Meta-heuristic ant colony optimization technique to forecast the amount of summer monsoon rainfall: skill comparison with Markov chain model

Forecasting summer monsoon rainfall with precision becomes crucial for the farmers to plan for harvesting in a country like India where the national economy is mostly based on regional agriculture. The forecast of monsoon rainfall based on artificial neural network is a well-researched problem. In the present study, the meta-heuristic ant colony optimization (ACO) technique is implemented to forecast the amount of summer monsoon rainfall for the next day over Kolkata (22.6°N, 88.4°E), India. The ACO technique belongs to swarm intelligence and simulates the decision-making processes of ant colony similar to other adaptive learning techniques. ACO technique takes inspiration from the foraging behaviour of some ant species. The ants deposit pheromone on the ground in order to mark a favourable path that should be followed by other members of the colony. A range of rainfall amount replicating the pheromone concentration is evaluated during the summer monsoon season. The maximum amount of rainfall during summer monsoon season (June—September) is observed to be within the range of 7.5–35 mm during the period from 1998 to 2007, which is in the range 4 category set by the India Meteorological Department (IMD). The result reveals that the accuracy in forecasting the amount of rainfall for the next day during the summer monsoon season using ACO technique is 95 % where as the forecast accuracy is 83 % with Markov chain model (MCM). The forecast through ACO and MCM are compared with other existing models and validated with IMD observations from 2008 to 2012.     

All India summer monsoon rainfall prediction using an artificial neural network

The prediction of Indian summer monsoon rainfall (ISMR) on a seasonal time scales has been attempted by various research groups using different techniques including artificial neural networks. The prediction of ISMR on monthly and seasonal time scales is not only scientifically challenging but is also important for planning and devising agricultural strategies. This article describes the artificial neural network (ANN) technique with error- back-propagation algorithm to provide prediction (hindcast) of ISMR on monthly and seasonal time scales. The ANN technique is applied to the five time series of June, July, August, September monthly means and seasonal mean (June + July + August + September) rainfall from 1871 to 1994 based on Parthasarathy data set. The previous five years values from all the five time-series were used to train the ANN to predict for the next year. The details of the models used are discussed. Various statistics are calculated to examine the performance of the models and it is found that the models could be used as a forecasting tool on seasonal and monthly time scales. It is observed by various researchers that with the passage of time the relationships between various predictors and Indian monsoon are changing, leading to changes in monsoon predictability. This issue is discussed and it is found that the monsoon system inherently has a decadal scale variation in predictability. Received: 13 March 1999 / Accepted: 31 August 1999     

Pacific Ocean sea-surface temperature variability and predictability of rainfall in the early and late parts of the Indian summer monsoon season

For central India and its west coast, rainfall in the early (15 May–20 June) and late (15 September–20 October) monsoon season correlates with Pacific Ocean sea-surface temperature (SST) anomalies in the preceding month (April and August, respectively) sufficiently well, that those SST anomalies can be used to predict such rainfall. The patterns of SST anomalies that correlate best include the equatorial region near the dateline, and for the early monsoon season (especially since ~1980), a band of opposite correlation stretching from near the equator at 120°E to ~25°N at the dateline. Such correlations for both early and late monsoon rainfall and for both regions approach, if not exceed, 0.5. Although correlations between All India Summer Monsoon Rainfall and typical indices for the El Ni?o-Southern Oscillation (ENSO) commonly are stronger for the period before than since 1980, these correlations with early and late monsoon seasons suggest that ENSO continues to affect the monsoon in these seasons. We exploit these patterns to assess predictability, and we find that SSTs averages in specified regions of the Pacific Ocean in April (August) offer predictors that can forecast rainfall amounts in the early (late) monsoon season period with a ~25% improvement in skill relative to climatology. The same predictors offer somewhat less skill (~20% better than climatology) for predicting the number of days in these periods with rainfall greater than 2.5?mm. These results demonstrate that although the correlation of ENSO indices with All India Rainfall has decreased during the past few decades, the connections with ENSO in the early and late parts have not declined; that for the early monsoon season, in fact, has grown stronger in recent decades.     

