Homogeneous clusters over India using probability density function of daily rainfall

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.

     

Summer monsoon rainfall patterns over South Korea and associated circulation features

Summary Seasonal summer monsoon (June through August) rainfall patterns over South Korea are classified by an objective method using data for a 40-year period (1961–2000). The rainfall patterns are represented by the percentage departures from the normal rainfall of 12 stations spread uniformly over South Korea. The statistical technique employed is the k-means (KM) clustering method. The Euclidean distance has been used as a measure of similarity between the patterns. Four dominant types are obtained by this method. Intercorrelations among the types suggest that the dominant patterns are distinct. The summer monsoon rainfall shows an increasing trend. Investigation of the physical processes associated with these patterns using NCEP/NCAR Reanalysis data clearly reveals contrasting circulation features associated with the dominant types during the summer monsoon period. In particular, contrasting circulation features are related to the position, shape and strength of the North Pacific Subtropical High. Received October 30, 2000 Revised November 12, 2001     

The Relationship Between some Large-scale Atmospheric Parameters and Rainfall over Southeast Asia: A Comparison with Features over India

Summary Monthly rainfall data for 135 stations for periods varying from 25 to 125 years are utilised to investigate the rainfall climatology over the southeast Asian monsoon regime. Monthly rainfall patterns for the regions north of equator show that maximum rainfall along the west coasts occurs during the summer monsoon period, while the maximum along the east coasts is observed during the northeast monsoon period. Over the Indonesian region (south of the equator) maximum rainfall is observed west of 125 °E during northern winter and east of 125 °E during northern summer. The spatial relationships of the seasonal rainfall (June to September) with the large scale parameters – the Subtropical Ridge (STR) position over the Indian and the west Pacific regions, the Darwin Pressure Tendency (DPT) and the Northern Hemisphere Surface Temperature (NHST) – reveal that within the Asian monsoon regime, not only are there any regions which are in-phase with Indian monsoon rainfall, but there are also regions which are out-of-phase. The spatial patterns of correlation coefficients with all the parameters are similar, with in-phase relationships occurring over the Indian region, some inland regions of Thailand, central parts of Brunei and the Indonesian region lying between 120° to 140 °E. However, northwest Philippines and some southern parts of Kampuchea and Vietnam show an out-of-phase relationship. Even the first Empirical Orthogonal Function of seasonal rainfall shows similar spatial configuration, suggesting that the spatial correlation patterns depict the most dominant mode of interannual rainfall variability. The influence of STR and DPT (NHST) penetrates (does not penetrate) upto the equatorial regions. Possible dynamic causes leading to the observed correlation structure are also discussed. Received October 10, 1996 Revised February 25, 1997     

Western Himalayan snow cover and Indian monsoon rainfall: A re-examination with INSAT and NCEP/NCAR data

Summary ?This study presents the monthly climatology and variability of the INSAT (Indian National Satellite) derived snow cover estimates over the western Himalayan region. The winter/spring snow estimates over the region are related to the subsequent summer monsoon rainfall over India. The NCEP/NCAR data are used to understand the physical mechanism of the snow-monsoon links. 15 years (1986–2000) of recent data are utilized to investigate these features in the present global warming environment. Results reveal that the spring snow cover area has been declining and snow has been melting faster from winter to spring after 1993. Connections between snow cover estimates and Indian monsoon rainfall (IMR) show that spring snow cover area is negatively related with maximum during May, while snow melt during the February–May period is positively related with subsequent IMR, implying that smaller snow cover area during May and faster snow melt from winter to spring is conducive for good monsoon activity over India. NCEP/NCAR data further shows that the heat low over northwest India and the monsoon circulation over the Indian subcontinent, in particular the cross-equatorial flow, during May are intensified (weakened) when the snow cover area during May is smaller (extensive) and snow melts faster (slower) during the February–May period. The well-documented negative relationship between winter snow and summer rainfall seems to have altered recently and changed to a positive relationship. The changes observed in snow cover extent and snow depth due to global warming may be a possible cause for the weakening winter snow–IMR relationship. Received January 15, 2002; revised May 5, 2002; accepted June 23, 2002     

Comparison of classification and clustering methods in spatial rainfall pattern recognition at Northern Iran

