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1.
The aim of this study was to investigate temporal variation in seasonal and annual rainfall trend over Ranchi district of Jharkhand, India for the period (1901–2014: 113 years). Mean monthly rainfall data series were used to determine the significance and magnitude of the trend using non-parametric Mann–Kendall and Sen’s slope estimator. The analysis showed a significant decreased in rainfall during annual, winter and southwest monsoon rainfall while increased in pre-monsoon and post-monsoon rainfall over the Ranchi district. A positive trend is detected in pre-monsoon and post-monsoon rainfall data series while annual, winter and southwest monsoon rainfall showed a negative trend. The maximum decrease in rainfall was found for monsoon (? 1.348 mm year?1) and minimum (? 0.098 mm year?1) during winter rainfall. The trend of post-monsoon rainfall was found upward (0.068 mm year?1). The positive and negative trends of annual and seasonal rainfall were found statistically non-significant except monsoon rainfall at 5% level of significance. Rainfall variability pattern was calculated using coefficient of variation CV, %. Post-monsoon rainfall showed the maximum value of CV (70.80%), whereas annual rainfall exhibited the minimum value of CV (17.09%), respectively. In general, high variation of CV was found which showed that the entire region is very vulnerable to droughts and floods.  相似文献   

2.
The main objective of this paper is to analyze the spatial variability of rainfall trends using the spatial variability methods of rainfall trend patterns in Iran. The study represents a method on the effectiveness of spatial variability for predicting rainfall trend patterns variations. In rainfall trend analysis and spatial variability methods, seven techniques were used: Mann–Kendall test, Sen’s slope method, geostatistical tools as a global polynomial interpolation and the spatial autocorrelation (Global Moran’s I), high/low clustering (Getis-Ord General G), precipitation concentration index, generate spatial weights matrix tool, and activation functions of semiliner, sigmoid, bipolar sigmoid, and hyperbolic tangent in the artificial neural network technique .For the spatial variability of monthly rainfall trends, trend tests were used in 140 stations of spatial variability of rainfall trends in the 1975–2014 period. We analyzed the long and short scale spatial variability of rainfall series in Iran. Spatial variability distribution of rainfall series was depicted using geostatistical methods (ordinary kriging). Relative nugget effect (RNE) predicted from variograms which showed weak, moderate, and strong spatial variability for seasonal and annual rainfall series. Moreover, the rainfall trends at each station were examined using the trend tests at a significance level of 0.05. The results show that temporal and spatial trend patterns are different in Iran and the monthly rainfall had a downward (decreasing) trend in most stations, and the trend was statistically significant for most of the series (73.5% of the stations demonstrated a decreasing trend with 0.5 significance level). Rainfall downward trends are generally temporal-spatial patterns in Iran. The monthly variations of rainfall decreased significantly throughout eastern and central Iran, but they increased in the west and north of Iran during the studied interval. The variability patterns of monthly rainfall were statistically significant and spatially random. Activation functions in the artificial neural network models, in annual time scale, had spatially dispersed distribution with other clustering patterns. The results of this study confirm that variability of rainfall revealing diverse patterns over Iran should be controlled mainly by trend patterns in the west and north parts and by random and dispersed patterns in the central, southern, and eastern parts.  相似文献   

3.
Using the satellite derived sea surface temperature (SST) data for 1979 (bad monsoon) and 1983 (good monsoon), the SST variability for two contrasting monsoon seasons is studied. The study indicates that large negative anomalies off the Somali and Arabian coasts are associated with good monsoon rainfall over India. The strong monsoonal cooling in these regions can be attributed to strong low level winds and intense upwelling. The reappearance of 27°C isotherm off Somali coast in May/June coincides with the onset of southwest monsoon over India. Further, the influence of zonal anomaly of SST off Somalia Coast (SCZASST) and Central Indian Ocean Zonal Anomaly of SST (CIOZASST) with monsoon rainfall over India is brought out. The former is negatively related to the monsoon rainfall over western and central parts of India, whilst CIOZASST is positively related.  相似文献   

