Interannual and long-term variability of Indian summer monsoon rainfall

Analysis of summer monsoon (June to September) rainfall series of 29 subdivisions based on a fixed number of raingauges (306 stations) has been made for the 108-year period 1871–1978 for interannual and long-term variability of the rainfall. Statistical tests show that the rainfall series of 29 sub-divisions are homogeneous, Gaussian-distributed and do not contain any persistence. The highest and the lowest normal rainfall of 284 and 26 cm are observed over coastal Karnataka and west Rajasthan sub-divisions respectively. The interannual variability (range) varies over different sub-divisions, the lowest being 55 and the highest 231% of the normal rainfall, for south Assam and Saurashtra and Kutch sub-divisions respectively. High spatial coherency is observed between neighbouring sub-divisions; northeast region and northern west and peninsular Indian sub-divisions show oppositic correlation tendency. Significant change in mean rainfall of six sub-divisions is noticed. Correlogram and spectrum analysis show the presence of 14-year and QBO cycles in a few sub-divisional rainfall series.     

Large-scale interannual variation of the summer monsoon over India and its empirical prediction

Large-scale interannual variability of the northern summer southwest monsoon over India is studied by examining its variation in the dry area during the period 1871–1984. On the mean summer monsoon rainfall (June to September total) chart the 800 mm isohyet divides the country into two nearly equal halves, named as dry area (monsoon rainfall less than 800 mm) and wet area (monsoon rainfall greater than 800 mm). The dry area/wet area shows large variations from one year to another, and is considered as an index for assessing the large-scale performance of the Indian summer monsoon. Statistical and fluctuation characteristics of the summer monsoon dry area (SMDA) are reported. To identify possible causes of variation in the Indian summer monsoon, the correlation between the summer monsoon dry area and eleven regional/global circulation parameters is examined. The northern hemisphere surface air temperature, zonal/hemispheric/global surface air and upper air temperatures, Southern Oscillation, Quasi-biennial oscillation of the equatorial lower stratosphere, April 500-mb ridge along 75°E over India, the Indian surface air temperature and the Bombay sea level pressure showed significant correlation. A new predictor parameter that is preceding year mean monsoon rainfall of a few selected stations over India has been suggested in the present study. The stations have been selected by applying the objective technique ‘selecting a subset of few gauges whose mean monsoon rainfall of the preceding year has shown the highest correlation coefficient (CC) with the SMDA’. Bankura (Gangetic West Bengal), Cuddalore (Tamil Nadu) and Anupgarh (West Rajasthan) entered the selection showing a CC of 0.724. Using a dependent sample of 1951–1980 a predictive model (multiple CC = 0.745) has also been developed for the SMDA with preceding year mean monsoon rainfall of the three selected stations and the sea level pressure tendency at Darwin from Jan–Feb to Mar–May as independent parameters.     

Some characteristics of very heavy rainfall over Orissa during summer monsoon season

Orissa is one of the most flood prone states of India. The floods in Orissa mostly occur during monsoon season due to very heavy rainfall caused by synoptic scale monsoon disturbances. Hence a study is undertaken to find out the characteristic features of very heavy rainfall (24 hours rainfall ≥125 mm) over Orissa during summer monsoon season (June–September) by analysing 20 years (1980–1999) daily rainfall data of different stations in Orissa. The principal objective of this study is to find out the role of synoptic scale monsoon disturbances in spatial and temporal variability of very heavy rainfall over Orissa. Most of the very heavy rainfall events occur in July and August. The region, extending from central part of coastal Orissa in the southeast towards Sambalpur district in the northwest, experiences higher frequency and higher intensity of very heavy rainfall with less interannual variability. It is due to the fact that most of the causative synoptic disturbances like low pressure systems (LPS) develop over northwest (NW) Bay of Bengal with minimum interannual variation and the monsoon trough extends in west-northwesterly direction from the centre of the system. The very heavy rainfall occurs more frequently with less interannual variability on the western side of Eastern Ghat during all the months and the season except September. It occurs more frequently with less interannual variability on the eastern side of Eastern Ghat during September. The NW Bay followed by Gangetic West Bengal/Orissa is the most favourable region of LPS to cause very heavy rainfall over different parts of Orissa except eastern side of Eastern Ghat. The NW Bay and west central (WC) Bay are equally favourable regions of LPS to cause very heavy rainfall over eastern side of Eastern Ghat. The frequency of very heavy rainfall does not show any significant trend in recent years over Orissa except some places in north-east Orissa which exhibit significant rising trend in all the monsoon months and the season as a whole.     