Tropical Stratospheric Circulation and Monsoon Rainfall

Interannual variability of both SW monsoon (June-September) and NE monsoon (October-December) rainfall over subdivisions of Coastal Andhra Pradesh, Rayalaseema and Tamil Nadu have been examined in relation to monthly zonal wind anomaly for 10 hPa, 30 hPa and 50 hPa at Balboa (9°N, 80°W) for the 29 year period (1958-1986). Correlations of zonal wind anomalies to SW monsoon rainfall (r = 0.57, significant at 1% level) is highest with the longer lead time (August of the previous year) at 10 hPa level suggesting some predictive value for Coastal Andhra Pradesh. The probabilities estimated from the contingency table reveal non-occurrence of flood during easterly wind anomalies and near non-occurrence of drought during westerly anomalies for August of the previous year at 10 hPa which provides information for forecasting of performance of SW monsoon over Coastal Andhra Pradesh. However, NE monsoon has a weak relationship with zonal wind anomalies of 10 hPa, 30 hPa and 50 hPa for Coastal Andhra Pradesh, Raya     

Variability of the TRMM-PR total and convective and stratiform rain fractions over the Indian region during the summer monsoon

Level 3 (3A25) TRMM Precipitation Radar (PR) data are used for 13 years period (1998–2010) to prepare climatology of TRMM PR derived near surface rain (Total rain) and rain fractions for the 4-months duration of Indian Summer Monsoon season (June–September) as well as for individual months. It is found that the total rain is contributed mostly (99 %) by two rain fractions i.e. stratiform and convective rain fractions for the season as well as on the monthly basis. It is also found that total rain estimates by PR are about 65 % of the gauge measured rain over continental India as well as on sub-regional basis. Inter-annual variability of TRMM-PR rain estimates for India mainland and its sub-regions as well as over the neighboring oceanic regions, in terms of coefficient of variability (CV) is discussed. The heaviest rain region over north Bay of Bengal (BoB) is found to have the lowest CV. Another sub-region of low CV lies over the eastern equatorial Indian ocean (EEIO). The CVs of total rain as well as its two major constituents are found to be higher on monthly basis compared to seasonal basis. Existence of a well known dipole between the EEIO and the north BoB is well recognized in PR data also. Significant variation in PR rainfall is found over continental India between excess and deficit monsoon seasons as well as between excess and deficit rainfall months of July and August. Examination of rainfall fractions between the BoB and Central India on year to year basis shows that compensation in rainfall fractions exists on monthly scale on both the regions. Also on the seasonal and monthly scales, compensation is observed in extreme monsoon seasons between the two regions. However, much less compensation is observed between the north BoB and EEIO belts in extreme rain months. This leads to speculation that the deficit and excess seasons over India may result from slight shift of the rainfall from Central India to the neighboring oceanic regions of north BoB. Contribution of stratiform and convective rain fractions have been also examined and the two fractions are found to contribute almost equally to the total rain. Results are further discussed in terms of the possible impact of the two rain fractions on circulation based on possible difference is vertical profiles of latent heat of two types of rain. Substantial differences in the lower and upper tropospheric circulation regimes are noticed in both deficit and excess monsoon months/seasons, emphasizing the interaction between rainfall (latent heat) and circulation.     

Detecting rainfall trends in twentieth century (1871–2006) over Orissa State, India

Climate change has affected the temperature and rainfall characteristics worldwide. However, the changes are not equal for all regions and have localized intensity and must be quantified locally to manage the natural resources. Orissa is an eastern state in India where agricultural activities mainly depends on the rainfall and thus face problems due to changing patterns of rainfall due to changing climate. In the present study, attempts were made to study temporal variation in monthly, seasonal and annual rainfall over the state during the period from 1871 to 2006. Long term changes in rainfall characteristics were determined by both parametric and non-parametric tests. The analysis revealed a long term insignificant decline trend of annual as well as monsoon rainfall, where as increasing trend in post-monsoon season over the state of Orissa. Rainfall during winter and summer seasons showed an increasing trend. Statistically monsoon rainfall can be considered as very dependable as the coefficient of variation is 14.2%. However, there is decreasing monthly rainfall trend in June, July and September, where as increasing trend in August. This trend is more predominant in last 10?year. Based on departure from mean, rainfall analysis also showed an increased number of dry years compared to wet years after 1950. This changing rainfall trend during monsoon months is major concern for the rain-fed agriculture. More over, this will affect hydro power generation and reservoir operation in the region.     