Pattern recognition is the science of data structure and its classification. There are many classification and clustering methods prevalent in pattern recognition area. In this research, rainfall data in a region in Northern Iran are classified with natural breaks classification method and with a revised fuzzy c-means (FCM) algorithm as a clustering approach. To compare these two methods, the results of the FCM method are hardened. Comparison proved overall coincidence of natural breaks classification and FCM clustering methods. The differences arise from nature of these two methods. In the FCM, the boundaries between adjacent clusters are not sharp while they are abrupt in natural breaks method. The sensitivity of both methods with respect to rain gauge density was also analyzed. For each rain gauge density, percentage of boundary region and hardening error are at a minimum in the first cluster while the second cluster has the maximum error. Moreover, the number of clusters was sensitive to the number of stations. Since the optimum number of classes is not apparent in the classification methods and the boundary between adjacent classes is abrupt, use of clustering methods such as the FCM method, overcome such deficiencies. The methods were also applied for mapping an aridity index in the study region where the results revealed good coincidence between the FCM clustering and natural breaks classification methods.     

Multivariate ENSO index and Indian monsoon rainfall: relationships on monthly and subdivisional scales

Summary El Ni?o/Southern Oscillation (ENSO) is known to cause world-wide weather anomalies. It influences the Indian Monsoon Rainfall (IMR) also. But due to large spatial and temporal variability of monsoon rains, it becomes difficult to state any single uniform relationship between the ENSO and IMR that holds good over different subdivisions of India, though the general type of relationship between all India monsoon rainfall and ENSO is known since long. The selection of the most suitable ENSO index to correlate with the IMR is another problem. The purpose of the present study is twofold, namely, to examine the relationship between the ENSO and IMR for entire monsoon season by using an ENSO index which represents the ENSO phenomenon in a comprehensive way, namely, the Multivariate ENSO Index (MEI) and to establish the relationships between MEI and IMR for every meteorological subdivision of India for each monsoon month; i.e. June, July, August and September. A comparison of MEI/IMR correlations has been made with Southern Oscillation Index (SOI)/IMR correlations. The result may find applications in the long range forecasting of IMR on monthly and subdivisional scales, especially over the high monsoon rainfall variability regions of Northwestern and the Peninsular India. Received October 27, 2000     

Role of TBO and ENSO scale ocean–atmosphere interaction in the Indo-Pacific region on Asian summer monsoon variability

Interannual variability of the Indian summer monsoon rainfall has two dominant periodicities, one on the quasi-biennial (2–3 year) time scale corresponding to tropospheric biennial oscillation (TBO) and the other on low frequency (3–7 year) corresponding to El Niño Southern Oscillation (ENSO). In the present study, the spatial and temporal patterns of various atmospheric and oceanic parameters associated with the Indian summer monsoon on the above two periodicities were investigated using NCEP/NCAR reanalysis data sets for the period 1950–2005. Influences of Indian and Pacific Ocean SSTs on the monsoon season rainfall are different for both of the time scales. Seasonal evolution and movement of SST and Walker circulation are also different. SST and velocity potential anomalies are southeast propagating on the TBO scale, while they are stationary on the ENSO scale. Latent heat flux and relative humidity anomalies over the Indian Ocean and local Hadley circulation between the Indian monsoon region and adjacent oceans have interannual variability only on the TBO time scale. Local processes over the Indian Ocean determine the Indian Ocean SST in biennial periodicity, while the effect of equatorial east Pacific SST is significant in the ENSO periodicity. TBO scale variability is dependent on the local factors of the Indian Ocean and the Indian summer monsoon, while the ENSO scale processes are remotely controlled by the Pacific Ocean.     

Prediction of summer monsoon rainfall over India using the NCEP climate forecast system

The performance of a dynamical seasonal forecast system is evaluated for the prediction of summer monsoon rainfall over the Indian region during June to September (JJAS). The evaluation is based on the National Centre for Environmental Prediction’s (NCEP) climate forecast system (CFS) initialized during March, April and May and integrated for a period of 9 months with a 15 ensemble members for 25 years period from 1981 to 2005. The CFS’s hindcast climatology during JJAS of March (lag-3), April (lag-2) and May (lag-1) initial conditions show mostly an identical pattern of rainfall similar to that of verification climatology with the rainfall maxima (one over the west-coast of India and the other over the head Bay of Bengal region) well simulated. The pattern correlation between verification and forecast climatology over the global tropics and Indian monsoon region (IMR) bounded by 50°E–110°E and 10°S–35°N shows significant correlation coefficient (CCs). The skill of simulation of broad scale monsoon circulation index (Webster and Yang; WY index) is quite good in the CFS with highly significant CC between the observed and predicted by the CFS from the March, April and May forecasts. High skill in forecasting El Nino event is also noted for the CFS March, April and May initial conditions, whereas, the skill of the simulation of Indian Ocean Dipole is poor and is basically due to the poor skill of prediction of sea surface temperature (SST) anomalies over the eastern equatorial Indian Ocean. Over the IMR the skill of monsoon rainfall forecast during JJAS as measured by the spatial Anomaly CC between forecast rainfall anomaly and the observed rainfall anomaly during 1991, 1994, 1997 and 1998 is high (almost of the order of 0.6), whereas, during the year 1982, 1984, 1985, 1987 and 1989 the ACC is only around 0.3. By using lower and upper tropospheric forecast winds during JJAS over the regions of significant CCs as predictors for the All India Summer Monsoon Rainfall (AISMR; only the land stations of India during JJAS), the predicted mean AISMR with March, April and May initial conditions is found to be well correlated with actual AISMR and is found to provide skillful prediction. Thus, the calibrated CFS forecast could be used as a better tool for the real time prediction of AISMR.     

