Analysis of the long-term precipitation series at Athens,Greece

In this paper, the characteristics of the long-term precipitation series at Athens (1858–1985) have been statistically analyzed. This study covers both the history and the analysis of the data. The ten-year mean amounts, the monthly and annual amounts averaged over the intervals 1858–1890, 1891–1985, 1951–1980, 1858–1985, the mean number of hours of precipitation and the precipitation intensity are given. The analysis of long-term time series of climatic data (in particular precipitation) is a useful tool for the study of past climate. Different statistical techniques are used in order to depict monthly, seasonal and annual variations, as well as trends, periodicities and recurrence intervals of the amount, intensity and number of precipitation days. The analysis reveals many interesting characteristics. These characteristics of the precipitation regime are extended to a time scale from seasonal variation to a semi-secular trend. The study of such long-term series may be helpful not only in practical applications of rainfall, but also for explaining the possible physical or anthropogenic mechanisms of climatic fluctuations and tendencies. The series of precipitation at Athens is one of the longest in south-eastern Europe.     

Low precipitation over Greece during 1989–1990

Summary The unusual low precipitation observed during 1989–1990 in Greece is the subject of the present study. Statistical comparisons of this drought period with other dry periods in the historical records, possible statistical cycles, (periodicities), or general trends, together with the behaviour of the long time series are investigated. Power spectra of the annual rainfall series are also plotted considering the 95 years of data for the most reliable station in Greece. Furthermore, the drought persistence between successive months is also analysed using standard methods. The analysis identified periodicities of 2.1 and 3.6 years while no significant trends were observed in almost all cases. Finally the persistency of successive dry days was found to be insignificant after the fourth day.With 5 Figures     

Comparison of rainfall structures between NCEP/NCAR reanalyses and observed data over tropical Africa

 A comparison is made between modelled (NCEP/NCAR reanalysis) and observed (CRC and CRU dataset) annual and monthly precipitation over tropical Africa during the period 1958–1997. The split moving-windows dissimilarity analysis (SMWDA) is used to locate abrupt changes in rainfall time series. In the NCEP reanalysis data, we identify a main abrupt shift, which occurs in 1967 and concerns more than 50% of grid points. In the observation, this shift is only found over parts of tropical North Africa. Three other NCEP abrupt shifts (1975, 1983 and 1987) in the reanalysis shown by the SMWDA, each concerning about 20% of tropical Africa, are not identified in the observation. One hypothesis concerning the 1967 marked abrupt shift is a problem of data assimilation in the NCEP/NCAR model which generated artificial shifts in the time series. In view of this result, further comparisons have been restricted to the period 1968–1997. On a continental scale, while the CRC and NCEP mean seasonal rainfall patterns are almost the same, however, some regional features are not well reproduced. Using five regional rainfall indexes, the mean seasonal rainfall cycle is correctly reproduced, but the NCEP reanalysis generally underestimates the amounts during the rainy season. The NCEP reanalysis rainfall is closer to the observation when the region shows a single rainy season. The correlation values between NCEP and CRC interannual rainfall variations over the period 1968–1997 are very low and seldom significant. The NCEP four main structures of rainfall variability as deducted from rotated principal component analysis are not realistic at all and the associated time series are systematically dominated by a marked low-frequency variability not present in the observation. However, the main teleconnections between ENSO and African rainfall variations are fairly well reproduced, with a correct location of the main structures, but with lower correlation values than those found in the observation. Received: 22 September 1999 / Accepted: 10 May 2000     

Are droughts predictable?

From a study of rainfall series at several locations, it is shown that approximate but meaningful predictions of drought-prone intervals are possible only for regions where the rainfall series have prominent periodicities (of large amplitudes) in the long periodicity region (about 10 yr or more), provided the patterns do not change abruptly and drastically. Also, rainfall for individual years cannot be predicted accurately.     

Iranian rainfall series analysis by means of nonparametric tests

The study of the trends and fluctuations in rainfall has received a great deal of attention, since changes in rainfall patterns may lead to floods or droughts. The objective of this study was to analyze the annual, seasonal, and monthly rainfall time series at seven rain gauge stations in the west of Iran for a 40-year period (from October 1969 to September 2009). The homogeneity of the rainfall data sets at the rain gauge stations was checked by using the cumulative deviations test. Three nonparametric tests, namely Kendall, Spearman, and Mann–Kendall, at the 95 % confidence level were used for the trend analysis and the Theil–Sen estimator was applied for determining the magnitudes of the trends. According to the homogeneity analysis, all of the rainfall series except the September series at Vasaj station were found to be homogeneous. The obtained results showed an insignificant trend in the annual and seasonal rainfall series at the majority of the considered stations. Moreover, only three significant trends were observed at the February rainfall of Aghajanbolaghi station, the November series of Vasaj station, and the March rainfall series of Khomigan station. The findings of this study on the temporal trends of rainfall can be implemented to improve the water resources strategies in the study region.     