Characteristic changes in the long and short spells of different rain intensities in India

In this paper, changes in the long and short spells of different rain intensities are statistically analyzed using daily gridded rainfall data prepared by the India Meteorological Department for the period 1951–2008. In order to study regional changes, analyses have been conducted over nine selected agro-meteorological (agro-met) divisions, five homogeneous zones, and also over the whole of India. Rain events of different intensities with continuous rainfall of more than or equal to 4 days are classified here as long spells. Those with less than 4 days are termed as short spells. Those results which are statistically significant at 95% confidence level are discussed in this paper. Trend analysis shows that during the summer monsoon months of June to September, short spell rain events with heavy intensity have increased over India as a whole. On the other hand, long spell rain events with moderate and low intensities have decreased in numbers. Results further show that the contributions of long spell moderate and short spell low-intensity rain events to the total rainfall have decreased whereas the contributions of short spell heavy and moderate-intensity rain events to the total seasonal rainfall have increased. Percentage changes in various categories of long and short spells in the decade 1991–2000 compared with the earlier decade 1951–1960, highlight the maximum increase in heavy-intensity short spell category and decrease in moderate-intensity long spell category in India as a whole and in most of the homogeneous zones and agro-met divisions. The changes in different types of rain events differ in the six homogeneous zones and nine selected agro-met divisions. However, in three homogeneous zones and three agro-met divisions, the short spell heavy-intensity rain events dominate as in the entire country. There are also changes observed in the monthly occurrences of above categories of rain events during the 4 months of summer monsoon. Such results with details of changes in rain categories in different parts of India have important implications in agriculture sector in the country.     

A note on the break‐up of lakes and rivers as indicators of climate change

Abstract

A detailed examination has been made of the relationship between the space and time variations of the Indian summer monsoon rainfall and the equatorial eastern‐Pacific sea surface temperature (SST) anomaly in different seasons for the 108‐year period, 1871–1978. There is a strong inverse relationship between the two. The correlation coefficients between All‐India monsoon rainfall and the sea surface temperature anomaly for the concurrent season; June, July and August (JJA) and for the succeeding seasons; September, October and November (SON) and December, January and February (DJF) are consistently and highly significant. Even a random sample of 50 years gave values significant at the 0.1 percent level. The sliding window correlation analysis of 10‐, 20‐ and 30‐year widths indicates that the relationships between All‐India monsoon rainfall and the sea surface temperature anomaly for the concurrent JJA and the succeeding SON and DJF seasons exhibit stability and consistency in significance. For contiguous meteorological sub‐divisions west of longitude 80°E the relationship is highly significant for JJA and for succeeding SON and DJF seasons.     

Comparison of intra-seasonal forecast of Indian summer monsoon between two versions of NCEP coupled models

The real-time forecasting of monsoon activity over India on extended range time scale (about 3 weeks) is analyzed for the monsoon season of 2012 during June to September (JJAS) by using the outputs from latest (CFSv2 [Climate Forecast System version 2]) and previous version (CFSv1 [Climate Forecast System version 1]) of NCEP coupled modeling system. The skill of monsoon rainfall forecast is found to be much better in CFSv2 than CFSv1. For the country as a whole the correlation coefficient (CC) between weekly observed and forecast rainfall departure was found to be statistically significant (99 % level) at least for 2 weeks (up to 18 days) and also having positive CC during week 3 (days 19–25) in CFSv2. The other skill scores like the mean absolute error (MAE) and the root mean square error (RMSE) also had better performance in CFSv2 compared to that of CFSv1. Over the four homogeneous regions of India the forecast skill is found to be better in CFSv2 with almost all four regions with CC significant at 95 % level up to 2 weeks, whereas the CFSv1 forecast had significant CC only over northwest India during week 1 (days 5–11) forecast. The improvement in CFSv2 was very prominent over central India and northwest India compared to other two regions. On the meteorological subdivision level (India is divided into 36 meteorological subdivisions) the percentage of correct category forecast was found to be much higher than the climatology normal forecast in CFSv2 as well as in CFSv1, with CFSv2 being 8–10 % higher in the category of correct to partially correct (one category out) forecast compared to that in CFSv1. Thus, it is concluded that the latest version of CFS coupled model has higher skill in predicting Indian monsoon rainfall on extended range time scale up to about 25 days.     

