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1.

The Indian landmass has been divided into homogeneous clusters by applying the cluster analysis to the probability density function of a century-long time series of daily summer monsoon (June through September) rainfall at 357 grids over India, each of approximately 100 km × 100 km. The analysis gives five clusters over Indian landmass; only cluster 5 happened to be the contiguous region and all other clusters are dispersed away which confirms the erratic behavior of daily rainfall over India. The area averaged seasonal rainfall over cluster 5 has a very strong relationship with Indian summer monsoon rainfall; also, the rainfall variability over this region is modulated by the most important mode of climate system, i.e., El Nino Southern Oscillation (ENSO). This cluster could be considered as the representative of the entire Indian landmass to examine monsoon variability. The two-sample Kolmogorov-Smirnov test supports that the cumulative distribution functions of daily rainfall over cluster 5 and India as a whole do not differ significantly. The clustering algorithm is also applied to two time epochs 1901–1975 and 1976–2010 to examine the possible changes in clusters in a recent warming period. The clusters are drastically different in two time periods. They are more dispersed in recent period implying the more erroneous distribution of daily rainfall in recent period.

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2.
Monthly rainfall extremes have been analyzed for three stations in Southern Ontario. The double exponential probability distribution was fitted to the extreme values for each month considered, each duration selected, and sets of annual extremes. A station‐year approach yielded monthly and annual extreme value distributions for the lumped region of Southern Ontario. The analysis has revealed a pronounced seasonal pattern in the rainfall extremes – the amount of rain expected with a selected probability of occurrence during the summer being considerably greater than the rainfall that might be expected to be exceeded at the same probability level during the spring or fall. The extent of the seasonal variability was found also to vary with duration. The implications of the variability are seen to be significant for the estimation of the magnitude and frequency of floods.  相似文献   

3.
Summary The paper deals with the variability of summer-monsoon rainfall during normal, flood and drought years over India. During flood years the monsoon rainfall increases mostly all over parts of the country and large area less than 100 cm isohytel covers Orissa and adjoining Madhya Pradesh. During drought years the rainfall amount decreases over the entire country and isohytel of 100 cm shrinks to almost a point. The variability of monsoon rainfall from flood to normal to drought years depends upon the number of depression/low-pressure area which form over the North Bay and move inland. To understand the intraseasonal and interannual variability of the monsoon rainfall, daily and seasonal anomalies has been performed by using the Empirical Orthogonal Function analysis. Further Empirical Orthogonal Function (EOF) analysis is carried out on these data to find out the nature of rainfall distribution in different monsoon categories namely normal, flood and drought years. This technique thus serves to identify spatial and temporal patterns characteristics of possible physical significance. Received July 25, 2000/Revised September 26, 2000  相似文献   

4.
W. May 《Climate Dynamics》2004,22(2-3):183-204
In this study the simulation of the variability and extremes of daily rainfall during the Indian summer monsoon for the present-day and the future climate is investigated. This is done on the basis of a global time-slice experiment (TSL) with the ECHAM4 atmospheric general circulation model (GCM) at a high horizontal resolution of T106. The first time-slice (period: 1970–1999) represents the present-day climate and the second (2060–2089) the future climate. Moreover, observational rainfall data from the Global Precipitation Climatology Project (GPCP, 1997–2002) and rainfall data from the ECMWF re-analysis (ERA, 1958–2001) are considered. ERA reveals serious deficiencies in its representation of the variability and extremes of daily rainfall during the Indian summer monsoon. These are mainly a severe overestimation of the frequency of wet days over the oceans and in the Himalayas, where also the rainfall intensity is overestimated. Further, ERA shows unrealistically heavy rainfall events over the tropical Indian Ocean. The ECHAM4 atmospheric GCM at a horizontal resolution of T106, on the other hand, simulates the variability and extremes of daily rainfall in good agreement with the observations. The only marked deficiencies are an underestimation of the rainfall intensity on the west coast of the Indian peninsula and in Bangladesh, an overestimation over the tropical Indian Ocean, due to an erroneous northwestward extension of the tropical convergence zone, and an overestimation of the frequency of wet days in Tibet. Further, heavy rainfall events are relatively strong in the centre of the Indian peninsula. For the future, TSL predicts large increases in the rainfall intensity over the tropical Indian Ocean as well as in northern Pakistan and northwest India, but decreases in southern Pakistan, in the centre of the Indian peninsula, and over the western part of the Bay of Bengal. The frequency of wet days is markedly increased over the tropical Indian Ocean and decreased over the northern part of the Arabian Sea and in Tibet. The intensity of heavy rainfall events is generally increased in the future, with large increases over the Arabian Sea and the tropical Indian Ocean, in northern Pakistan and northwest India as well as in northeast India, Bangladesh, and Myanmar.  相似文献   

