A High Resolution Daily Gridded Rainfall Data for the Indian Region: Analysis of break and active monsoon spells

Transcription

1 A High Resolution Daily Gridded Rainfall Data for the Indian Region: Analysis of break and active monsoon spells M.Rajeevan, Jyoti Bhate, J.D.Kale National Climate Centre India Meteorological Department Pune, B.Lal India Meteorological Department New Delhi Address for correspondence Dr. M.Rajeevan National Climate Centre India Meteorological Department Pune INDIA

2 Abstract In this paper, we report the development of a high resolution (1 0 X 1 0 Lat/Long) gridded daily rainfall data set for the Indian region to fulfill the demands from the research community. We have considered daily rainfall data of 6329 stations for the interpolation analysis. However, there are only 1803 stations with a minimum 90% data availability during the analysis period ( ). We have used only those 1803 stations for interpolation in order to minimize the risk of generating temporal inhomogeneities in the gridded data due to varying station densities. For the analysis, we have followed the interpolation method proposed by Shepard (1968) based on the weights calculated from the distance between the station and the grid point and also the directional effects. Standard quality controls were performed before carrying out the interpolation analysis. Comparison with similar global gridded rainfall data sets revealed that the present rainfall analysis is better in accurate representation of spatial rainfall variation. The Global data sets underestimate the heavy rainfall along the west-coast and over Northeast India. The inter-annual variability of southwest monsoon seasonal (June- September) rainfall was found to be similar in all the data sets. Using this gridded rainfall data set, an analysis was made to identify the break and active periods during the southwest monsoon season (June to September). Break (Active) periods during the monsoon season were identified as the periods in which the standardized daily rainfall anomaly averaged over Central India ( N, E) is less than -1.0 (more than 1.0). The break periods thus identified for the period were found to be comparable with the periods identified by earlier studies. In contrary to a recent study by Joseph and Simon (2005), no evidence was found for any statistically significant trends in the number of break or active days during the period This gridded rainfall data set is available to the research community for noncommercial applications.

3 1. Introduction Information on spatial and temporal variations of rainfall is very important in understanding the hydrological balance on a global/regional scale. The distribution of precipitation is also important for water management for agriculture, power generation, and drought monitoring. In India, rainfall received during the south-west monsoon season (June to September) is very crucial for its economy. Real time monitoring of rainfall distribution on daily basis is required to evaluate the progress and status of monsoon and to initiate necessary action to control drought/flood situations. High resolution observed rainfall data are also required to validate regional/mesoscale models, and to examine and model the intra-seasonal oscillations like Madden-Julian Oscillation (MJO) over the Indian region. Gridded rainfall data sets are useful for regional studies on the hydrological cycle, climate variability and evaluation of regional models. In the recent years, there has been a considerable interest among different research groups in developing high resolution of gridded rainfall data sets. (Huffman et al. 1997, Xie and Arkin 1997, Dai et a l. 1997, Rudolf et al. 1998, Gruber et al.2000, New et al. 1999, Adler et al. 2003, Mitra et al. 2003, Chen et al. 2004, Beck et al. 2005). In this paper, we discuss the development of a high resolution (1 0 X 1 0 Latitude/Longitude) daily gridded rainfall data set for the Indian region for 53 years ( ). The details of data used, quality control adopted and the methodologies of interpolation are discussed. 2. Rainfall Data and Quality Control After the major drought of 1877 and the accompanying famine, India Meteorological Department established a large network of rain gauge stations, which provided a valuable source of data to analyze the space-time structure of the monsoon rainfall and its variability. With the introduction of telegraph system, daily rainfall and also other meteorological observations began to be collected and analysed on daily basis. India Meteorological Department over the years has maintained very high standards in monitoring the rainfall and other meteorological parameters over India with great care and accuracy (Sikka 2003).