Coherent rainfall zones: Case study for Karnataka

Generally average rainfall over meteorological subdivisions is used for assessment of the variability of monsoon rainfall. It is shown here that variations of seasonal rainfall over the meteorological subdivisions of interior Karnataka are not coherent. A methodology for delineating coherent rainfall zones is developed in this paper and applied to derive such zones for the State of Karnataka.     

Quantitative precipitation forecasting over Narmada Catchment

Quantitative precipitation forecasting (QPF) has been attempted over the Narmada Catchment following a statistical approach. The catchment has been divided into five sub-regions for the development of QPF models with a maximum lead-time of 24 hours. For this purpose the data of daily rainfall from 56 raingauge stations, twice daily observations on different surface meteorological parameters from 28 meteorological observatories and upper air data from 11 aerological stations for the nine monsoon seasons of 1972–1980 have been utilized. The horizontal divergence, relative vorticity, vertical velocity and moisture divergence are computed using the kinematic method at different pressure levels and used as independent variables along with the rainfall and surface meteorological parameters. Multiple linear regression equations have been developed using the stepwise procedure separately with actual and square root and log-transformed rainfall using 8-year data (1972–1979). When these equations were verified with an independent data for the monsoon season of 1980, it was found that the transformed rainfall equations fared much better compared to the actual rainfall equations. The performance of the forecasts of QPF model compared to the climatological and persistence forecasts has been assessed by computing the verification scores using the forecasts for the monsoon season of 1980.     

Relationship between all-India summer monsoon rainfall and southern oscillation/eastern equatorial Pacific sea surface temperature

The interannual variability of all-India summer monsoon (June to September) rainfall and its teleconnections with the southern oscillation index (SOI) and sea surface temperature (SST) anomaly of the eastern equatorial Pacific ocean have been examined for the period 1871–1978 for different seasons (i.e., winter, spring, summer and autumn). The relationship (correlation coefficient) between all-India summer monsoon rainfall andSOI for different seasons is positive and highly significant. Further examination of 10-, 20- and 30-year sliding window lengths’ correlations, brings out the highly consistent and significant character of the relationships. The relationship between all-India monsoon rainfall andSST for different seasons is negative and is significant at 1 % level or above. Drought years are characterised by negative anomalies ofSOI and positive anomalies ofSST and vice versa with flood years. The relationship betweenSOI andSST is negative and significant at 0.1 % level. The relationships between all-India summer monsoon rainfall,SOI and sst are expected to improve our understanding of the interannual variability of the summer monsoon.     

Spatio-temporal variability of summer monsoon rainfall over Orissa in relation to low pressure systems

The summer monsoon rainfall over Orissa occurs mostly due to low pressure systems (LPS) developing over the Bay of Bengal and moving along the monsoon trough. A study is hence undertaken to find out characteristic features of the relationship between LPS over different regions and rain-fall over Orissa during the summer monsoon season (June-September). For this purpose, rainfall and rainy days over 31 selected stations in Orissa and LPS days over Orissa and adjoining land and sea regions during different monsoon months and the season as a whole over a period of 20 years (1980-1999) are analysed. The principal objective of this study is to find out the role of LPS on spatial and temporal variability of summer monsoon rainfall over Orissa. The rainfall has been significantly less than normal over most parts of Orissa except the eastern side of Eastern Ghats during July and hence during the season as a whole due to a significantly less number of LPS days over northwest Bay in July over the period of 1980-1999. The seasonal rainfall shows higher interannual variation (increase in coefficient of variation by about 5%) during 1980-1999 than that during 1901-1990 over most parts of Orissa except northeast Orissa. Most parts of Orissa, especially the region extending from central part of coastal Orissa to western Orissa (central zone) and western side of the Eastern Ghats get more seasonal monsoon rainfall with the development and persistence of LPS over northwest Bay and their subsequent movement and persistence over Orissa. The north Orissa adjoining central zone also gets more seasonal rainfall with development and persistence of LPS over northwest Bay. While the seasonal rainfall over the western side of the Eastern Ghats is adversely affected due to increase in LPS days over west central Bay, Jharkhand and Bangladesh, that over the eastern side of the Eastern Ghats is adversely affected due to increase in LPS days over all the regions to the north of Orissa. There are significant decreasing trends in rainfall and number of rainy days over some parts of southwest Orissa during June and decreasing trends in rainy days over some parts of north interior Orissa and central part of coastal Orissa during July over the period of 1980-1999     