5.
In many regions of the world, planning agricultural and water management activities is usually done based on probabilities for monthly rainfall, taking on values on specified intervals of values. These intervals of monthly rainfall amounts are commonly grouped into three categories: drought, normal rainfall, and abundant rainfall. Changes in the probabilities for occurrence of monthly rainfall amounts within these climatic rainfall categories will influence the decisions farmers and water managers will take (for example, crops to cultivate, flood preparedness, and operations of water reservoirs). This research explores the changes produced by the SO (Southern Oscillation) on the probability that the areal average of monthly rainfall (AAvMR) takes on values belonging to specified climatic rainfall categories. The semi-arid region under study is a major agricultural region in central Argentina; weather effects on agriculture in this region influence the world market of several crops. The evolution of the Southern Oscillation was divided into three phases: LSOI (low Southern Oscillation index phase, that includes ENSO events), NSOI (neutral SOI phase), and HSOI (high SOI phase that includes La Niña–SO events). The following are the criteria defining the three phases of the SO: (1) low SOI (ENSO), where the five-month moving average of the SO index, SOI, is less than −0.5 standard deviation during at least five consecutive months, and is equal to or less than −1 standard deviation during at least one month; (2) high SOI (La Niña–SO), where the SOI is greater than 0.5 standard deviation during at least five consecutive months, and is equal to or greater than 1 standard deviation during at least one month; and (3) neutral SOI (transition between extremes), where the SOI does not correspond to low SOI nor to high SOI. It was found that the Southern Oscillation influences the probability distribution of monthly rainfall only in four months of the year. Findings show that monthly rainfall has a complex response to the evolution of the SO. The response is not restricted to higher probability for occurrence of abundant rainfall or drought categories during low SOI (ENSO) or high SOI (La Niña–SO) episodes, respectively. The LSOI (ENSO) phase influences the AAvMR in several ways: depending on the month, it increases or decreases the probability of the abundant rainfall category. LSOI (ENSO) also increases or decreases, depending on the month, the probability of the normal rainfall category. It also decreases the probability that AAvMR takes on values in the drought category. A similar kind of complex response of monthly rainfall amounts occurs when the active phase is the HSOI (La Niña–SO). The responses are: (1) the probability of the category `drought' increases only in three months of the year, (2) increase or decrease of the probability of the normal rainfall category, depending on the month, and (3) decrease of the probability of the abundant rainfall category. Finally, the effects of NSOI (neutral phase of the SO) are not negligible. Depending on the month, NSOI episodes increase or decrease the probability of drought, or abundant rainfall, or normal rainfall categories.  相似文献   

6.
Summary Indian monsoon rainfall data is shown to be decomposable into six empirical time series, called intrinsic mode functions. This helps one to identify the first empirical mode as a nonlinear part and the remaining as the linear part of the data. The nonlinear part is handled by artificial neural network (ANN) techniques, whereas the linear part is amenable for modeling through simple regression concepts. It is found that the proposed model explains between 75 to 80% of the interannual variability (IAV) of eight regional rainfall series considered here. The model is efficient in statistical forecasting of rainfall as verified on an independent subset of the data series. It is demonstrated that the model is capable of foreshadowing the drought of 2002, with the help of only antecedent data. The statistical forecast of All India rainfall for the year of 2004 is 80.34 cms with a standard deviation of 3.3 cms. This expected value is 94.25% of the longterm climatic average.  相似文献   