Spectral characteristics of the series of annual rainfall in England and Wales

Despite the small size of England and Wales, the rainfall there shows large variations, possibly due to geographical and topographical conditions. Spectral analysis shows a large number of periodicities, but many are statistically insignificant, indicating considerable randomness. Significant periodicities are invariably in the low periodicity region (periodicity < 5 yr). Occasionally, high periodicities are also encountered, but are always accompanied by low periodicities. Hence, meaningful predictions are not possible.     

Analysis of daily rainfall processes in lower extremadura (Spain) and homogenization of the data

Summary In this paper we analyze, from the point of view of stochastic processes, daily rainfall data recorded at the Badajoz Observatory (Southwestern Spain) since the beginning of the century. We attempt to identify any periodicities or trends in the daily rainfall occurrences and their dependence structure, and attempt to select the appropriate point stochastic model for the daily rainfall series.Standard regression analysis, graphical methods and the Cramer statistic show a rise in the number of cases of light rain (between 0.1 and 5 mm/d) and a decline in the number of cases of moderate to heavy rain (> 5 mm/d) in the daily rainfall at least at the 5% significance level.That the homogenization process was satisfactory is shown by the mean interarrival time of the homogenized series and the test of the rate of homogenized daily rainfall occurrences. Our analysis also shows that the behavior of the spectra of the homogenized daily rainfall counts is completely different from that of a Poisson process, so that the hypothesis of a non-homogeneous Poisson process is rejected.With 7 Figures     

西南地区春季及其各月降水的气候特征分析

利用1951～2017年我国西南地区26个台站的降水观测资料,本文研究了该地区春季及其各月降水的基本气候特征。结果表明:5月是春季我国西南地区降水最多的月份,其占春季总降水的55.3%,此外,5月降水年际变化强度最大;西南地区春季及其各月降水均具有显著的年代际变化特征,5月降水在春季降水的年代际变化特征中占据主导地位;在年代际突变特征方面,西南地区春季降水存在显著的年代际突变特征,其在1970年代后期发生了减少突变,而在1990年代中后期发生了增加突变;在变化趋势方面,春季西南区域平均降水的变化趋势不明显,但从空间上看,西南地区东部表现出显著增多的趋势,而西部则表现出显著减少的趋势;在周期方面,西南地区春季及其4～5月降水均具有显著的年际和年代际变化周期,需要说明的是,3月降水没有明显的年代际变化周期,总体上看,5月降水在春季降水周期变化中占据主导地位。     

Simulation of Indian summer monsoon: experiments with SSTs

Summary Hindcasts for the Indian summer monsoons (ISMs) of 2002 and 2003 have been produced from an ensemble of numerical simulations performed with a global model by changing SST. Two sets of ensemble simulations have been produced without vegetation: (i) by prescribing the weekly observed SST from ECMWF (European Centre for Medium Range Weather Forecasting) analyses, and (ii) by adding weekly SST anomalies (SSTA) of April to the climatological SST during the simulation period from May to August. For each ensemble, 10 simulations have been realized with different initial conditions that are prepared from ECMWF data with five each from April and May analyses of both the years. The predicted June–July monsoon rainfall over the Indian region shows good agreement with the GPCP (observed) pentad rainfall distribution when 5 member ensemble is taken from May initial conditions. The All-India June–July simulated rainfall time series matches favourably with the observed time series in both the years for the five member ensemble from May initial condition but drifts away from observation with April initial conditions. This underscores the role of initial conditions in the seasonal forecasting. But the model has failed to capture the strong intra-seasonal oscillation in July 2002. Heating over equatorial Indian Ocean for June 2002 in a particular experiment using 29th May 12 GMT as initial conditions shows some intra-seasonal oscillation in July 2002 rainfall, as in observation. Further evaluation of the seasonal simulations from this model is done by calculating the empirical orthogonal functions (EOFs) of the GPCP rainfall over India. The first four EOFs explain more than 80% of the total variance of the observed rainfall. The time series of expansion coefficients (principal components), obtained by projecting on the observed EOFs, provide a better framework for inter-comparing model simulations and their evaluation with observed data. The main finding of this study is that the All-India rainfall from various experiments with prescribed SST is better predicted on seasonal scale as compares to prescribed SST anomalies. This is indicative of a possible useful seasonal forecasts from a GCM at least for the case when monsoon is going to be good. The model responses do not differ much for 2002 and 2003 since the evolution of SST during these years was very similar, hence July rainfall seems to be largely modulated by the other feedbacks on the overall circulation.     

