韶关市年和月最大日降水量多年一遇的极值计算

用皮尔逊-III型分布分别对韶关市年和月最大日降水量两种变量进行拟合,进而计算了它们在不同重现期下的极值。结果表明:韶关市50年一遇的估算年最大日降水量是220.5 mm,100年一遇是244.1 mm。实况表明,韶关在1956~2006年间出现的最大日降水量是234.8 mm,与韶关50年一遇估算的日降水量相吻合;韶关平均2.2年出现一次100 mm以上的日降水量,而该降水量的估算重现期是2年,亦十分接近;总体上拟合良好,对其不同重现期下的极值估计结果可信。对月最大日降水量的拟合以3月的拟合误差最小,5~7月10年一遇以上的日降水量均超过100 mm,说明这3个月较其他月份容易出现大暴雨,尤其6月份所有重现期的最大日降水量均为其它月份之最,5月和7月次之。     

新会年和月最大日降水量多年一遇的极值计算

利用新会降水资料,用皮尔逊一Ⅲ型分布分别对新会年最大日降水量和1～12月的月最大日降水量进行拟合,进而计算了它们在不同重现期下的极值。结果表明:新会50a一遇的估算年最大日降水量是308mm,100a一遇是335mm。新会4～9月5a一遇的最大日降水量接近或超过100mm,说明汛期各月比较容易出现大暴雨。除7月外,4～9月新会50年一遇的日降水量均超过200mm。经检验,除了2月和12月外,年和各月最大日降水量通过0.05显著水平检验,拟合效果良好,对其不同重现期下的极值估计结果可信。     

用极值分布计算黔南最大一日降水量的重现期

该文应用极值分布计算黔南各县市最大日降水量的重现期，结果表明：都匀市最大降水量５０年一遇为１８８．１ｍｍ，１００年一遇为２０７．６ｍｍ，２００年一遇为２２７．０ｍｍ，罗甸２００年一遇为３０８．２ｍｍ，为全州最大，龙里２００年一遇为１３８．９ｍｍ为全州最小，此外，还计算了罗甸１９７６年５月２４日出现的全省最大一日降水量３３６．７ｍｍ的重现期约为４００年一遇。     

珠海市年雨量和年最大日雨量多年一遇的极值计算

用皮尔逊-III型分布分别对珠海市年雨量和年最大日雨量两种变量进行拟合,进而计算了它们在不同重现期下的极值。结果表明:珠海市50年一遇的估算年雨量和年最大日雨量分别是2 908 mm和565 mm,100年一遇的分别是3 042 mm和648 mm。用澳门和珠海的雨量历史资料验证发现:澳门在104年间出现的最大年雨量是3 041.4 mm,与珠海百年一遇估算雨量吻合;珠海平均7.2年出现一次2 500 mm以上年雨量,而该雨量的估算重现期是7.5年,亦十分接近;年最大日雨量的估算重现期则比实际重现期小些。可见拟合良好,对其不同重现期下的极值估计结果可信。     

广东年最大月降水量的重现期计算

广东地处华南南部,是全国雨量最丰沛的地区。全省各地的年平均降水量在１３７８～２５２６ｍｍ之间,最多的年降水量曾超过３６００ｍｍ,有些地方一个月的降水量就超过１２００ｍｍ。降水的季节性明显,雨季为４～９月,暴雨量占年降水量的３０％左右［１］。本文利用现有年最大月降水量资料,根据极值分布理论,计算Ｋ年一遇的年最大月降水量分布。１　计算方法　　本文采用指数分布法,形式为：Ｉ∧＝ａｌｇＴ＋ｂ（１）………………………其中Ｉ∧为降水强度（ｍｍ／日）,Ｔ为重现期（年）,ａ、ｂ为待定系数。把ｌｇＴ看成变数,则上…     

吐鲁番一日最大降水量年极值频率分布

利用三参数Weibull分布函数对吐鲁番平原、山区一日最大降水量年极值频率分布进行了拟合,分析了平原、山区一日最大降水量的频率分布特征,同时应用甘倍尔分布和对数正态分布也进行了拟合。结果表明：对吐鲁番平原、山区一日最大降水量极值频率分布用三参数Weibull分布拟合比甘倍尔分布、对数正态分布精度要高。利用上述3种方法对吐鲁番平原40多年、山区30多年一日最大降水量重现期进行了估计。     