Indian Monsoon Variability in a Global Warming Scenario

The Intergovernmental Panel on Climate Change (IPCC) constituted by the World Meteorological Organisation provides expert guidance regarding scientific and technical aspects of the climate problem. Since 1990 IPCC has, at five-yearlyintervals, assessedand reported on the current state of knowledge and understanding of the climate issue. These reports have projected the behaviour of the Asian monsoon in the warming world. While the IPCC Second Assessment Report (IPCC, 1996) on climate model projections of Asian/Indian monsoon stated ``Most climate models produce more rainfall over South Asia in a warmer climate with increasing CO<sub>2</sub>', the recent IPCC (2001) Third Assessment Report states ``It is likely that the warming associated with increasing greenhouse gas concentrations will cause an increase in Asian summer monsoon variability and changes in monsoon strength.'Climate model projections(IPCC, 2001) also suggest more El Niño – like events in the tropical Pacific, increase in surface temperatures and decrease in the northern hemisphere snow cover. The Indian Monsoon is an important component of the Asian monsoon and its links with the El Niño Southern Oscillation (ENSO) phenomenon, northern hemisphere surface temperature and Eurasian snow are well documented.In the light of the IPCC globalwarming projections on the Asian monsoon, the interannual and decadal variability in summer monsoon rainfall over India and its teleconnections have been examined by using observed data for the 131-year (1871–2001) period. While the interannual variations showyear-to-year random fluctuations, thedecadal variations reveal distinct alternate epochs of above and below normal rainfall. The epochs tend to last for about three decades. There is no clear evidence to suggest that the strength and variability of the Indian Monsoon Rainfall (IMR) nor the epochal changes are affected by the global warming. Though the 1990s have been the warmest decade of the millennium(IPCC, 2001), the IMR variability has decreased drastically.Connections between the ENSO phenomenon, Northern Hemisphere surface temperature and the Eurasian snow with IMR reveal that the correlations are not only weak but have changed signs in the early 1990s suggesting that the IMR has delinked not only with the Pacific but with the Northern Hemisphere/Eurasian continent also. The fact that temperature/snow relationships with IMR are weak further suggests that global warming need not be a cause for the recent ENSO-Monsoon weakening.Observed snow depth over theEurasian continent has been increasing, which could be a result of enhanced precipitation due to the global warming.     

Spatio-temporal analysis of sub-hourly rainfall over Mumbai,India: Is statistical forecasting futile?

Mumbai, the commercial and financial capital of India, experiences incessant annual rain episodes, mainly attributable to erratic rainfall pattern during monsoons and urban heat-island effect due to escalating urbanization, leading to increasing vulnerability to frequent flooding. After the infamous episode of 2005 Mumbai torrential rains when only two rain gauging stations existed, the governing civic body, the Municipal Corporation of Greater Mumbai (MCGM) came forward with an initiative to install 26 automatic weather stations (AWS) in June 2006 (MCGM 2007), which later increased to 60 AWS. A comprehensive statistical analysis to understand the spatio-temporal pattern of rainfall over Mumbai or any other coastal city in India has never been attempted earlier. In the current study, a thorough analysis of available rainfall data for 2006–2014 from these stations was performed; the 2013–2014 sub-hourly data from 26 AWS was found useful for further analyses due to their consistency and continuity. Correlogram cloud indicated no pattern of significant correlation when we considered the closest to the farthest gauging station from the base station; this impression was also supported by the semivariogram plots. Gini index values, a statistical measure of temporal non-uniformity, were found above 0.8 in visible majority showing an increasing trend in most gauging stations; this sufficiently led us to conclude that inconsistency in daily rainfall was gradually increasing with progress in monsoon. Interestingly, night rainfall was lesser compared to daytime rainfall. The pattern-less high spatio-temporal variation observed in Mumbai rainfall data signifies the futility of independently applying advanced statistical techniques, and thus calls for simultaneous inclusion of physics-centred models such as different meso-scale numerical weather prediction systems, particularly the Weather Research and Forecasting (WRF) model.     

Observed daily large-scale rainfall patterns during BOBMEX-1999

A daily rainfall dataset and the corresponding rainfall maps have been produced by objective analysis of rainfall data. The satellite estimate of rainfall and the raingauge values are merged to form the final analysis. Associated with epochs of monsoon these rainfall maps are able to show the rainfall activities over India and the Bay of Bengal region during the BOBMEX period. The intra-seasonal variations of rainfall during BOBMEX are also seen using these data. This dataset over the oceanic region compares well with other available popular datasets like GPCP and CMAP. Over land this dataset brings out the features of monsoon in more detail due to the availability of more local raingauge stations.     