Empirical Orthogonal Function (EOF) analysis of monsoon rainfall and satellite-observed outgoing long-wave radiation for Indian monsoon: a comparative study

Summary The present study involves the use of Empirical Orthogonal Function (EOF) analysis/Principal Component Analysis (PCA) to compare the dominant rainfall patterns from normal rainfall records over India, coupled with the major modes of the Outgoing Long-wave Radiation (OLR) data for the period (1979–1988) during the monsoon period (June–September). To understand the intraseasonal and interannual variability of the monsoon rainfall, daily and seasonal anomalies have been obtained by using the (EOF) analysis. Importantly, pattern characteristics of seasonal monsoon rainfall covering 68 stations in India are highlighted.The purpose is to ascertain the nature of rainfall distribution over the Indian continent. Based on this, the percentage of variance for both the rainfall and OLR data is examined. OLR has a higher spatial coherence than rainfall. The first principal component of rainfall data shows high positive values, which are concentrated over northeast as well as southeast, whereas for the OLR, the area of large positive values is concentrated over northwest and lower value over south India apart from the Indian ocean. The first five principal components explain 92.20% of the total variance for the rainfall and 99.50% of the total variance for the outgoing long-wave radiation. The relationship between monsoon rainfall and Southern Oscillations has also been examined and for the Southern Oscillations, it is 0.69 for the monsoon season. The El-Niño events mostly occurred during Southern Oscillations, i.e. Walker circulation. It has been found that the average number of low pressure system/low pressure system days play an important role during active (flood) or inactive (drought) monsoon year, but low pressure system days play more important role in comparison to low pressure systems and their ratio are (16:51) and (13:25) respectively. Significantly, the analysis identifies the spatial and temporal pattern characteristics of possible physical significance.     

Spatial and temporal relationships between global land surface air temperature anomalies and Indian summer monsoon rainfall

Summary Using the 60 year period (1931–1990) gridded land surface air temperature anomalies data, the spatial and temporal relationships between Indian summer monsoon rainfall and temperature anomalies were examined. Composite temperature anomalies were prepared in respect of 11 deficient monsoon years and 9 excess monsoon years. Statistical tests were carried out to examine the significance of the composites. In addition, correlation coefficients between the temperature anomalies and Indian summer monsoon rainfall were also calculated to examine the teleconnection patterns.There were statistically significant differences in the composite of temperature anomaly patterns between excess and deficient monsoon years over north Europe, central Asia and north America during January and May, over NW India during May, over central parts of Africa during May and July and over Indian sub-continent and eastern parts of Asia during July. It has been also found that temperature anomalies over NW Europe, central parts of Africa and NW India during January and May were positively correlated with Indian summer monsoon rainfall. Similarly temperature anomalies over central Asia during January and temperature anomalies over central Africa and Indian region during July were negatively correlated. There were secular variations in the strength of relationships between temperature anomalies and Indian summer monsoon rainfall. In general, temperature anomalies over NW Europe and NW India showed stronger correlations during the recent years. It has been also found that during excess (deficient) monsoon years temperature gradient over Eurasian land mass from sub-tropics to higher latitudes was directed equatowards (polewards) indicating strong (weak) zonal flow. This temperature anomaly gradient index was found to be a useful predictor for long range forecasting of Indian summer monsoon rainfall.With 12 Figures     

Reclassification of rainfall regions of Turkey by K-means methodology and their temporal variability in relation to North Atlantic Oscillation (NAO)