4 A brief historical account and description of the rainfall data collection by the IMD are given by Walker (1910) and Parthasarathy and Mooley (1978). Using the IMD daily rainfall data of , Hartman and Michelsen (1989) converted into a gridded data set by grouping the station data into 1 0 Lat/Long grid boxes. Using this gridded rainfall data set, Hartman and Michelsen (1989), Krishnamurthy and Shukla (2000) and Krishnamurthy and Shukla (2005) studied the intra-seasonal and interannual variability of rainfall over India. For the present analysis, we have used the daily rainfall data archived at the National Data Centre, India Meteorological Department (IMD), Pune. IMD operates about 537 observatories, which measure and report rainfall occurred in the past 24 hours ending 0830 hours Indian Standard Time (0300 UTC). In addition, most of the state governments also maintain rain-gauges, for real time rainfall monitoring. IMD digitizes, quality controls and archives these data also along with rainfall data recorded at IMD observatories. Before archiving, IMD makes multi-stage quality control of observed values. We have considered rainfall data for the period for the present analysis. Standard quality control is performed before carrying out the analysis. First, station information (especially location) was verified, wherever the details are available. The precipitation data themselves are checked for coding or typing errors. Many such errors were identified, which were corrected by referring to the original manuscripts. For the period, , IMD has the rainfall records of 6329 stations, with varying periods. Out of these 6329 stations, 537 stations are the IMD observatory stations, 522 stations are under the Hydrometeorology programme and 70 are Agromet stations. Remaining stations are rainfall-reporting stations maintained by state governments. However, only 1803 stations out of 6329 stations had a minimum 90% data availability during the analysis period ( ). We have used only those 1803 stations for which a minimum 90% data are available for the analysis in order to minimize the risk of generating temporal inhomogeneities in the gridded data due to varying station densities. The network of stations (1803 stations) considered for this study is shown in Fig.1. The density of stations is not uniform throughout the country. Density is the highest over south Peninsula and is very poor over northern plains of India (Uttar Pradesh) and eastern parts of central India. Fig.2. shows the day to day

5 variation of number of rainfall stations, which were available for the analysis. On an average 1600 station data were available for the analysis. However, during the recent years, number of stations available for the analysis dropped significantly. This is due to the delay in digitizing and archiving the manuscripts which are received at IMD in a delayed mode. 3. Interpolation Method There are different methods of numerical interpolation of irregularly distributed data to a regular N-dimensional array (Thiebaux and Pedder (1987)). Bussieres and Hogg (1989) studied the error of spatial interpolation using four different objective methods. For application to the specific project grid, the statistical optimal interpolation technique displayed the lowest root mean square errors. This technique and Shepard OA, displayed zero bias and would be useful for areal average computations. The GPCC used a variant of the spherical-coordinate adaptation of Shepard s method (Willmott et al. 1985) to interpolate the station data to regular grid points. These regular points are then averaged to provide area mean, monthly total precipitation on 2.5 grid cells. New et al. (1999) used the thin plate splines proposed by Hutchinson (1998). Mitra et al (2003) used the successive correction method of Cressman (Krishnamurti et al. 1983). For the present analysis, we have used the interpolation scheme proposed by Shepard (1968). In the Shepard (1968) method, interpolated values are computed from a weighted sum of the observations. Given a grid point, the search distance is defined as the distance from this point to a given station. The interpolation is restricted to the radius of influence. For search distances equal to or greater than the radius of influence, the grid point value is assigned a missing code when there is no station location located within this distance. In this method, interpolation is limited to the radius of influence. A predetermined maximum value limits the number of data points used which, in the case of high data density, reduces the effective radius of influence. We have also considered the method proposed by Shepard to locally modify the scheme for including the directional effects and barriers. In this interpolation method, no initial guess is required. More details of the method are given in Shepard (1968) and Rajeevan et al. (2005).

6 We have interpolated station rainfall data into a rectangular grid of 35 X 32 grids for each day for the period The starting point of the grid is N and E. From this point, there are 35 points towards east and 32 points towards north. We have created one binary file for each year. For the leap year, we have created data for 366 days. 4. Comparison with Global data set After completing the rainfall analysis for the period , we have compared the IMD gridded data set with VASClimo data set, which is a global gridded rainfall data set. German Weather Service and Johann Wolfgang Goethe-University, Frankfurt jointly carried out a climate research project, named Variability Analysis of Surface Climate Observations (VASClimo), which was started in October Main objective of this project is the creation of a new 50-year precipitation climatology for the global land-areas gridded at three different resolutions (0.5 0 lat/lon, 1 0 lat/lon, and 2.5 o lat/lon) on the basis of quality controlled station data. More details of this new rainfall climatology are available in Beck et al. (2005). To compare the IMD analysis with the VASClimo data set, we have considered the VASClimo data of Since both these global data are available on monthly time scale, we have added IMD daily gridded rainfall data into monthly total before comparing with the global data sets. The results of comparison of the southwest monsoon seasonal (June to September) rainfall only are presented here. Fig.3. shows the spatial distribution of the seasonal (June-September) mean rainfall averaged for the period derived from the IMD gridded rainfall data set. The rainfall pattern suggests maximum along the west coast of India and NE India. Rainfall minimum is observed over NW India as well as over SE India. Fig.4.a shows the difference between the present analysis (IMD) and VASClimo data set and Fig 4.b shows the correlation coefficient between the IMD analysis and VASClimo data set. Over the most parts of India, the differences between the VASClimo data and IMD data are of the order of 50mm only. However, along the west coast of India, IMD rainfall values are more than the VASClimo values. However, the correlations between