4.
In this study Tropospheric Biennial Oscillation (TBO) and south Asian summer monsoon rainfall are examined in the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFSv2) hindcast. High correlation between the observations and model TBO index suggests that the model is able to capture most of the TBO years. Spatial patterns of rainfall anomalies associated with positive TBO over the south Asian region are better represented in the model as in the observations. However, the model predicted rainfall anomaly patterns associated with negative TBO years are improper and magnitudes are underestimated compared to the observations. It is noted that positive (negative) TBO is associated with La Niña (El Niño) like Sea surface temperature (SST) anomalies in the model. This leads to the fact that model TBO is El Niño-Southern Oscillation (ENSO) driven, while in the observations Indian Ocean Dipole (IOD) also plays a role in the negative TBO phase. Detailed analysis suggests that the negative TBO rainfall anomaly pattern in the model is highly influenced by improper teleconnections allied to IOD. Unlike in the observations, rainfall anomalies over the south Asian region are anti-correlated with IOD index in CFSv2. Further, summer monsoon rainfall over south Asian region is highly correlated with IOD western pole than eastern pole in CFSv2 in contrast to the observations. Altogether, the present study highlights the importance of improving Indian Ocean SST teleconnections to south Asian summer rainfall in the model by enhancing the predictability of TBO. This in turn would improve monsoon rainfall prediction skill of the model.  相似文献   

5.
Monsoon onset over Kerala (India) which occurs every year is a major climatic phenomenon that involves large scale changes in wind, rainfall and sea surface temperature (SST). Over the last 150 years, the date of monsoon onset over Kerala (DMOK) has varied widely, the earliest being 11 May, 1918 and the most delayed being 18 June, 1972. DMOK has a long term (1870–2014) mean of 01 June and standard deviation of 7–8 days. We have studied the inter-annual and decadal time scale variability of DMOK and their relation with SST. We found that SST anomalies of large spatial scale similar to those in El Nino/La Nina are associated with the inter-annual variability in DMOK. Indian Ocean between latitudes \(5^{\circ }\hbox {S}\) and \(20^{\circ }\hbox {N}\) has two episodes of active convection associated with monsoon onset over Kerala (MOK), one around DMOK and the other about six weeks earlier (called pre-monsoon rain peak or bogus monsoon onset) and in between a two week period of suppressed convection occurs over north Indian Ocean. A prominent decadal time scale variability was found in DMOK having large and statistically significant linear correlation with the SST gradient across the equator over Indian and Pacific oceans, the large correlation persisting for several months prior to the MOK. However, no linear trend was seen in DMOK during the long period from 1870 to 2014.  相似文献   

6.
总结以往滑坡预测方法存在的诸多不足,针对滑坡监测位移-时间曲线特点,本文提出了一种基于时间序列的人工蜂群算法（ABC）与支持向量回归机（SVR）相结合的滑坡位移预测方法。以三峡库区白水河滑坡为例,通过对滑坡位移、降雨、库水位等因素的分析,研究影响滑坡位移变化的因素。用时间序列加法模型和移动平均法将滑坡位移分解为趋势项和周期项。以多项式最小二乘法拟合滑坡位移趋势项,用人工蜂群支持向量机模型对滑坡位移周期项进行训练和预测。通过灰色系统关联分析法计算多项因子与滑坡位移周期项之间的关联性。最终的滑坡总位移预测值为周期项预测值与趋势项预测值之和。与BP神经网络、PSO-SVR模型方法相比,该方法在滑坡位移预测中有更高的精度,在防灾减灾工作中有较好的推广应用前景。  相似文献   

7.
Indian monsoon varies in its nature over the geographical regions. Predicting the rainfall not just at the national level, but at the regional level is an important task. In this article, we used a deep neural network, namely, the stacked autoencoder to automatically identify climatic factors that are capable of predicting the rainfall over the homogeneous regions of India. An ensemble regression tree model is used for monsoon prediction using the identified climatic predictors. The proposed model provides forecast of the monsoon at a long lead time which supports the government to implement appropriate policies for the economic growth of the country. The monsoon of the central, north-east, north-west, and south-peninsular India regions are predicted with errors of 4.1%, 5.1%, 5.5%, and 6.4%, respectively. The identified predictors show high skill in predicting the regional monsoon having high variability. The proposed model is observed to be competitive with the state-of-the-art prediction models.  相似文献   

