利用信噪方差比探讨了山东省月尺度降水可预报性,发现２～３月、９～１０月不可预报,其它月份可预报性强。一般大气环流系统冬夏转换季节可预报性差。与１３个代表站预报评分进行了比较,发现可预报性指数越高,则实际预报评分也相对较高,两者有较好的对应关系。     

全球地面降水月值历史数据集研制

全球降水历史数据是开展气候、水循环等研究的基础。收集整理全球12个数据源降水历史月值资料,通过站号、站名甄别不同数据源中相同台站,对344个通过相关系数、一致率、均值t检验、方差F检验的台站多源资料进行拼接,尽可能多地融合各套数据产品优势,最终形成全球降水历史月值数据集(CMA Global PrecipitationV1.0, CGP)。数据集重点解决当前国际数据产品在东亚地区站点稀少、同时应用多套数据应用门槛较高等问题。数据集收录3.1万个台站共计1.87×107组月降水记录, 4152个台站序列长度达百年。与美国大气海洋局(NOAA)的全球降水数据集(GHCN-M V2.0)对比,CGP新增1万个站点、0.5×107组有效观测记录和1030条百年序列,其中141条百年序列通过多源整合技术获取。CGP的站点和数据量优势主要体现在东亚、东欧、西伯利亚等站点稀疏地区。基于CGP分析的全球降水时空特征与国际同类产品的结果较一致。新增的数据虽然没有改变全球降水分布的总体特征,但对区域性的百年降水变化检测有一定影响。基于CGP的全球降水百年序列结果显示,20世纪前半叶全球降水量偏小,近20年是1900年以来全球降水量最大的时期,各纬度带、各个国家或地区的降水长期变化趋势呈现显著的差异。      

人工神经网络在月降水量预测业务中的研究和应用综述

月降水量的年际变化具有显著的非线性变化特征,预测难度大,历来是重大气象灾害预测的重点难点问题.BP(back propagation)神经网络在月降水量预测业务中的研究和应用中,取得了较好的成果,其中应用较广泛的是PCA-BP神经网络模型、遗传算法优化神经网络、RBF神经网络预测模型、小波神经网络模型、粒子群-神经网络...     

Statistical significance of trends in monthly heavy precipitation over the US

Trends in monthly heavy precipitation, defined by a return period of one year, are assessed for statistical significance in observations and Global Climate Model (GCM) simulations over the contiguous United States using Monte Carlo non-parametric and parametric bootstrapping techniques. The results from the two Monte Carlo approaches are found to be similar to each other, and also to the traditional non-parametric Kendall’s τ test, implying the robustness of the approach. Two different observational data-sets are employed to test for trends in monthly heavy precipitation and are found to exhibit consistent results. Both data-sets demonstrate upward trends, one of which is found to be statistically significant at the 95% confidence level. Upward trends similar to observations are observed in some climate model simulations of the twentieth century, but their statistical significance is marginal. For projections of the twenty-first century, a statistically significant upwards trend is observed in most of the climate models analyzed. The change in the simulated precipitation variance appears to be more important in the twenty-first century projections than changes in the mean precipitation. Stochastic fluctuations of the climate-system are found to be dominate monthly heavy precipitation as some GCM simulations show a downwards trend even in the twenty-first century projections when the greenhouse gas forcings are strong.     

A nonlinear time-lag differential equation model for predicting monthly precipitation

This paper investigates the nonlinear prediction of monthly rainfall time series which consists of phase space con-tinuation of one-dimensional sequence, followed by least-square determination of the coefficients for the terms of the time-lag differential equation model and then fitting of the prognostic expression is made to 1951-1980 monthly rainfall datasets from Changsha station Results show that the model is likely to describe the nonlinearity of the an-nual cycle of precipitation on a monthly basis and to provide a basis for flood prevention and drought combating for the wet season.     

Statistical analysis of daily maximum and monthly precipitation at Belgrade

Summary  A statistical analysis of monthly mean and daily maximum precipitation at Belgrade during the period 1888–1995 is presented. A very high correlation coefficient exists between the monthly and daily maximum precipitation. Weather types that are associated with the maximum daily precipitation at Belgrade are also analysed. Received July 19, 1999 Revised December 25, 1999     

Postprocessing of simulated precipitation for impact research in West Africa. Part I: model output statistics for monthly data