广州市单日降水量极值特征、分布拟合与推算

选取最优概率分布函数有助于提高气象要素重现期极值计算的可靠性。基于广州气象站1908—2016年逐日降水资料,构建年最大日降水量序列,采用线性趋势分析方法,研究了广州市年最大日降水量的变化特征,选取皮尔逊-Ⅲ型、对数正态、指数和耿贝尔-Ⅰ分布4种分布函数拟合广州市年最大日降水量序列,并按ω2检验、似然比检验等方法进行拟合优度检验。结果表明,近56年来,广州市年最大日降水量呈不显著的增加趋势。4—9月日最大降水量出现次数较多,6个月的出现次数占全年的93. 6%,其中,前汛期出现次数大于后汛期的。对数正态分布确定为广州市年最大日降水量拟合最优分布函数。对数正态分布估算的广州市50 a一遇的年最大日降水量是240. 1 mm,100 a一遇的是266. 1 mm,150 a一遇估算的是281. 4 mm。观测资料表明,广州平均1. 8 a出现一次150 mm以上的日降水量,而该降水量的估算重现期是1. 9 a,相当吻合。     

基于两种分布的勉县年最大日降水量重现期估算

2021年8月22日勉县遭遇极端降水事件,日降水量高达2379 mm,灾害十分严重。统计分析1959—2021年勉县历史逐年最大日降水量特点,采用皮尔逊Ⅲ型（简称P-Ⅲ型）曲线分布和耿贝尔极值分布方法推算重现期及降水量,并将两者进行比较,对2021年8月22日极端大暴雨进行重现期估算。结果表明：勉县年最大日降水年际变化明显,2008年以来变率增大且有更极端的趋势;基于P-Ⅲ型曲线分布和耿贝尔极值分布的1959—2020年最大日降水积累概率拟合效果均较好,但耿贝尔极值分布对年最大日降水量的拟合优于P-Ⅲ型分布;应用耿贝尔极值分布推算勉县极值降水,100 a一遇的日降水量为1547 mm,2021年8月22日降水量2379 mm的重现期为4 88133 a。增加2021年最大日降水量进入样本序列重新构建耿贝尔极值分布函数,推算日降水量2379 mm的重现期为70735 a,100 a一遇的估算降水量为1834 mm,重现期及降水量估算变化均较大,说明超极端降水和样本长度对重现期的推算影响较大。     

日最大降水量服从极值分布的一个例证

采用经验及统计理论方法推算出新疆喀什日最大降水量服从极值分布,其分布函数ｐ（Ｒ）＝１－ｅｘｐ｛－ｅｘｐ［－１６．７８（Ｒ－１３．５８）］｝。这为“原始分布为指数分布,其最大项服从极值分布”的理论提供了又一个例证。     

多年平均年降水量的面积分布律

通过对全国，新疆，天山乌鲁木齐河流域三种不同区域多年平均年降水量的计算分析发现，在这三种区域里，多年平均年降水量与其占有的面积之间丰同一种函数关系－我指分布函数关系。这一关系式的得出，有助于（１）定量地描述某一气候区的降水气候状况。并且，这种对问题的提法既不太粗，也不太细，而是只提降水量为某一值的区域占有多大的比例；（３）根据河水径流深估计无雨量站区域的多年降水量和计算水文气象学中的一些参数。     

西南地区极端降水变化趋势

利用西南地区90个气象台站1970-2010年逐日降水量资料,依据世界气象组织(WMO)定义的连续5d最大降水量、总降水量、强降水比等6种极端降水指数,采用F检验、11a滑动平均等统计方法,研究了西南地区极端强降水变化趋势的时空变化特征。在时间上,西南地区近41年来冬、春、夏季连续5d最大降水量缓慢波动上升,秋季连续5d最大降水量呈下降趋势;强降水、降水强度及强降水比呈上升趋势,但总降水量和最长持续无降水日数呈减少趋势;另外,各极端降水指数还存在明显的年际、年代际变化。在空间上,西南地区极端降水变化趋势具有显著的地域差异,呈东西或西北东南向梯度变化特征。其中冬季连续5d最大降水量、降水强度、强降水比及最长持续无降水日数,在西南大部分地区呈增加趋势。秋季连续5d最大降水量与总降水量在西南大部分地区呈减少趋势。而春、夏季连续5d最大降水量和强降水的增减区域大致相当。     

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。     

Periodic signals in Spanish monthly precipitation data

Summary Forty-two monthly rainfall time series from Spain have been analyzed for the presence of cycles and regional patterns. The study was carried out by means of periodicity tests devised by Whittle, Hannan, Bartlett, Siegel, and Priestley. These tests contribute to a mixed spectrum analysis leading to the evaluation of signals versus the background of coloured noise (autocorrelation). Also, a direct maximum entropy method (MEM) for estimating spectra was applied to the series, in particular an autoregressive (AR) power spectral density (psd) estimation, and an autoregressive moving average (ARMA) psd estimation, with parameters obtained by exact maximum likelihood estimator (MLE).With 4 Figures     

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     

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.     

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     

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...     

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。     

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.