7 VASClimo and IMD rainfall data are very large (exceeding even 0.6) over central and NW parts of India. Over Gujarat and central Peninsula, correlations exceed even 0.8. We have further compared the inter-annual variations of rainfall among the data set. For this purpose, area weighted rainfall for the southwest monsoon (June- September) season was calculated with all the two data sets. However, we have excluded NE parts of India for calculating the area weighted rainfall. The seasonal mean and standard deviation of rainfall of two data sets are given below: Rainfall Product IMD ( ) VASClimo ( ) Mean Rainfall Coefficient of variation mm 11.6 % mm 12.1% The two data set shows similar coefficient of variation, i.e.12%. Fig. 5 shows the interannual variation of southwest monsoon seasonal (June-September) rainfall calculated from IMD analysis as well as VASClimo data set. There is an excellent similarity in the inter-annual rainfall variation among the different data sets with all major drought and excess years were well captured the two data sets. The correlation coefficient between the IMD and VASClimo data sets is 0.95 (for the period ). 5. Analysis of Break Days The gridded daily rainfall data, presented in this report will be useful for many applications. Some of the possible applications are validation of general circulation and numerical weather prediction models and studies on intra-seasonal variability like active and break cycles. In the past, similar gridded data sets ( ) were used to examine the intra-seasonal variability (Hartmann and Michelsen 1989, Krishnamurthy and Shukla, 2003, Krishnamurthy and Shukla 2005) of the Indian summer monsoon. The present rainfall data set has been used to identify the active break periods during the southwest monsoon season. Long intense breaks are often associated with poor monsoon seasons, and they have a large impact on rainfed agriculture (Gadgil

8 and Joseph 2003). Traditionally monsoon breaks have been identified at IMD on the basis of surface pressure and wind patterns over the Indian region. The traditional breaks as followed by the India Meteorological Department have been documented by Ramamurthy (1969) and De et al. (1998). Recently, Gadgil and Joseph (2003) have examined the active and break periods ( ) using only rainfall data over the monsoon zone area covering the central parts of India. In the present analysis, the active and break periods during the southwest monsoon season have been identified in the following way. The area averaged daily rainfall time series for each year from has been prepared by simply taking arithmetic mean of all rainfall at all grid points over the central India ( o N, E). For each calendar day, the climatological mean and standard deviation of rainfall were calculated using the data of Then for each year, the area averaged daily rainfall time series has been converted to standardized rainfall anomaly time series, by subtracting the daily rainfall time series from the climatological mean and then dividing by its daily standard deviation. The standardized rainfall anomaly time series for the year 1988 (an excess monsoon year) and 2002 (a deficient monsoon year) are shown in Fig. 6. The break period has been identified as the period during which the standardized rainfall anomaly is less than 1.0, provided it is maintained consecutively for 3 days or more. For 2002, the break periods have been identified as 6 July to 17 July and 23 July to 31 July. Similarly, the active period has been identified as the period during which the standardized rainfall anomaly is more than 1.0, provided it is maintained consecutively for 3 days or more. We have examined the active and break periods for other years also based on the above criteria. We have listed the break days during July and August months only, provided it lasts consecutively for 3 days or more. The results giving the break periods during the period are given in Table-1. In the same Table, break days as defined by Ramamurthy (1969), De et al. (1998) and Gadgil and Joseph (2003) are also shown for comparison. The periods identified in this study are comparable with others, especially with Gadgil and Joseph (2003). To examine the spatial structure of the rainfall during the break phases, lagged composites of daily rainfall anomalies were constructed. The lagged break composites for lags ranging from -10 to +12 days are shown in Fig. 7. Lag 0 refers to the mid point of each break period. At lag -6, positive anomalies are seen along the foothills of