7.
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8.
Seasonal rainfall amounts, directly responsible for availability of water resources on a specified area, are strongly dependent on the climate system. In order to highlight some features of such dependence, generally circulation indexes based on the difference in the sea level pressure between two geographic areas are taken into account. In the present study, the relationships between winter rainfall series observed in the Calabria region (southern Italy) and the North Atlantic Oscillation Index (NAOI) have been analysed. Firstly, a correlation analysis between precipitation and the NAOI was performed. Subsequently, the influence of the different phases of the NAO on the winter precipitation has been detected by a composite analysis, and by identifying changes in the behaviour of the probability density functions (gamma distribution) fitted on monthly rainfall. The results evidence a clear link existing between the phases of the climatic index and the amount of winter rainfall.  相似文献   

9.
Summary The interannual and decadal scale variability in the North Atlantic Oscillation (NAO) and its relationship with Indian Summer monsoon rainfall has been investigated using 108 years (1881–1988) of data. The analysis is carried out for two homogeneous regions in India, (Peninsular India and Northwest India) and the whole of India. The analysis reveals that the NAO of the preceding year in January has a statistically significant inverse relationship with the summer monsoon rainfall for the whole of India and Peninsular India, but not with the rainfall of Northwest India. The decadal scale analysis reveals that the NAO during winter (December–January–February) and spring (March–April–May) has a statistically significant inverse relationship with the summer monsoon rainfall of Northwest India, Peninsular India and the whole of India. The highest correlation is observed with the winter NAO. The NAO and Northwest India rainfall relationship is stronger than that for the Peninsular and whole of India rainfall on climatological and sub-climatological scales.Trend analysis of summer monsoon rainfall over the three regions has also been carried out. From the early 1930s the Peninsular India and whole of India rainfall show a significant decreasing trend (1% level) whereas the Northwest India rainfall shows an increasing trend from 1896 onwards.Interestingly, the NAO on both climatological and subclimatological scales during winter, reveals periods of trends very similar to that of Northwest Indian summer monsoon rainfall but with opposite phases.The decadal scale variability in ridge position at 500 hPa over India in April at 75° E (an important parameter used for the long-range forecast of monsoon) and NAO is also investigated.With 4 Figures  相似文献   

10.
The main goal of this study is to determine the oceanic regions corresponding to variability in African rainfall and seasonal differences in the atmospheric teleconnections. Canonical correlation analysis (CCA) has been applied in order to extract the dominant patterns of linear covariability. An ensemble of six simulations with the global atmospheric general circulation model ECHAM4, forced with observed sea surface temperatures (SSTs) and sea ice boundary variability, is used in order to focus on the SST-related part of African rainfall variability. Our main finding is that the boreal summer rainfall (June–September mean) over Africa is more affected by SST changes than in boreal winter (December–March mean). In winter, there is a highly significant link between tropical African rainfall and Indian Ocean and eastern tropical Pacific SST anomalies, which is closely related to El Niño-Southern Oscillation (ENSO). However, long-term changes are found to be associated with SST changes in the Indian and tropical Atlantic Oceans, thus, showing that the tropical Atlantic plays a critical role in determining the position of the intertropical convergence zone (ITCZ). Since ENSO is less in summer, the tropical Pacific and the Indian Oceans are less important for African rainfall. The African summer monsoon is strongly influenced by SST variations in the Gulf of Guinea, with a response of opposite sign over the Sahelian zone and the Guinean coast region. SST changes in the subtropical and extratropical oceans mostly take place on decadal time scales and are responsible for low-frequency rainfall fluctuations over West Africa. The modelled teleconnections are highly consistent with the observations. The agreement for most of the teleconnection patterns is remarkable and suggests that the modelled rainfall anomalies serve as suitable predictors for the observed changes.  相似文献   

11.
Continuous periodogram analyses of 115 years (1871-1985) summer monsoon rainfall over the Indian region show that the power spectra follow the universal and unique inverse power law form of the statistical normal distribution with the percentage contribution to total variance representing the eddy probability corresponding to the normalized standard deviation equal to [(log L/log T50) – 1] where L is the period length in years and T50 the period up to which the cumulative percentage contribution to total variance is equal to 50. The above results are con-sistent with a recently developed non-deterministic cell dynamical model for atmospheric flows. The implications of the above result for prediction of interannual variability of rainfall is discussed.  相似文献   