All-India monthly and seasonal rainfall series: 1871–1993

Summary The Indian rainfall has often been used as a proxy data for the Asian monsoon as a whole for understanding the energy budget of the major circulation features and also used as an input parameter in estimating the other regional parameters. In view of this, a long homogeneous rainfall series of All-India (India taken as one unit) has been prepared based on a fixed and well distributed network of 306 raingauge stations over India by giving proper area-weightage. This paper contains a listing of All-India monthly, seasonal and annual homogeneous data series for the period 1871–1993. Some statistical details and long-term changes of the All-India monsoon rainfall have been discussed.With 4 Figures     

Wavelet analysis of a centennial (1895–1994) southern Brazil rainfall series (Pelotas, 31°46′19″S 52°20′ 33″W)

In this work we apply the wavelet transform to the Pelotas (southern Brazil) total annual rainfall series (1894–1995). Classical, wavelet and cross-wavelet analyses were performed in the El Niño Southern Oscillation (ENSO), Quasi-Biennial Oscillation (QBO), sunspot number (Rz) and Pelotas rainfall time series. Classical spectral analysis for Pelotas has shown a large number of short periods – between 2.2–5.6 years (yr) and periods at 8.9, 11.7 and 24.9 yr. Further, we have found that the Pelotas rainfall wavelet spectrum shows the most significant periodicities around 2–8 yr, but they have an intermittent character. Cross-wavelet spectrum showed that: rainfall and QBO series are correlated at 2–3 yr (QBO) scales and this cross-power is continuous along the time series interval; rainfall and SOI have higher cross-power around 4–8 yr, but this signal is sporadic; rainfall and sunspot number (Rz) showed higher cross-power around the 11-yr solar cycle period, but this cross-power is sporadically high and low; finally, the rainfall cross-spectrum with the double sunspot number (Rz22) revealed a high cross-power around 20–22 yr which is more persistent in duration, compared to the 11-yr period. These wavelet results are compared with classical spectral analysis and with previous work results. We concluded that the phenomenon that influences most of Pelotas rainfall variability is ENSO, but only a minor part of the variance (～30%) can be described by a simple multi-linear dependence on solar/ENSO/QBO phenomena, this result could imply that non-linear coupling among sun and internal climatic variability (QBO, ENSO) has an important role in the local/regional climate variations.     

Relationships between rainy days,mean daily intensity and seasonal rainfall in normal,flood and drought years over india

There are limitations in using the seasonal rainfall total in studies of Monsoon rainfall climatology. A correlation analysis of the individual station seasonal rainfall with all India seasonal mean rainfall has been made. After taking the significance test (strictly up to 5% level) the stations which are significantly correlated have been considered in this study in normal, flood and drought years respectively. Analysis of seasonal rainfall data of 50 stations spread over a period of 41 years suggests that a linear relationship fits better than the logarithmic relationship when seasonal rain-fall versus number of rainy days is studied. The linear relationship is also found to be better in the case of seasonal rainfall versus mean daily intensity.     

Spatiotemporal variations in rainfall erosivity during the period of 1960–2011 in Guangdong Province,southern China

Rainfall erosivity, which shows a potential risk of soil loss caused by water erosion, is an important factor in soil erosion process. In consideration of the critical condition of soil erosion induced by rainfall in Guangdong Province of southern China, this study analyzed the spatial and temporal variations in rainfall erosivity based on daily rainfall data observed at 25 meteorological stations during the period of 1960–2011. The methods of global spatial autocorrelation, kriging interpolation, Mann–Kendall test, and continuous wavelet transform were used. Results revealed that the annual rainfall erosivity in Guangdong Province, which spatially varied with the maximum level observed in June, was classified as high erosivity with two peaks that occur in spring and summer. In the direction of south–north, mean annual rainfall erosivity, which showed significant relationships with mean annual rainfall and latitude, gradually decreased with the high values mainly distributed in the coastal area and the low values mainly occurring in the lowlands of northwestern Guangdong. Meanwhile, a significant positive spatial autocorrelation which implied a clustered pattern was observed for annual rainfall erosivity. The spatial distribution of seasonal rainfall erosivity exhibited clustering tendencies, except spring erosivity with Moran’s I and Z values of 0.1 and 1.04, respectively. The spatial distribution of monthly rainfall erosivity presented clustered patterns in January–March and July–October as well as random patterns in the remaining months. The temporal trend of mean rainfall erosivity in Guangdong Province showed no statistically significant trend at the annual, seasonal, and monthly scales. However, at each station, 1 out of 25 stations exhibited a statistically significant trend at the annual scale; 4 stations located around the Pearl River Delta presented significant trends in summer at the seasonal scale; significant trends were observed in March (increasing trends at 3 stations), June (increasing trends at 4 stations located in the Beijiang River Basin), and October (decreasing trends at 4 stations) at the monthly scale. In accordance with the mean annual rainfall over Guangdong Province, the mean annual rainfall erosivity showed two significant periodicities of 3–6 and 10–12 years at a confidence level of 95 %. In conclusion, the results of this study provide insights into the spatiotemporal variation in rainfall erosivity in Guangdong Province and support for agrolandscape planning and water and soil conservation efforts in this region.     