A wind tunnel study of flows over idealised urban surfaces with roughness sublayer corrections

Variation of soil moisture during active and weak phases of summer monsoon JJAS (June, July, August, and September) is very important for sustenance of the crop and subsequent crop yield. As in situ observations of soil moisture are few or not available, researchers use data derived from remote sensing satellites or global reanalysis. This study documents the intercomparison of soil moisture from remotely sensed and reanalyses during dry spells within monsoon seasons in central India and central Myanmar. Soil moisture data from the European Space Agency (ESA)—Climate Change Initiative (CCI) has been treated as observed data and was compared against soil moisture data from the ECMWF reanalysis-Interim (ERA-I) and the climate forecast system reanalysis (CFSR) for the period of 2002–2011. The ESA soil moisture correlates rather well with observed gridded rainfall. The ESA data indicates that soil moisture increases over India from west to east and from north to south during monsoon season. The ERA-I overestimates the soil moisture over India, while the CFSR soil moisture agrees well with the remotely sensed observation (ESA). Over Myanmar, both the reanalysis overestimate soil moisture values and the ERA-I soil moisture does not show much variability from year to year. Day-to-day variations of soil moisture in central India and central Myanmar during weak monsoon conditions indicate that, because of the rainfall deficiency, the observed (ESA) and the CFSR soil moisture values are reduced up to 0.1 m³/m³ compared to climatological values of more than 0.35 m³/m³. This reduction is not seen in the ERA-I data. Therefore, soil moisture from the CFSR is closer to the ESA observed soil moisture than that from the ERA-I during weak phases of monsoon in the study region.

     

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     

Meteorological subdivisional-level extended range forecast over India during southwest monsoon 2012

The real-time multi-model ensemble (MME)-based extended range (up to 3 weeks) forecast of monsoon rainfall over India during the 2012 monsoon season is analyzed using the outputs of European Centre for Medium Range Weather Forecasts (ECMWF) monthly forecast coupled model, National Centre for Environmental Prediction (NCEP) Climate Forecast System version 2 coupled model and Japan Meteorological Agency (JMA)’ ensemble prediction system. Although the individual models show useful skill in predicting the extended range forecast of monsoon, the MME forecast is found to be superior compared to these. For the country as a whole, the correlation coefficient (CC) between the observed and MME forecast rainfall departure is found to be statistically significant (99 % level) at least for 2 weeks (up to 18 days). Over the four homogeneous regions of India, the CC is found to be significant (above 95 % level) up to 2 weeks except in case of northeast India, which shows significant CC for week 1 (days 5–11) only. On the meteorological subdivision level (India is divided into 36 meteorological subdivisions) the mean percentage of correct forecast is found to be much higher than the climatology forecast. Considering the complex problem of forecasting of monsoon in the extended range timescales, the MME-based predictions for 2–3 weeks provide skillful results and useful guidance for application in agriculture and other sectors in India.     

Optimization of the raingauges for a representative all-India and subdivisional rainfall series

Summary An objective approach similar to the forward selection of independent variables in the multiple linear regression has been attempted to optimize the network of raingauges for the summer monsoon rainfall (June–September total) series (1871–1984) of India as well as its 29 selected meteorological subdivisions prepared involving the data of 306 raingauges. For the all-India monsoon rainfall series twenty seven gauges entered the selection whose mean showed the correlation coefficient (CC) of 0.9869. Keeping in view the difficulties of getting data from all the 306 gauges, 35 India Meteorological Department (IMD) gauges with mean showing CC of 0.9898 have been identified for updating this series. The constructed all-India monsoon rainfall series for the period 1871–1992 using 35 selected observations is presented. It was interesting to note that the set of 35 gauges selected for the monsoon total has shown equally promising results for the all-India monsoon monthly (June–September) as well as the annual rainfall series.For the 29 subdivisional monsoon rainfall series, however, in total 188 IMD-gauges (62% of the total of 306 gauges) entered the selection. For 17 subdivisions the CC exceeded 0.98, for 3 subdivisions it varied between 0.97 and 0.98, for 5 subdivisions between 0.96 and 0.97 and for the remaining 4 subdivisions between 0.90 and 0.94. They showed equally encouraging results for the monsoon monthly and annual rainfall series for the different subdivisions.Limitations and implications of the optimization technique are also briefly discussed.With 9 Figures     