12.
A new approach to ensemble forecasting of rainfall over India based on daily outputs of four operational numerical weather prediction (NWP) models in the medium-range timescale (up to 5 days) is proposed in this study. Four global models, namely ECMWF, JMA, GFS and UKMO available on real-time basis at India Meteorological Department, New Delhi, are used simultaneously with adequate weights to obtain a multi-model ensemble (MME) technique. In this technique, weights for each NWP model at each grid point are assigned on the basis of unbiased mean absolute error between the bias-corrected forecast and observed rainfall time series of 366 daily data of 3 consecutive southwest monsoon periods (JJAS) of 2008, 2009 and 2010. Apart from MME, a simple ensemble mean (ENSM) forecast is also generated and experimented. The prediction skill of MME is examined against observed and corresponding outputs of each constituent model during monsoon 2011. The inter-comparison reveals that MME is able to provide more realistic forecast of rainfall over Indian monsoon region by taking the strength of each constituent model. It has been further found that the weighted MME technique has higher skill in predicting daily rainfall compared to ENSM and individual member models. RMSE is found to be lowest in MME forecasts both in magnitude and area coverage. This indicates that fluctuations of day-to-day errors are relatively less in the MME forecast. The inter-comparison of domain-averaged skill scores for different rainfall thresholds further clearly demonstrates that the MME algorithm improves slightly above the ENSM and member models.  相似文献   

13.
The present study attempts to formulate a regression model to predict summer rainfall over Peninsular India (PIR) using some regional predictors. Parameters having significant correlation (99%) with PIR were identified for the period 1975–1997 (training), and a 15-year sliding correlation (90%) was found to check the consistency of the relationship between PIR and predictors. From a set of 14 candidate predictors, 4 were selected using a stepwise regression method and tested over a period from 1998 to 2006. The predictors selected are sea surface temperature during March over Indian Ocean, air temperature at 850?hPa during May over Peninsular India, zonal, and meridional wind at 700?hPa during February and January, respectively, over the Arabian Sea. The model captures a variance of 77.7% and has a multiple correlation of 0.88. The root mean square error, absolute mean error, and bias for the training (test) period were 7.6% (21.5%), 6.6% (17.9%), and 0% (11.4%) of mean rainfall, respectively. Results of the climatological predictions show that the model developed is useful.  相似文献   

14.
Agriculture in India is highly sensitive to climatic variations particularly to rainfall and temperature; therefore, any change in rainfall and temperature will influence crop yields. An understanding of the spatial and temporal distribution and changing patterns in climatic variables is important for planning and management of natural resources. Time series analysis of climate data can be a very valuable tool to investigate its variability pattern and, maybe, even to predict short- and long-term changes in the series. In this study, the sub-divisional rainfall data of India during the period 1871 to 2016 has been investigated. One of the widely used powerful nonparametric techniques namely wavelet analysis was used to decompose and de-noise the series into time–frequency component in order to study the local as well as global variation over different scales and time epochs. On the decomposed series, autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models were applied and by means of inverse wavelet transform, the prediction of rainfall for different sub-divisions was obtained. To this end, empirical comparison was carried out toward forecast performance of the approaches namely Wavelet-ANN, Wavelet-ARIMA, and ARIMA. It is reported that Wavelet-ANN and Wavelet-ARIMA approach outperforms the usual ARIMA model for forecasting of rainfall for the data under consideration.  相似文献   

15.
In the present study the Principal Component Analysis (PCA) is used to determine the dominant rainfall patterns from rainfall records over India. Pattern characteristics of seasonal monsoon rainfall (June–September) over India for the period 1940 to 1990 are studied for 68 stations. The stations have been chosen on the basis of their correlation with all India seasonal rainfall after taking the ‘t’ Student distribution test (5% level). The PCA is carried out on the rainfall data to find out the nature of rainfall distribution and percentage of variance is estimated. The first principal component explains 55.50% of the variance and exhibits factor of one positive value throughout the Indian subcontinent. It is characterized by an area of large positive variation between 10°N and 20°N extending through west coast of India. These types of patterns mostly occur due to the monsoon depression in the head Bay of Bengal and mid-tropospheric low over west coast of India. The analysis identifies the spatial and temporal characteristics of possible physical significance. The first eight principal component patterns explain for 96.70% of the total variance.  相似文献   