Rainfall over Turkey portrays highly variable character both spatially and temporally. The aim of this study is to redefine main rainfall clusters of Turkey by using k-means methodology and investigate spatial shifts in the redefined rainfall clusters in subsequent periods with respect to North Atlantic Oscillation (NAO) patterns. Initially, monthly rainfall totals are subjected to k-means clustering by taking into consideration 148 stations covering the 1977?C2006 period. Considering the maximum silhouette value and lowest negative silhouette number, six rainfall clusters are determined as optimum classifications for this climate period. The results indicate that Aegean?CMarmara and Eastern Anatolia?CCentral Anatolia geographic regions are characterized as single rainfall cluster contrary to the conventional geographical regions. The Mediterranean region is characterized with two separate sub-regions indicating highly variable rainfall distribution characters over the region. The study further adapts a similar classification for 10-year sub-periods to determine spatial shifts of the redefined rainfall clusters for the last 30?years. From one decade to another, temporally drier and wetter clusters are observed with underlying shifting causes in relation to NAO patterns. Parallel to other studies in the literature, NAO is found to be partially useful in explaining the temporally dry trends while less useful in justifying wet periods. On the other hand, coefficient of variation (COV) is introduced in order to explain the temporal shifts in the clusters. Strong relations are obtained between the regions with the higher COV numbers and highest cluster shifts, while smaller COV numbers are associated with the most stable clusters.     

Role of intra-seasonal oscillations in modulating Indian summer monsoon rainfall

The day-to-day behavior of Indian summer monsoon rainfall (IMR) is associated with a hierarchy of quasi-periods, namely 3?C7, 10?C20 and the 30?C60?days. These two periods, the 10?C20?days and the 30?C60?days have been related with the active and break cycles of the monsoon rainfall over the Indian sub-continent. The seasonal strength of Indian summer monsoon rainfall may depend on the frequency and duration of spells of break and active periods associated with the fluctuations of the above intra-seasonal oscillations (ISOs). Thus the predictability of the seasonal (June through September) mean Indian monsoon depends on the extent to which the intra-seasonal oscillations could be predicted. The primary objective of this study is to bring out the dynamic circulation features during the pre-monsoon/monsoon season associated with the extreme phases of these oscillations The intense (weak) phase of the 10?C20 (30?C60) days oscillation is associated with anti-cyclonic circulation over the Indian Ocean, easterly flow over the equatorial Pacific Ocean resembling the normal or cold phase (La Nina) of El Nino Southern Oscillation (ENSO) phenomenon, and weakening of the north Pacific Sub-tropical High. On the other hand the weak phase of 10?C20?days mode and the intense phase of 30?C60?days mode shows remarkable opposite flow patterns. The circulation features during pre-monsoon months show that there is a tendency for the flow patterns observed in pre-monsoon months to persist during the monsoon months. Hence some indications of the behavior of these modes during the monsoon season could be foreshadowed from the spring season patterns. The relationship between the intensity of these modes and some of the long-range forecasting parameters used operationally by the India Meteorological Department has also been examined.     

Pattern characteristics of Indian monsoon rainfall using principal component analysis (PCA)

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.     

Long-range prediction of Indian summer monsoon rainfall using data mining and statistical approaches

This paper presents a hybrid model to better predict Indian summer monsoon rainfall. The algorithm considers suitable techniques for processing dense datasets. The proposed three-step algorithm comprises closed itemset generation-based association rule mining for feature selection, cluster membership for dimensionality reduction, and simple logistic function for prediction. The application of predicting rainfall into flood, excess, normal, deficit, and drought based on 36 predictors consisting of land and ocean variables is presented. Results show good accuracy in the considered study period of 37years (1969–2005).     

Analysis of spatial and temporal extreme monsoonal rainfall over South Asia using complex networks

We present a detailed analysis of summer monsoon rainfall over the Indian peninsular using nonlinear spatial correlations. This analysis is carried out employing the tools of complex networks and a measure of nonlinear correlation for point processes such as rainfall, called event synchronization. This study provides valuable insights into the spatial organization, scales, and structure of the 90th and 94th percentile rainfall events during the Indian summer monsoon (June–September). We furthermore analyse the influence of different critical synoptic atmospheric systems and the impact of the steep Himalayan topography on rainfall patterns. The presented method not only helps us in visualising the structure of the extreme-event rainfall fields, but also identifies the water vapor pathways and decadal-scale moisture sinks over the region. Furthermore a simple scheme based on complex networks is presented to decipher the spatial intricacies and temporal evolution of monsoonal rainfall patterns over the last 6 decades.     