澳门单站气象资料变化趋势的预报模型之比较

以澳门单站年和四季降水和温度两个气象要素的气象资料(1952~2001年)为主要研究对象,利用3种统计预报模型(谐波分析外推模型、开环门限自回归模型和主分量因子的逐步回归模型)进行拟合及试报,对澳门的降水及温度的变化做较深入的分析及预测,比较3种统计预报模型在研究澳门单站气象资料的变化趋势中的优劣。结果发现,3种预报模型在预报年和四季平均气温的方面都比较理想,但对降水量变化序列做拟合及试报则差异较大。总体而言,气候场的主分量逐步回归法对相同的气候资料建模做拟合及试报,结果拟合率是三者中最高的,复相关为0.821~0.911,预报效果除秋季较差外,其余都非常好。     

Variations of Temperature and Precipitation in Italy from 1866 to 1995

Summary Series of annual and seasonal temperature and precipitation representing respectively northern and southern Italy are compared for trend, interannual variability and periodicity in the period 1866–1995. Temperature and precipitation trends are almost always anticorrelated except in winter in the North where an anomalous behavior is evident till about 1980. The result is that the Italian climate has become warmer and drier especially in the South since about 1930.The interannual variability does not present significant maxima, but only minima that cannot be related tothe start of a trend either for temperature or for precipitation. The power spectra of the series show broad significant peaks containing the quasi-biennial oscillation and other well known periodicities probably due to solar cycles or to the North Atlantic ocean-atmosphere oscillation (NAO). Received December 16, 1998 Revised October 21, 1999     

Stochastic Modeling of the Van Lake Monthly Level Fluctuations in Turkey

Summary  Planning, design, construction and operation of lakeshore structures require information about the future likely extremes of the lake levels at a given risk percentage. Alternative future likely synthetic sequences can be numerically generated provided that the underlying generating mechanism of the lake level fluctuation phenomenon is identified. Simple linear and periodic nonlinear models are used for modeling the deterministic part in the lake level records. Linear trend is eliminated from the original lake level historic data by regression line technique. The nonlinear part needs two stages for its identification. First Fourier series is applied to model interannual periodicities in the lake level fluctuation series and then monthly standardization procedure is applied for seasonal periodic nonlinear component modeling. A second order Markov model is found suitable for the remaining stochastic parts. The application of the methodology is presented for the Lake Van monthly level data in eastern Turkey. Suitable models are identified and their parameters are estimated for trend, periodic and stochastic parts. Likely, synthetic lake levels are generated by the stochastic model and hence lake level extreme values are depicted for the next 2, 6, 12, 24, 60 and 120 months with risk calculations. Such risk calculations take into account the stochastic characteristics of the lake level fluctuations only. The deterministic parts as linear trends and periodicities are added to the stochastic extreme events for the actual simulation of the lake levels. The model presented in this paper is not for time prediction of future lake levels but rather for the simulation of possible equally likely extreme lake level value occurrences over any desired future period. Received April 22, 1998/Revised July 28, 1999     

Evaluation of spatial and temporal characteristics of rainfall in Malawi: a case of data scarce region