Probable maximum point rainfall estimation for the southern half of the Indian peninsula

Preparation of a generalized chart of probable maximum precipitation (PMP) for the southern half of the Indian peninsula lying between lat. 8°N to 16°N has been attempted in this study. Maximum 1-day rainfall data of 70 to 80 years from 1891 for about 600 stations in the peninsular states of Tamil Nadu, Kerala, South Karnataka and southern portions of Andhra Pradesh were used. In order to get appropriate values of PMP, envelope frequency factor (K _m) curve based on the actual rainfall data of the region was prepared. This study has shown that one-day PMP estimates over this region range from about 25 cm to about 85 cm. The heavy rainfall received over the coastal areas of Tamil Nadu in association with the cyclonic disturbance of November 1976 was examined and it was found that this rainfall was nowhere near the PMP estimates for this area.     

从湿季降水分异论哀牢山季风交汇

选择哀牢山和无量山中南部区域,利用该区1961-2007年月降水资料,分析湿季及各月降水时空分异与区域地形及季风交汇的关联.结论如下:区域湿季降水与岭谷地形的空间分异有着很好的吻合.哀牢山东侧主脉两侧降水量分异明显,但两侧各站湿季及各月降水的年际变化趋势等多项特征具有一致性.该区东南部和西北部的湿季降水存在明显分异,中...     

Inter-annual variability of summer monsoon rainfall over Orissa (India) in relation to cyclonic disturbances

The summer monsoon rainfall over Orissa, a state on the eastern coast of India, is more significantly related than Indian summer monsoon rainfall (ISMR) to the cyclonic disturbances developing over the Bay of Bengal. Orissa experiences floods and droughts very often due to variation in the characteristics of these disturbances. Hence, an attempt was made to find out the inter-annual variability in the rainfall over Orissa and the frequencies of different categories of cyclonic disturbances affecting Orissa during monsoon season (June–September). For this purpose, different statistical characteristics, such as mean, coefficient of variation, trends and periodicities in the rainfall and the frequencies of different categories of cyclonic disturbances affecting Orissa, were analysed from 100 years (1901–2000) of data. The basic objective of the study was to find out the contribution of inter-annual variability in the frequency of cyclonic disturbances to the inter-annual variability of monsoon rainfall over Orissa. The relationship between summer monsoon rainfall over Orissa and the frequency of cyclonic disturbances affecting Orissa shows temporal variation. The correlation between them has significantly decreased since the 1950s. The variation in their relationship is mainly due to the variation in the frequency of cyclonic disturbances affecting Orissa. The variability of both rainfall and total cyclonic disturbances has been above normal since the 1960s, leading to more floods and droughts over Orissa during recent years. The inter-annual variability of seasonal rainfall over Orissa and the frequency of cyclonic disturbances affecting Orissa during monsoon season show a quasi-biennial oscillation period of 2–2.8 years. There is least impact of El Nino southern oscillation (ENSO) on inter-annual variability of both the seasonal rainfall over Orissa and the frequencies of monsoon depressions/total cyclonic disturbances affecting Orissa.     

The chaotic time series of Indian monsoon rainfall and its prediction

The time series of Indian summer monsoon rainfall for the period 1871–1989 has been analysed using the method of deterministic chaos. It is found that a strange attractor underlies the time series implying the existence of a prediction function. This function has been approximated by a second-degree polynomial, involving the rainfalls of the past seven years and the coefficients have been estimated by least squares fit. The interannual variations of actual and computed rainfalls have been presented for a comparative study.     

Probabilities of excess and deficient southwest monsoon rainfall over different meteorological sub-divisions of India

Temporal distribution of southwest monsoon (June –September) rainfall is very useful for the country’s agriculture and food grain production. It contributes more than 75% of India’s annual rainfall. In view of this, an attempt has been made here to understand the performance of the monthly rainfall for June, July, August and September when the seasonal rainfall is reported as excess, deficient or normal. To know the dependence of seasonal rainfall on monthly rainfall, the probabilities of occurrence of excess, deficient and normal monsoon when June, July, August and also June + July and August + September rainfall is reported to be excess or deficient, are worked out using the long homogenous series of 124 years (1871-–1994) data of monthly and seasonal rainfall of 29 meteorological sub-divisions of the plain regions of India. In excess monsoon years, the average percentage contribution of each monsoon month to the long term mean (1871–1994) seasonal rainfall (June –September) is more than that of the normal while in the deficient years it is less than normal. This is noticed in all 29 meteorological sub-divisions. From the probability analysis, it is seen that there is a rare possibility of occurrence of seasonal rainfall to be excess/deficient when the monthly rainfall of any month is deficient/excess.     