16.
Summary Annual precipitation over Serbia and Montenegro is studied in terms of its variability. The dependence of three selected absolute measures of variability (standard deviation, absolute mean deviation and mean absolute interannual variability) from the mean annual precipitation are examined for the area of interest. Two cases of extreme precipitation in Serbia were analysed using the gamma probability density function and some transformations.  相似文献   

17.
Probabilistic seasonal predictions of rainfall that incorporate proper uncertainties are essential for climate risk management. In this study, three different multi-model ensemble (MME) approaches are used to generate probabilistic seasonal hindcasts of the Indian summer monsoon rainfall based on a set of eight global climate models for the 1982–2009 period. The three MME approaches differ in their calculation of spread of the forecast distribution, treated as a Gaussian, while all three use the simple multi-model subdivision average to define the mean of the forecast distribution. The first two approaches use the within-ensemble spread and error residuals of ensemble mean hindcasts, respectively, to compute the variance of the forecast distribution. The third approach makes use of the correlation between the ensemble mean hindcasts and the observations to define the spread using a signal-to-noise ratio. Hindcasts are verified against high-resolution gridded rainfall data from India Meteorological Department in terms of meteorological subdivision spatial averages. The use of correlation for calculating the spread provides better skill than the other two methods in terms of rank probability skill score. In order to further improve the skill, an additional method has been used to generate multi-model probabilistic predictions based on simple averaging of tercile category probabilities from individual models. It is also noted that when such a method is used, skill of probabilistic forecasts is improved as compared with using the multi-model ensemble mean to define the mean of the forecast distribution and then probabilities are estimated. However, skill of the probabilistic predictions of the Indian monsoon rainfall is too low.  相似文献   

18.
Summary The summer monsoon rainfall over Orissa, a state of eastern India, shows characteristic intraseasonal and interannual variability, due to interaction of basic westerly flow with orography and the synoptic scale monsoon disturbances including low-pressure systems and cyclonic circulations extending upto mid-tropospheric level (LPSC). These systems normally develop over the north Bay of Bengal and move west-northwestwards along the monsoon trough. The essence of this study is to find out the main features of the intraseasonal variability of daily monsoon rainfall over Orissa in relation to synoptic systems like LPSC and its implication on the interannual variation of rainfall. For this purpose, the actual and mean daily rainfall data of 31 uniformly distributed stations, six homogeneous regions and Orissa as a whole during monsoon season (June–September) over a period of 20 years (1980–1999) are subjected to auto-correlation and power spectrum analyses. The actual and average daily scores of significant EOFs and actual daily occurrence along with daily probability of occurrence of the LPSC influencing rainfall over Orissa during the same period are also subjected to auto-correlation and power spectrum analyses. The intraseasonal variation of monsoon rainfall over Orissa and different homogeneous regions is dominated by the synoptic mode (3–9 days) of variation due to the similar mode of variation in the occurrence of LPSC influencing the rainfall. The seasonal rainfall and hence the interannual variation depends on the intraseasonal variation of rainfall modulated with the synoptic mode of variation in the occurrence of the LPSC. The occurrence of LPSC over the northwest (NW) Bay/NW and adjoining northeast (NE) Bay and its subsequent movement and persistence over Orissa and east Madhya Pradesh & Chhattisgarh in synoptic mode (3–6 days) alongwith absence of similar mode in the occurrence of the LPSC over NE Bay, Gangetic West Bengal (GWB) in the north and west central (WC) Bay to the south leads to excess rainfall over different homogeneous regions and Orissa as a whole. The reverse is the case in deficient years over Orissa and all homogeneous regions except southwest Orissa. The occurrence of the LPSC over GWB in synoptic mode (about 5 days) alongwith absence of synoptic mode in the occurrence of the LPSC over NW Bay leads to deficient rainfall year over southwest Orissa. Correspondence: U. C. Mohanty, Centre for Atmospheric Sciences, Indian Institute of Technology, Delhi Hauz Khas, New Delhi 110016, India  相似文献   