This paper presents the methods, procedure and results in studying spatial and temporal characteristics of rainfall in Malawi, a data scarce region, between 1960 and 2006. Rainfall variables and indicators from rainfall readings at 42 stations in Malawi, excluding Lake Malawi, were analysed at monthly, seasonal and annual scales. In the study, the data were firstly subjected to quality checks through the cumulative deviations test and the standard normal homogeneity test. Spatial rainfall variability was investigated using the spatial correlation function. Temporal trends were analysed using Mann?CKendall and linear regression methods. Heterogeneity of monthly rainfall was investigated using the precipitation concentration index (PCI). Finally, inter-annual and intra-annual rainfall variability were tested using normalized precipitation anomaly series of annual rainfall series (|AR|) and the PCI (|APCI|), respectively. The results showed that (1) most stations revealed statistically non-significant decreasing rainfall trends for annual, seasonal, monthly and the individual months from March to December at the 5% significance level. The months of January and February (the highest rainfall months), however, had overall positive but statistically non-significant trends countrywide, suggesting more concentration of the seasonal rainfall around these months. (2) Spatial analysis results showed a complex rainfall pattern countrywide with annual mean of 1,095?mm centred to the south of the country and mean inter-annual variability of 26%. (3) Spatial correlation amongst stations was highest only within the first 20?km, typical of areas with strong small-scale climatic influence. (4) The country was further characterised by unstable monthly rainfall regimes, with all PCIs more than 10. (5) An increase in inter-annual rainfall variability was found.     

Interrelationship of rainfall,temperature and reference evapotranspiration trends and their net response to the climate change in Central India

The monthly rainfall data from 1901 to 2011 and maximum and minimum temperature data from 1901 to 2005 are used along with the reference evapotranspiration (ET₀) to analyze the climate trend of 45 stations of Madhya Pradesh. ET₀ is calculated by the Hargreaves method from 1901 to 2005 and the computed data is then used for trend analysis. The temporal variation and the spatial distribution of trend are studied for seasonal and annual series with the Mann-Kendall (MK) test and Sen’s estimator of slope. The percentage of change is used to find the rate of change in 111 years (rainfall) and 105 years (temperatures and ET₀). Interrelationships among these variables are analyzed to see the dependency of one variable on the other. The results indicate a decreasing rainfall and increasing temperatures and ET₀ trend. A similar pattern is noticeable in all seasons except for monsoon season in temperature and ET₀ trend analysis. The highest increase of temperature is noticed during post-monsoon and winter. Rainfall shows a notable decrease in the monsoon season. The entire state of Madhya Pradesh is considered as a single unit, and the calculation of overall net change in the amount of the rainfall, temperatures (maximum and minimum) and ET₀ is done to estimate the total loss or gain in monthly, seasonal and annual series. The results show net loss or deficit in the amount of rainfall and the net gain or excess in the temperature and ET₀ amount.     

A stochastic model for the analysis of maximum daily temperature

Accurately predicting precipitation trends is vital in the economic development of a country. Ground observed data from the Nigeria Meteorological Agency (NIMET) was analyzed to study the long-term spatio-temporal trends of rainfall on annual and seasonal scales for 23 stations in Nigeria during a 40-year period spanning from 1974 to 2013. After testing the presence of autocorrelation, Mann–Kendall (modified Mann–Kendall) test was applied to non-autocorrelated (autocorrelated) series to detect the trends in rainfall data. Theil and Sen’s slope estimator test was used to find the magnitude of change over a time period. Pettitt’s test, Standard Normal Homogeneity Test, and Buishand’s test were further used to test the homogeneity of the rainfall series. The results show an increasing trend in annual rainfall; however, only nine stations have a significant increase during the period of study. On the seasonal time scale, a significant increasing trend was observed in the pre- and post-monsoon seasons, while only nine stations show a significant increasing trend in monsoon rainfall and a significant decreasing trend in the winter rainfall over the last 40 years. During the study period, 15.4 and 13.90 % increase were estimated for annual and monsoonal rainfall, respectively. Furthermore, seven stations exhibit changes in mean rainfall while majority of the stations considered (Eighteen stations) exhibit homogeneous trends in annual and seasonal rainfall over the country. The performance of the different tests used in this study was consistent at the verified significance level.     

华北夏季降水异常与南太平洋夏季海表温度变化主要模态的可能联系

利用1951—2006年NCEP/NCAR再分析资料,采用EOF（empirical orthogonal function,经验正交函数）展开方法提取了南太平洋12—2月海表温度变化的主要模态,其第一模态反映了ENSO（El Nino-Southern Oscillation）的信号,周期为3～5a;第二模态与南半球环状模（Southern-Hemi-sphere annual mode,SAM）相联系,周期为准两年。结果表明,南太平洋EOF1时间序列处于正（负）相位时华北地区后期5月多（少）雨,同时注意到南、北太平洋EOF1对中国5月降水的影响区域基本一致。南太平洋EOF2时间序列处于正（负）相位时江南地区的夏季平均降水减少（增多）。去掉EOF1和EOF2的线性趋势后,这种显著相关仍然存在,只是显著相关区域有所缩小。利用南、北太平洋EOF1时间序列作为预报因子,建立回归预报方程,可为预测中国华北地区5月降水提供依据