Decadal scale oscillations and trend in the Indian monsoon rainfall

The emerging need for extended climate prediction requires a consideration of the relative roles of climate change and low-frequency natural variability on decadal scale. Addressing this issue, this study has shown that the variability of the Indian monsoon rainfall (IMR) consists of three decadal scale oscillations and a nonlinear trend during 1901–2004. The space–time structures of the decadal oscillations are described. The IMR decadal oscillations are shown to be associated with Atlantic Multidecadal Oscillation (AMO), Atlantic tripole oscillation and Pacific Decadal Oscillation (PDO). The sea surface temperatures (SSTs) of the North Pacific and North Atlantic Oceans are also resolved as nonlinear decadal oscillations. The SST AMO mode has high positive correlation with IMR while the SST tripole mode and SST PDO have negative correlation. The trend in IMR increases during the first half of the period and decreases during the second half. The IMR trend is modified when combined with the three decadal oscillations.     

中国东部夏季（6—8月）降水的一种客观分型方法

对中国东部１２７站４７年（１９５１－１９９７）夏季（６－８月）的降水距平百分率数据进行客观分类,分析了我国东部夏季主要雨型的空间和时间特征。应用Ｗａｒｄ方法对上述数据进行了分类计算,并将此分类计算结果作为Ｋ－均值方法进行分类的初始类（凝聚类）,通过逐个修改,得出最终分类。经过比较,应用上述两种方法相结合的算法得出的中国东部夏季三类雨型的空间分布特征与目前国家气候中心业务工作中常用的三类雨型的空间分     

我国夏季雨型的前期异常特征及预报方法的初步研究

用１９５１～１９９５年资料研究了我国东部夏季降水各雨型的前期大气环流及我国地面气象要素场的异常特征．结果表明,在冬季１月份北太平洋地区、秋季中国南海地区的海平面气压场有预报我国夏季雨型的信号．夏季不同雨型的前期冬季特征不同,我国的降水、气温场也有差异,４月份我国大范围的温度异常也是值得注意的预测信号．这些特征可以作为我国夏季雨型的预报信号及预报工具．     

The evolution of Indian summer monsoon in 1997 and 1983

Summary During most El-Ni?o events the Indian summer monsoon rainfall has been below normal. El-Ni?o that occurred during 1997 was one of the strongest in the 20th century, but did not have an adverse impact on the Indian summer monsoon rainfall in 1997. This is despite the fact that most parameters observed in May 1997 suggested that the Indian summer monsoon rainfall may be below normal. This intriguing feature of the 1997 Indian summer monsoon rainfall has been examined by studying the evolution of various parameters from May to August. The behavior of the 1997 monsoon is related to its evolution during June and July, with westward migration of cloudbands from West Pacific that increased convection over Bay of Bengal. We find that there exists a significant correlation between convective activity over Bay of Bengal and winds over the Arabian Sea with the latter lagging convection over Bay of Bengal by about three days. The convective activity over Bay of Bengal induces stronger winds over the Arabian Sea and this in turn enhances advection of moisture into the Indian landmass and leads to increased precipitable water and strength of the monsoon. Using a simple thermodynamic model we show that increased precipitable water during July leads to increased rainfall. A similar behavior has also been noticed during the 1983 monsoon, with precursors indicating a possible poor monsoon but subsequent events changed the course of the monsoon. Received May 21, 2001 Revised October 10, 2001     

Identification of typical synoptic patterns causing heavy rainfall in the rainy season in Japan by a Self-Organizing Map

In order to systematically and visually understand well-known but qualitative and complex relationships between synoptic fields and heavy rainfall events in Kyushu Islands, southwestern Japan, during the BAIU season, these synoptic fields were classified using the Self-Organizing Map (SOM), which can convert complex non-linear features into simple two-dimensional relationships. It was assumed that the synoptic field patterns could be simply expressed by the spatial distribution of (1) wind components at the 850 hPa level and (2) precipitable water (PW) defined by the water vapor amount contained in a vertical column of the atmosphere. By the SOM algorithm and the clustering techniques of the U-matrix and the K-means, the synoptic fields could be divided into eight kinds of patterns (clusters). One of the clusters has the notable spatial features represented by a large PW content accompanied by strong wind components known as low-level jet (LLJ). The features of this cluster indicate a typical synoptic field pattern that frequently causes heavy rainfall in Kyushu during the rainy season.In addition, an independent data set was used for validating the performance of the trained SOM. The results indicated that the SOM could successfully extract heavy rainfall events related to typical synoptic field patterns of the BAIU season. Interestingly, one specific SOM unit was closely related to the occurrence of disastrous heavy rainfall events observed during both training and validation periods. From these results, the trained SOM showed good performance for identifying synoptic fields causing heavy rainfall also in the validation period. We conclude that the SOM technique may be an effective tool for classifying complicated non-linear synoptic fields and identifying heavy rainfall events to some degree.