9 Himalayas, associated with the shift of the monsoon trough over that region. At lag -10 days, negative rainfall anomalies appear over east central parts of India, which increase and slowly expand northwestwards. At lag 0, large negative anomalies cover most parts of India except NE and SE parts of India. Positive anomalies over SE parts of India first develop around lag -8 and slowly expand in area. From lag +2 days, negative anomalies over central parts of India decrease both in area and magnitude. During this period, positive anomalies slowly move northwest wards. By Lag +12, large positive anomalies are seen along the west coast. With the revival of monsoon, at lag +12, positive anomalies appear over the coast of Orissa and adjoining area. Recently, Joseph and Simon (2005) reported that duration of break (weak) monsoon spells in a monsoon season has increased by 30% during the period Number of days with daily average rainfall less than 8mm/day (break or weak monsoon spells) increased and number of days with daily average rainfall more than 12 mm/day (active monsoon spells) decreased during the same period. These are alarm findings for a country whose food production and economy depend heavily on monsoon rainfall. To confirm the findings of Joseph and Simon (2005) and to further explore the issue, we have made an analysis with 53 years of IMD daily gridded rainfall data. Using the standardized daily rainfall anomaly averaged over the Central India ( N, 72 0 to 85 0 E), number break and active days during the period June to September were calculated for each year for the period The time series of number of break and active days for the period are shown in Fig. 8 and 9 respectively. The time series of break days has a very high negative correlation (-0.86) with southwest monsoon seasonal (June to September) rainfall. With the time series of active days, the corresponding correlation is In the study of Joseph and Simon (2005), the number of break and active days were identified using a different criterion as mentioned in the above paragraph for the period Their study revealed weaker correlations of and 0.54 respectively for number of break and active days. Thus the time series of break and active days prepared in this study is a more representative measure of monsoon activity during the season. However, Figs. 8 and 9 do not show any statistically significant trend either in number of break days or number of active days, which is in contrary from the results of Joseph and Simon (2005).

10 6. Conclusions In this paper, the results of the development of a high resolution (1 0 X 1 0 Lat/Long) daily gridded rainfall data are discussed. We have considered 1803 stations which had a minimum 90% of data availability during the analysis period We have considered the Shepard (1968) method with directional effects for the interpolation to 1 0 X 1 0 Latitude / Longitude regular grids. Before the interpolation, certain quality controls of the data were carried out. The gridded rainfall data set, thus developed, was compared with other similar global gridded rainfall data sets. The present IMD gridded rainfall analysis is better in accurate representation of rainfall over the Indian region, especially along the west-coast and NE India. The correlation coefficients between the IMD rainfall time series and other global data sets are more than The present rainfall data set is used to identify the active and break periods during the southwest monsoon season using an objective criterion based on standardized daily rainfall anomaly. The active and break periods thus identified were found comparable to the periods identified by earlier studies. In contrary to a recent study of Joseph and Simon (2005), the present study revealed no significant trend in the number of break and active days during the southwest monsoon season during the period It is believed that the present IMD gridded rainfall data set will be extensively used for many applications like validation of climate and numerical weather prediction models and also for studies on intra-seasonal variability and monsoon predictability studies. We shall be further updating the present rainfall analysis from 1901 onwards, so that more than 100 years of gridded daily rainfall data are available for the research community. Acknowledgements We have received encouragement and guidance from many scientists, during the process of development of this data set. We are grateful to all of them. The completion of this important project was a result of the encouragement and valuable guidance of Shri.S.R.Kalsi, ADGM (S), IMD and Dr N. Jayanthi, DDGM (WF) & LACD ADGM (R), IMD Pune. We also had fruitful discussions with Dr.J. Shukla, Dr. V. Krishnamurthy, Dr. B.N. Goswami, Dr. K. Rupa Kumar and Dr. Ravi Nanjundiah. We thank all of them for their guidance and encouragement. We also would like to thank Shri._G.S.Prakasa Rao, Director, IMD for his encouragement and greatly supporting us during this important project.