Development of an artificial neural network based multi-model ensemble to estimate the northeast monsoon rainfall over south peninsular India: an application of extreme learning machine

The south peninsular part of India gets maximum amount of rainfall during the northeast monsoon (NEM) season [October to November (OND)] which is the primary source of water for the agricultural activities in this region. A nonlinear method viz., Extreme learning machine (ELM) has been employed on general circulation model (GCM) products to make the multi-model ensemble (MME) based estimation of NEM rainfall (NEMR). The ELM is basically is an improved learning algorithm for the single feed-forward neural network (SLFN) architecture. The 27 year (1982–2008) lead-1 (using initial conditions of September for forecasting the mean rainfall of OND) hindcast runs (1982–2008) from seven GCM has been used to make MME. The improvement of the proposed method with respect to other regular MME (simple arithmetic mean of GCMs (EM) and singular value decomposition based multiple linear regressions based MME) has been assessed through several skill metrics like Spread distribution, multiplicative bias, prediction errors, the yield of prediction, Pearson’s and Kendal’s correlation coefficient and Wilmort’s index of agreement. The efficiency of ELM estimated rainfall is established by all the stated skill scores. The performance of ELM in extreme NEMR years, out of which 4 years are characterized by deficit rainfall and 5 years are identified as excess, is also examined. It is found that the ELM could expeditiously capture these extremes reasonably well as compared to the other MME approaches.     

IMPACT OF LATENT HEAT FLUX ON INDIAN SUMMER MONSOON DURING EL NI?O/LA NI?A YEARS

El Nio or La Nia manifest in December over the Pacific and will serve as an index for the forecasting of subsequent Indian summer monsoon,which occurs from June to mid-September.In the present article,an attempt is made to study the variation of latent heat flux (LHF) over the north Indian Ocean during strong El Nio and strong La Nia and relate it with Indian monsoon rainfall.During strong El Nio the LHF intensity is higher and associated with higher wind speed and lower cloud amount.During El Nio all India rainfall is having an inverse relation with LHF.Seasonal rainfall is higher in YY+1 (subsequent year) than YY (year of occurrence).However there is a lag in rainfall during El Nio YY+1 from June to July when compared with the monthly rainfall.     

Performance of GCMs for seasonal prediction over India��a case study for 2009 monsoon

The 2009 drought in India was one of the major droughts that the country faced in the last 100?years. This study describes the anomalous features of 2009 summer monsoon and examines real-time seasonal predictions made using six general circulation models (GCMs). El Ni?o conditions evolved in the Pacific Ocean, and sea surface temperatures (SSTs) over the Indian Ocean were warmer than normal during monsoon 2009. The observed circulation patterns indicate a weaker monsoon in that year over India with weaker than normal convection over the Bay of Bengal and Indian landmass. Skill of the GCMs during hindcast period shows that neither these models simulate the observed interannual variability nor their multi-model ensemble (MME) significantly improves the skill of monsoon rainfall predictions. Except for one model used in this study, the real-time predictions with longer lead (2- and 1-month lead) made for the 2009 monsoon season did not provide any indication of a highly anomalous monsoon. However, with less lead time (zero lead), most of the models as well as the MME had provided predictions of below normal rainfall for that monsoon season. This study indicates that the models could not predict the 2009 drought over India due to the use of less warm SST anomalies over the Pacific in the longer lead runs. Hence, it is proposed that the uncertainties in SST predictions (the lower boundary condition) have to be represented in the model predictions of summer monsoon rainfall over India.     

Variability of Summer Monsoon Rainfall in India on Inter-Annual and Decadal Time Scales

Indian Summer Monsoon Rainfall(ISMR)exhibits a prominent inter-annual variability known as troposphere biennial oscillation.A season of deficient June to September monsoon rainfall in India is followed by warm sea surface temperature(SST)anomalies over the tropical Indian Ocean and cold SST anomalies over the western Pacific Ocean.These anomalies persist until the following monsoon,which yields normal or excessive rainfall.Monsoon rainfall in India has shown decadal variability in the form of 30 year epochs of alternately occurring frequent and infrequent drought monsoons since1841,when rainfall measurements began in India.Decadal oscillations of monsoon rainfall and the well known decadal oscillations in SSTs of the Atlantic and Pacific oceans have the same period of approximately 60 years and nearly the same temporal phase.In both of these variabilities,anomalies in monsoon heat source,such as deep convection,and middle latitude westerlies of the upper troposphere over south Asia have prominent roles.     