Rainfall represents an important factor in agriculture and food security, particularly, in the low latitudes. Climatological and hydrological studies which attempt to diagnose the hydrological cycle, require high-quality precipitation data. In West Africa, like in many parts of the world, the density of observational data is low and climate models are needed in order to perform homogeneous and complete data sets. However, climate models tend to produce systematic errors, especially, in terms of rainfall and cloud processes, which are usually approximated by physical parameterizations. In this study, a 25-year climatology of monthly precipitation in West Africa is presented, derived from a regional climate model simulation, and evaluated with respect to observational data. It is found that the model systematically underestimates the rainfall amount and variability and does not capture some details of the seasonal cycle in sub-Saharan West Africa. Thus, in its present form the precipitation climatology is not appropriate to draw a realistic picture of the hydrological cycle in West Africa nor to serve as input data for impact research. Therefore, a statistical model is developed in order to adjust the simulated rainfall data to the characteristics of observed precipitation. Assuming that the regional climate model is much more reliable in terms of atmospheric circulation and thermodynamics, model output statistics is used to correct simulated rainfall by means of other simulated parameters of the near-surface climate like temperature, sea level pressure and wind components. Monthly data is adjusted by a cross-validated multiple regression model. The resulting adjusted rainfall climatology reveals a substantial improvement in terms of the model deficiencies mentioned above. In part II of this publication, the characteristics of simulated daily precipitation is adapted to station data by applying a weather generator. Once the postprocessing approach is trained, it can be extrapolated to simulation periods, for which observational data do not exist like for instance future climate.     

Trends in monthly precipitation over the northwest of Iran (NWI)

Increasing global temperatures during the last century have had their own effects on other climatic conditions, particularly on precipitation characteristics. This study was meant to investigate the spatial and temporal monthly trends of precipitation using the least square error (LSE) approach for the northwest of Iran (NWI). To this end, a database was obtained from 250 measuring stations uniformly scattered all over NWI from 1961 to 2010. The spatial average of annual precipitation in NWI during the period of study was approximately 220.9–726.7 mm. The annual precipitation decreased from southwest to northeast, while the large amount of precipitation was concentrated in the south-west and in the mountainous areas. All over NWI, the maximum and minimum precipitation records occurred from March to May and July to September, respectively. The coefficient of variation (CV) is greater than 44 % in all of NWI and may reach over 76 % in many places. The greatest range of CV, for instance, occurred during July. The spatial variability of precipitation was consistent with a tempo-spatial pattern of precipitation trends. There was a considerable difference between the amounts of change during the months, and the negative trends were mainly attributed to areas concentrated in eastern and southern parts of NWI far from the western mountain ranges. Moreover, limited areas with positive precipitation trends can be found in very small and isolated regions. This is observable particularly in the eastern half of NWI, which is mostly located far from Westerlies. On the other hand, seasonal precipitation trends indicated a slight decrease during winter and spring and a slight increase during summer and autumn. Consequently, there were major changes in average precipitation that occurred negatively in the area under study during the observation period. This finding is in agreement with those findings by recent studies which revealed a decreasing trend of around 2 mm/year over NWI during 1966–2005.

     

降尺度方法在安徽省月降水量预测中的试用

基于NCEP/NCAR 500 hPa高度场、T63月动力延伸预报500 hPa高度场和安徽省降水资料,依据动力预报产品释用方法中所建立的月降水距平百分率预报方程,从月和旬两种不同时间尺度以及固定资料和选择资料来反演方程系数共4种降尺度方法来预报安徽省20个代表站月降水。1995—2005年11 a的回报检验表明了4种方法都具有较好的预报能力,从旬时间尺度较月尺度来预报月降水具有优势,在汛期和汛期降水偏多年更为明显。     

Probability of occurrence of monthly and seasonal winter precipitation over Northwest India based on antecedent-monthly precipitation

Theoretical and Applied Climatology - Winter (December, January, and February (DJF)) precipitation over northwest India (NWI) is mainly associated with the eastward moving mid-latitude synoptic...     

Simultaneous statistical modeling of monthly precipitation amount at several observational points

Demonstrated is the possibility of the group modeling of the series of monthly precipitation amount using the method of canonical expansion. Used are the algorithms and software computing programs developed as applied to the statistical models of the monthly and annual river runoff. The model of canonical expansion preserves all statistical characteristics of initial series.     

Some annual variation characteristics for the Northern Hemispheric monthly mean precipitation fields

By the utilization of monthly precipitation data from all stations in the Northern Hemisphere annexed to the “World Survey of climatology, Vol. 1-15, the distributions of the maximum precipitation months (MPM), the annual relative precipitation (ARP) and the monthly relative precipitation (percent of annual) in January and July are respectively mapped. Moreover the distributions of intermonthly relative precipitation variabilities from January to December are plotted as well. From these figures, the precipitation in the Northern Hemisphere may be classified into three types (continental, oceanic and transitional types) and 17 regions. The precipitation regime may also be divided into two patterns, the global and regional patterns. The global pattern consists of planetary front system and ITCZ and its inter-monthly variation shows the north-and-south shift of the rain belt; the regional pattern consists of the sea-land monsoon and plateau monsoon regime, in which the inter-monthly variation of rain belt shows a east- and-west shift.     

Predicting monthly precipitation along coastal Ecuador: ENSO and transfer function models

Theoretical and Applied Climatology - It is well known that El Niño-Southern Oscillation (ENSO) modifies precipitation patterns in several parts of the world. One of the most impacted areas is...