11 References Adler, R.F., Huffman, G.J., and Coauthors, 2003, The version-2 Global Precipitation Climatology Project (GPCC) Monthly precipitation analysis (1979-present), J.Hydrometeorology, 4, Beck,C., Grieser, J., and Rudolf, B., 2005, A New monthly precipitation climatology for the Global Land Areas for the Period , Climate Status Report 2004, German Weather Service, Offenbach, Germany, 10 pp. Bussieres, N and W.Hogg, 1989, The Objective analysis of daily rainfall by distance weighting schemes on a mesoscle grid, Atmosphere-Ocean, 3, Chen, M., P.Xie, J.E.Janowiak, and P.A.Arkin, 2002, Global Land Precipitation: A 50 yr monthly Analysis Based on Gauge Observations, J.Hydrometeorology, 3, Dai, A., Fung, I., and Genio, D.G., 1997, Surface observed global land precipitation variations during , J.Climate, 10, De, U.S., Lele,R.R., and Natu, J.C., 1998, Breaks in southwest monsoon, India Meteorological Department, Report no 1998/3. Gadgil, S., and Joseph, P.V., 2003, On breaks of the Indian monsoon, Proc.Indian.Acad.Sci. (Earth and Planet Sci), 112, 4, Gruber, A., X Su, M.Kanamitsu, and J.Schemm, 2000, The comparison of two merged rain gauge satellite precipitation data sets, Bull.Amer.Met.Soc., 81, Hartman, D.L., and M.L.Michelsen, 1989, Intraseasonal periodicities in Indian rainfall, J.Atmos.Sci., 46, Huffman, G.J., and Coauthors, 1997, The Global Precipitation Climatology Project (GPCC) combined precipitation data sets, Bull.Amer.Meteor.Soc., 78, Hutchinson, M.F,1998, Interpolation of rainfall data with thin plate smoothing splines: I two dimensional smoothing of data with short range correlation. Journal of Geographic Information and Decision Analysis 2(2): Joseph, P.V., and Anu Simon, 2005, Weakening trend of the southwest monsoon current through peninsular India from 1950 to the present, Current Science, 89, 4, Krishnamurti, T.N., S.Cocke, R.Pasch, and S.Low-Nam, 1983, Precipitation estimates from rain gauge and satellite observations, summer MONEX, FSU report 83-7, Department of Meteorology, The Florida State University, 373 pp. Krishnamurthy, V., and J.Shukla, 2000, Intra-seasonal and Inter-annual variability of rainfall over India, J.Climate, 13, Krishnamurthy, V., and J.Shukla, 2005, Intraseasonal and seasonally persisting patterns of Indian monsoon rainfall, COLA Technical Report, CTR 188, June 2005, pp41.

12 Mitra, A.K., M.Das Gupta, S.V.Singh and T.N.Krishnamurti, 2003, Daily rainfall for the Indian monsoon region from merged satellite and raingauge values: Large scale analysis from real time data, J.Hyrdrometeorology, 4, New, M, Hulme, M. and Jones, P.D., 1999, Representing Twentieth-Century Space-Time Variability. Part I: Development of a Mean Monthly Terrestrial Climatology, J.Climate, 1999, 12, Parthasarathy, B., and Mooley,D.A., Some features of a long homogenous series of Indian summer monsoon rainfall, Mon.Wea.Rev., 106, Rajeevan, M., Jyoti Bhate, J.D.Kale and B.Lal, 2005, Development of a high resolution daily gridded rainfall data for the Indian region, IMD Met Monograph No: Climatology 22/2005, pp27. (Available from National Climate Centre, IMD Pune. ncc@imdpune.gov.in). Ramamurthy, K., 1969, Monsoon of India: Some aspects of the Break in the Indian southwest monsoon during July and August, Forecasting Manual, 1-57, No. IV, 18.3, India Met.Dept, Poona, India. Rudolf, B., H.Hauschild, W.Ruth, and U.Schneider, 1994, Terrestrial precipitation analysis: Operational method and required density of point measurements, Global Precipitation and Climate Change, M.Desbois and E.Desalmand, Eds NATO ASI Series I, Vol.26, Springer-Verlag, Shepard, D., 1968, A two-dimensional interpolation function for irregularly spaced data, Proc ACM Nat.Conf, pp Sikka, D.R., 2003, Evaluation of monitoring and forecasting of summer monsoon over India and a review of monsoon drought of 2002, Proc.Indian Natn.Sci.Acad. 69, 5, Theibaux, H.J., and Pedder, M.A., 1987, Spatial Objective analysis with Applications in Atmospheric Science, Academic Press, London. Walker, G.T., 1910, On the meteorological evidence for supposed changes of climate in India, Indian. Meteor.Memo, 21, Part I, Willmott, C.J., C.M.Rowe, and W.D.Philpot, 1985, Small-scale climate maps: A sensitivity analysis of some common assumptions associated with grid-point interpolation and contouring, Amer.Cartographer, 12, Xie, P., and P.A.Arkin, 1997, Global Precipitation: A 17-year monthly analysis based on gauge observations, satellite measurements and numerical models outputs, Bull.Amer.Meteor.Soc., 78,