8.
为可靠预测基坑周边地表沉降的发展趋势,提出了一种基于混合蛙跳算法和广义回归神经网络模型的基坑地表最大沉降预测模型（SFLA-GRNN模型）。首先,在沉降机制分析并初选输入变量集的基础上,利用灰色相关度分析对模型输入、输出变量的相关性进行量化,并剔除与输出变量相关性明显偏小的输入变量;其次,利用混合蛙跳算法（SFLA）对广义回归神经网络模型（GRNN）的平滑因子进行优化确定,减少人为因素对模型精度和泛化能力的不良影响;最后,利用筛选得到的输入变量集建立基坑地表最大沉降预测的广义回归神经网络模型。实例应用及对比计算结果表明,基于灰色相关度的输入变量筛选和基于混合蛙跳算法的平滑因子优化均能够有效提高广义回归神经网络模型的精度和泛化能力,以上结论可为类似变形预测提供参考。  相似文献   

9.
Thunderstorms are of much importance in tropics, as this region is considered to have central role in the convective overturn of the atmosphere and play an important role in rainfall activity. It is well known that El Niño and La Niña are well associated with significant climate anomalies at many places around the globe. Therefore, an attempt is made in this study to analyze variability in thunderstorm days and rainfall activity over Indian region and its association with El Niño and La Niña using data of thunderstorm day’s for 64 stations well distributed all over India for the period 1981–2005 (25 years). It is seen that thunderstorm activity is higher and much variable during pre-monsoon (MAM) and southwest monsoon (JJAS) than the rest of the year. Positive correlation coefficients (CCs) are seen between thunderstorms and rainfall except for the month of June during which the onset of the southwest monsoon sets over the country. CCs during winter months are highly correlated. Composite anomalies in thunderstorms during El Niño and La Niña years suggest that ENSO conditions altered the patterns of thunderstorm activity over the country. Positive anomalies are seen during pre-monsoon (MAM) and southwest monsoon months (JAS) during La Niña years. Opposite features are seen in southwest monsoon during El Niño periods, but El Niño favors thunderstorm activity during pre-monsoon months. There is a clear contrast between the role of ENSO during southwest monsoon and post-monsoon on thunderstorm activity over the country. Time series of thunderstorms and precipitation show strong association with similarities in their year-to-year variation over the country.  相似文献   

10.
In this study, an analysis of century scale climate trends in the central highlands of Sri Lanka is presented. Monthly rainfall and temperature records of the period 1869–2006 from five climatological stations were analyzed. The trend is calculated by the least square regression analysis and the significance of the observed trend is estimated using the Mann–Kendall statistic. The results clearly show that there is a statistically significant decrease in annual rainfall in the western slopes of the central highlands. Throughout the last century, the annual reduction of rainfall in Nuwara Eliya which is at an altitude of 1895 m was 5.2 mm/year. The decrease is largely due to the reduction in southwest monsoon rainfall which contributes to 75% of the total reduction. No significant change was observed on the eastern side of the central highlands which receives rainfall predominantly from the northeast monsoons. The mean annual temperature in the mountainous region shows a uniform increasing trend which is in line with the 100-year global temperature increase of 0.8 ± 0.2°C. Kandy, which is at an altitude of 477 m and closely linked with the rainfall climatology of Nuwara Eliya, showed no significant change in the mean annual temperature. If the current trend continues, in another 100 years, western and eastern slopes of central highlands will receive the same amount of rainfall from the southwest monsoon and the northeast monsoon which will have far reaching consequences for Sri Lanka’s economy and the ecology of the hill country.  相似文献   

11.
Skilful prediction of the monthly and seasonal summer monsoon rainfall over India at a smaller spatial scale is a major challenge for the scientific community. The present study is aimed at achieving this objective by hybridising two mathematical techniques, namely synthetic superensemble (SSE) and supervised principal component regression (SPCR) on six state-of-the art Global Climate Models (GCMs). The performance of the mathematical model is evaluated using correlation analysis, the root mean square error, and the Nash–Sutcliffe efficiency index. Results feature reasonable improvement over central India, which is a zone of maximum rainfall activity in the summer monsoon season. The study also highlights improvement in the monthly prediction of rainfall over raw GCMs (15–20% improvement) with exceptional improvement in July. The developed model is also examined for anomalous years of monsoon and it is found that the model is able to capture the signs of anomalies over different gridpoints of the Indian domain.  相似文献   