19.
The summer monsoon rainfall over India exhibits strong intraseasonal variability. Earlier studies have identified Madden Julian Oscillation (MJO) as one of the most influencing factors of the intraseasonal variability of the monsoon rainfall. In this study, using India Meteorological Department (IMD) high resolution daily gridded rainfall data and Wheeler?CHendon MJO indices, the intra-seasonal variation of daily rainfall distribution over India associated with various Phases of eastward propagating MJO life cycle was examined to understand the mechanism linking the MJO to the intraseasonal variability. During MJO Phases of 1 and 2, formation of MJO associated positive convective anomaly over the equatorial Indian Ocean activated the oceanic tropical convergence zone (OTCZ) and the resultant changes in the monsoon circulation caused break monsoon type rainfall distribution. Associated with this, negative convective anomalies over monsoon trough zone region extended eastwards to date line indicating weaker than normal northern hemisphere inter tropical convergence zone (ITCZ). The positive convective anomalies over OTCZ and negative convective anomalies over ITCZ formed a dipole like pattern. Subsequently, as the MJO propagated eastwards to west equatorial Pacific through the maritime continent, a gradual northward shift of the OTCZ was observed and negative convective anomalies started appearing over equatorial Indian Ocean. During Phase 4, while the eastwards propagating MJO linked positive convective anomalies activated the eastern part of the ITCZ, the northward propagating OTCZ merged with monsoon trough (western part of the ITCZ) and induced positive convective anomalies over the region. During Phases 5 and 6, the dipole pattern in convective anomalies was reversed compared to that during Phases 1 and 2. This resulted active monsoon type rainfall distribution over India. During the subsequent Phases (7 and 8), the convective and lower tropospheric anomaly patterns were very similar to that during Phase 1 and 2 except for above normal convective anomalies over equatorial Indian Ocean. A general decrease in the rainfall was also observed over most parts of the country. The associated dry conditions extended up to northwest Pacific. Thus the impact of the MJO on the monsoon was not limited to the Indian region. The impact was rather felt over larger spatial scale extending up to Pacific. This study also revealed that the onset of break and active events over India and the duration of these events are strongly related to the Phase and strength of the MJO. The break events were relatively better associated with the strong MJO Phases than the active events. About 83% of the break events were found to be set in during the Phases 7, 8, 1 and 2 of MJO with maximum during Phase 1 (40%). On the other hand, about 70% of the active events were set in during the MJO Phases of 3 to 6 with maximum during Phase 4 (21%). The results of this study indicate an opportunity for using the real time information and skillful prediction of MJO Phases for the prediction of break and active conditions which are very crucial for agriculture decisions.  相似文献   

20.
华东地区夏季不同等级降水变化特征分析   总被引：10，自引：3，他引：7  
白静漪  管兆勇 《气象科学》2014,34(4):365-372
采用华东地区78个气象站点逐日降水资料,根据日降水量的5个等级划分,应用线性趋势分析、相关分析等分析了不同等级降水频率和降水量的空间分布及其变化趋势。结果表明:(1)夏季不同等级降水频率在整个华东地区具有明显的地区差异,区域平均的降水频率由大到小依次为小雨、微量降水、中雨、大雨、暴雨。(2)平均的夏季总降水量呈南多北少的分布,各等级降水对总降水量的贡献率由大到小依次为暴雨、大雨、中雨、小雨,暴雨对夏季总降水量的贡献在某些年份可达50%以上。(3)区域平均的夏季降水日数呈下降趋势,但总降水量却有明显的增大趋势。(4)区域平均的某等级降水频率正异常时,华东地区各地该等级降水频率,亦多表现为正异常,尤其中雨以上等级降水频率异常符号在整个华东地区更为一致。(5)华东区域微量降水和小雨发生频率分别与其他等级降水存在显著的反相关关系,而中雨、大雨、暴雨三者发生频率之间无显著相关。  相似文献   

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