District-wide drought climatology of the southwest monsoon season over India based on standardized precipitation index (SPI)

District-wide drought climatology over India for the southwest monsoon season (June–September) has been examined using two simple drought indices; Percent of Normal Precipitation (PNP) and Standardized Precipitation Index (SPI). The season drought indices were computed using long times series (1901–2003) of southwest monsoon season rainfall data of 458 districts over the country. Identification of all India (nation-wide) drought incidences using both PNP and SPI yielded nearly similar results. However, the district-wide climatology based on PNP was biased by the aridity of the region. Whereas district-wide drought climatology based on SPI was not biased by aridity. This study shows that SPI is a better drought index than PNP for the district-wide drought monitoring over the country. SPI is also suitable for examining break and active events in the southwest monsoon rainfall over the country. The trend analysis of district-wide season (June–September) SPI series showed significant negative trends over several districts from Chattisgarh, Bihar, Kerala, Jharkhand, Assam and Meghalaya, Uttaranchal, east Madhya Pradesh, Vidarbha etc., Whereas significant positive trends in the SPI series were observed over several districts from west Uttar Pradesh, west Madhya Pradesh, South & north Interior Karnataka, Konkan and Goa, Madhya Maharashtra, Tamil Nadu, East Uttar Pradesh, Punjab, Gujarat etc.     

Active and break spells of the Indian summer monsoon

In this paper, we suggest criteria for the identification of active and break events of the Indian summer monsoon on the basis of recently derived high resolution daily gridded rainfall dataset over India (1951–2007). Active and break events are defined as periods during the peak monsoon months of July and August, in which the normalized anomaly of the rainfall over a critical area, called the monsoon core zone exceeds 1 or is less than −1.0 respectively, provided the criterion is satisfied for at least three consecutive days. We elucidate the major features of these events. We consider very briefly the relationship of the intraseasonal fluctuations between these events and the interannual variation of the summer monsoon rainfall.     

Trends and variability of meteorological drought over the districts of India using standardized precipitation index

Meteorological drought during the southwest monsoon season and for the northeast monsoon season over five meteorological subdivisions of India for the period 1901–2015 has been examined using district and all India standardized precipitation index (SPI). Whenever all India southwest monsoon rainfall was less than ?10% or below normal, for those years all India SPI was found as ?1 or less. Composite analysis of SPI for the below normal years, viz., less than ?15% and ?20% of normal rainfall years indicate that during those years more than 30% of country’s area was under drought condition, whenever all India southwest monsoon rainfall was –15% or less than normal. Trend analysis of monthly SPI for the monsoon months identified the districts experiencing significant increase in drought occurrences. Significant positive correlation has been found with the meteorological drought over most of the districts of central, northern and peninsular India, while negative correlation was seen over the districts of eastern India with NINO 3.4 SST. For the first time, meteorological drought analysis over districts and its association with equatorial pacific SST and probability analysis has been done for the northeast monsoon over the affected regions of south peninsular India. Temporal correlation of all India southwest monsoon SPI and south peninsular India northeast monsoon SPI has been done with the global SST to identify the teleconnection of drought in India with global parameters.     

Multi-model ensemble schemes for predicting northeast monsoon rainfall over peninsular India

The northeast (NE) monsoon season (October, November and December) is the major period of rainfall activity over south peninsular India. This study is mainly focused on the prediction of northeast monsoon rainfall using lead-1 products (forecasts for the season issued in beginning of September) of seven general circulation models (GCMs). An examination of the performances of these GCMs during hindcast runs (1982–2008) indicates that these models are not able to simulate the observed interannual variability of rainfall. Inaccurate response of the models to sea surface temperatures may be one of the probable reasons for the poor performance of these models to predict seasonal mean rainfall anomalies over the study domain. An attempt has been made to improve the accuracy of predicted rainfall using three different multi-model ensemble (MME) schemes, viz., simple arithmetic mean of models (EM), principal component regression (PCR) and singular value decomposition based multiple linear regressions (SVD). It is found out that among these three schemes, SVD based MME has more skill than other MME schemes as well as member models.     

Influence of southern oscillation and SSTs over Nino-3.4 region on the winter monsoon rainfall over coastal Andhra Pradesh

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