12.
The present article reports studies to develop a univariate model to forecast the summer monsoon (June–August) rainfall over India. Based on the data pertaining to the period 1871–1999, the trend and stationarity within the time series have been investigated. After revealing the randomness and non-stationarity within the time series, the autoregressive integrated moving average (ARIMA) models have been attempted and the ARIMA(0,1,1) has been identified as a suitable representative model. Consequently, an autoregressive neural network (ARNN) model has been attempted and the neural network has been trained as a multilayer perceptron with the extensive variable selection procedure. Sigmoid non-linearity has been used while training the network. Finally, a three-three-one architecture of the ARNN model has been obtained and after thorough statistical analysis the supremacy of ARNN has been established over ARIMA(0,1,1). The usefulness of ARIMA(0,1,1) has also been described.  相似文献   

13.
Long range prediction of Indian summer monsoon rainfall   总被引：3，自引：0，他引：3  
The search for new parameters for predicting the all India summer monsoon rainfall (AISMR) has been an important aspect of long range prediction of AISMR. In recent years NCEP/NCAR reanalysis has improved the geographical coverage and availability of the data and this can be easily updated. In this study using NCEP/NCAR reanalysis data on temperature, zonal and meridional wind at different pressure levels, few predictors are identified and a prediction scheme is developed for predicting AISMR. The regression coefficients are computed by stepwise multiple regression procedure. The final equation explained 87% of the variance with multiple correlation coefficient (MCC), 0.934. The estimated rainfall in the El-Niño year of 1997 was ?1.7% as against actual of 4.4%. The estimated rainfall deficiency in both the recent deficient years of 2002 and 2004 were ?19.5% and ?8.5% as against observed ?20.4% and ?11.5% respectively.  相似文献   

14.
INTERACTION BETWEEN THE ENSO AND ASIAN MONSOON RECORDED IN DASUOPU ICE CORE FROM HIMALAYAS  相似文献   

15.
The predictability of Indian summer monsoon rainfall from pre-season circulation indices is explored from observations during 1939–91. The predictand is the all-India average of June–September precipitation NIR, and the precursors examined are the latitude position of the 500 mb ridge along 75°E in April (L), the pressure tendency April minus January at Darwin (DPT), March-April-May temperature at six stations in west central India (T6), the sea surface temperature (SST) anomaly in the northeastern Arabian Sea in May (ASM), SST anomaly in the Arabian Sea in January (ANJ), northern hemisphere temperature anomaly in January–February (NHT), and Eurasian snow cover in January (SNOW). Monsoon rainfall tends to be enhanced with a more northerly ridge position, small Darwin pressure tendency, warmer pre-season conditions, and reduced winter snow cover. However, relationships have varied considerably over the past half-century, with the strongest associations during 1950–80, and a drastic weakening in the 1980s. Four prediction models were constructed based on stepwise multiple regression, using as predictors combinations of L, DPT, T6, ASM, and NHT, with 1939–68 as “dependent” dataset, or training period, and 1969–91 as “independent” dataset or verification period. For the 1969–80 portion of the verification period calculated and observed NIR values agreed closely, with the models explaining 74–79% of the variance. By contrast, after 1980 predictions deteriorated drastically, with the explained variance for the 1969–89 time span dropping to 25–31%. The monsoon rainfall of 1990 and 1991 turned out to be again highly predictable from models based on stepwise multiple regression and linear discriminant analysis and using as input L + DPT or L + DPT + NHT, and with this encouragement an experimental real-time forecast was issued of the 1992 monsoon rainfall. These results underline the need for investigations into decadal-scale changes in the general circulation setting and raise concern for the continued success of seasonal forecasting.  相似文献   

16.
周雨婷 《水文》2020,40(1):35-39
为提高多种典型人工神经网络应用于降水预报的精度与稳定性并做出优选,对太湖流域湖西区丹徒、丹阳、金坛、溧阳、宜兴5站的年降水量时间序列建立基于组成成分分析的人工神经网络模型,并通过平均相对误差、平均绝对误差、均方根误差及合格率4项评价指标对比分析预报效果。该模型采用Mann-Kendall法、秩和检验法、谱分析法进行组成成分分析;建立BP网络、小波神经网络、RBF网络、GRNN网络及Elman网络模拟并预测随机成分,与确定性成分叠加得年降水量预报结果。在湖西区的研究结果表明,基于组成成分分析的人工神经网络模型的拟合及预测精度高于原始人工神经网络和线性自回归模型,GRNN网络的预测精度与稳定性高于其他4类神经网络。  相似文献   