Impact of the modulated annual cycle and intraseasonal oscillation on daily-to-interannual rainfall variability across monsoonal India

Variability of the Indian summer monsoon is decomposed into an interannually modulated annual cycle (MAC) and a northward-propagating, intraseasonal (30–60-day) oscillation (ISO). To achieve this decomposition, we apply multi-channel singular spectrum analysis (M-SSA) simultaneously to unfiltered daily fields of observed outgoing long-wave radiation (OLR) and to reanalyzed 925-hPa winds over the Indian region, from 1975 to 2008. The MAC is essentially given by the year-to-year changes in the annual and semi-annual components; it displays a slow northward migration of OLR anomalies coupled with an alternation between the northeast winter and southwest summer monsoons. The impact of these oscillatory modes on rainfall is then analyzed using a 1-degree gridded daily data set, focusing on Monsoonal India (north of 17°N and west of 90°E) during the months of June to September. Daily rainfall variability is partitioned into three states using a Hidden Markov Model. Two of these states are shown to agree well with previous classifications of “active” and “break” phases of the monsoon, while the third state exhibits a dipolar east–west pattern with abundant rainfall east of about 77°E and low rainfall to the west. Occurrence of the three rainfall states is found to be an asymmetric function of both the MAC and ISO components. On average, monsoon active phases are favored by large positive anomalies of MAC, and breaks by negative ones. ISO impact is decisive when the MAC is near neutral values during the onset and withdrawal phases of the monsoon. Active monsoon spells are found to require a synergy between the MAC and ISO, while the east–west rainfall dipole is less sensitive to interactions between the two. The driest years, defined from spatially averaged June–September rainfall anomalies, are found to be mostly a result of breaks occurring during the onset and withdrawal stages of the monsoon, e.g., mid-June to mid-July, and during September. These breaks are in turn associated with anomalously late MAC onset or early MAC withdrawal, often together with a large-amplitude, negative ISO event. The occurrence of breaks during the core of the monsoon—from late July to late August—is restricted to a few years when MAC was exceptionally weak, such as 1987 or 2002. Wet years are shown to be mostly associated with more frequent active spells and a stronger MAC than usual, especially at the end of the monsoon season. Taken together, our results suggest that monthly and seasonal precipitation?predictability is higher in the early and late stages of the summer monsoon season.     

Variability of rainfall and effective onset and length of the monsoon season over a sub-humid climatic environment

The study has analyzed the variability and trends in monthly, seasonal and annual rainfall and rainy days of four locations over different agro-ecological zones of Bihar, namely Samastipur (zone-I), Madhepura (zone-II), Sabour (zone-IIIA) and Patna (zone-IIIB). The Mann–Kendall nonparametric test was employed for detection of statistical significance and slopes of the trend lines were determined using the method of least square linear fitting. The variability and trends of onset of effective monsoon and length of monsoon period were also analyzed using the same method. The mean annual rainfall varies from 1137 mm at Patna to 1219 mm at Sabour. July is the rainiest month in all the zones followed by August. Maximum increase in annual rainfall was found at Sabour (40.1% of mean/30 years at 95% confidence level) and minimum for Patna (10.1% of mean/30 years). Significant increasing trend of rainfall during July, August and September at rates of 41.9, 83.2, and 112.7% of the mean/30 years, respectively has been noticed at Madhepura. Analysis of rainy days indicates that rainy days increased during winter and annually for all the sites. The mean effective onset of monsoon varies from 18th June at Sabour to 28th June at Patna. The trends in the date of effective onset of monsoon indicate that the date tends to be early in all the sites except Madhepura. But a significant delayed trend in the onset at a rate of 2.8% of the mean/30 years has been observed for Madhepura. The early trend of the effective onset of monsoon and increasing trends of length of monsoon season have been observed for Samastipur, Sabour and Patna.