17.
根据山西宁武煤田某煤矿2#煤层采掘及钻孔揭露情况,发现该井田中部和南部存在古河流冲刷带。通过对比基于优选的4个地震属性的四元一次、四元二次多项式回归模型与BP神经网络预测模型,决定将BP人工神经网络模型预测煤层厚度数据应用于整个测区古河流冲刷带的预判工作。首先利用GeoFrame系统和Landmark公司Poststack模块,提取2#煤层反射波的各类沿层切片,分析并圈定出2#煤层古河流冲刷带的大致范围,在此基础上,利用垂直时间剖面中2#煤层反射波的各种波形特征,进一步判别2#煤层古河流冲刷带解释的可靠性,然后结合BP神经网络预测模型获得的2#煤层厚度变化趋势图,最终解释出2#煤层古河流冲刷带范围:勘探区内2#煤层厚度变化范围0～5.3m,根据其煤层厚度变化趋势,将全区划分出一大二小3个古河流冲刷带。  相似文献   

18.
An analysis of the mean monthly data of 124 years reveals that the relationship between the Southern Oscillation Index in September and the winter monsoon rainfall (WMR) over Coastal Andhra Pradesh (CAP) is variable and non-stationary. In the recent four decades, however, SOI (Sept) is negatively and significantly correlated with CAP WMR. A similar analysis is performed using 50 years of mean monthly SSTs over Nino-3.4 region in August and September and CAP WMR to detect a possible relationship and there is a striking positive relation between them. In both of the above cases, the September signal is more significant in the recent four decades than for the other months and seasons for probable prediction of CAP WMR. Finally, to examine the influence of SO on the winter monsoon rainfall, a non-parametric test “Mann-Whitney Rank Statistics” test has been applied to the rainfall associated with extreme positive and negative SOI events  相似文献   

19.
青海省冬季气温变化成因及其预测方法探讨   总被引：1，自引：1，他引：0  
利用青海省1961-2012年冬季气温观测资料、美国环境预报中心(NCEP)和国家大气研究中心(NCAR)月平均高度场再分析资料、国家气候中心和美国国家海洋局和大气管理局提供的126项环流指数, 探讨青海冬季气温变化特征及成因. 结果表明: 1961-2012年青海冬季气温呈显著上升趋势并具明显的年代际变化特征, 于1986年出现由冷向暖的明显转折; 西伯利亚高压、东亚冬季风是影响青海冬季气温的主要系统. 当冬季北半球500 hPa高度场出现欧亚(EU)遥相关型时, 青海冬季易于偏冷, 同时发现大西洋欧洲区极涡强度和赤道太平洋海域海温与东亚冬季风的强弱有密切关系. 采用主成分回归集成方法初步建立青海冬季气温预测模型, 经历史回报检验其距平符号一致率为87%, 具备一定预报技巧和能力.  相似文献   

20.
Indian summer monsoon is a global scale phenomenon controlled by different land, ocean, and atmospheric parameters. Sea surface temperature (SST) and snow are two of the major parameters, which may alter the spatial and temporal patterns of circulation and rainfall during Indian summer monsoon. In the current paper, we study the monsoon variability using long integrations (20 years) of the Indian Institute of Technology Delhi (IITD) Spectral model at T80L18 resolution with observed and climatological SST and snow. Study shows response of IITD GCM in simulating the Indian summer monsoon rainfall and circulation relative to the snow and SST as boundary conditions. The model’s response to SST and snow is examined by conducting four types of experiments by varying observed and climatological values of snow and SST. This paper discusses the seasonal total rainfall for country as a whole and 850 and 200 hPa wind for the period of 20 years starting from 1985 to 2004. The model has been integrated in the ensemble mode with five different initial conditions from the last week of April and first week of May. The model is able to capture the climatological patterns of seasonal total rainfall and averaged wind at lower and upper levels. Observed snow in the presence of climatological SST as a boundary condition shows much impact on rainfall and circulation than observed SST in the presence of climatological snow. Model performance is good in simulating the normal and excess monsoon conditions; it shows poor skill in capturing deficit monsoon years.  相似文献   

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