13 Table-1 Break days identified in the present analysis and previous studies YEAR BREAK DAYS JULY & AUGUST ( ) Ramamurthy (1969) Gadgil and Joseph up to 1967 Present analysis (2003) De et al. (2002) from 1968 to J, 24-30A 1-3 J, J, J, 24-29A 9-14J, 21-24J, 25-30A J, 10-13J, 27-30A 9-12J 9-15J J A 18-29J, 21-25A 22-29A J 22-29J A 23-26A 23-30A J 27-31J, 5-7A A A J, 30-31A 16-21J 20-23J J, 1-2A, 7-8A, 25-26A 18-22A 27-29A J, 22-23J 10-13J, 17-21J 16-18J, 21-23J J, 28J-3A 1-5 A J, 4-14A 6-8J, 4-15A 6-14J, 3-14A, A J, 22-31A 2-11J, 23-27A 2-13J, 24-31A J 7-10J 10-14J A 25-29A 25-31A A 17-20A, 25-27A 29-31A J, 23-26J 12-25J 14-19J, 23-26J J, 5-6A, 18-19A 17-20A 5-7 A, A J-3A 17J-3A 11-14J, 19J-3A J, 30J-1A 23J-1A J A, 29-31A 30-31A A J J, 21-22A J A 15-18A 14-21A J -

14 J, 15-31A 17-23J, 15-31A 2-7J, 12-31A J, 14-15A 17-20J A, 24-31A 26-30J, 23-27A A J - 1-9J, J J, 24-26A 22-25A 7-9J, J J A J, 23-25A 22-25A A, 24-27A J, 31J-2A, 22-31A 23-26A, 29-31A 3-5 J, 26-31A J, 23-24J, 31J-4A, 11-13A 28J-1A J, 30 J- 3A, 8-10A, 14-18A A 5-8J, 13-15A 15-17A J 10-12J, 29-31J 18-20J, 31J- 3A YEAR BREAK DAYS ( ) Present Analysis J J J, 8-14A J J A J 1-5 J, A, A J, 2-8A, A J, 22 J- 31 J

15 Fig.1. Locations of 1803 Rain gauge stations 2000 NO. OF STATIONS PER DAY ( ) (for stations with >=40 yrs of data) NO. OF STATIONS YEAR Fig.2. Number of stations per day available for the analysis

16 Fig. 3. Spatial pattern of southwest monsoon seasonal (June to September) mean rainfall Fig.4.a. The difference (in mm) between the IMD gridded rainfall data and VASClimo gridded rainfall data for the southwest monsoon season. Period:

17 Fig.4.b. Correlation Coefficient between the IMD rainfall data and VASClimo rainfall data during the southwest monsoon season (June-September). Period of analysis: COMPARISON OF IMD DATA FOR ALL INDIA ZONAL AVG. FOR JUNE-SEPT WITH VASCLIM ZONAL AVG ( ) IMD DATA VASCLIM % OF LPA YEAR Fig. 5. Interannual variation of southwest monsoon season (June- September) rainfall from the IMD gridded analysis and VASClimo analysis. Period:

18 3.0 STANDARDISED ANOMALY FOR DAILY RAINFALL : STD. ANOMALY Jun 8-Jun 15-Jun 22-Jun 29-Jun 6-Jul 13-Jul 20-Jul 27-Jul 3-Aug 10-Aug 17-Aug 24-Aug 31-Aug 7-Sep 14-Sep 21-Sep 28-Sep DATE 4.0 STANDARDISED ANOMALY FOR DAILY RAINFALL : STD. ANOMALY Jun 8-Jun 15-Jun 22-Jun 29-Jun 6-Jul 13-Jul 20-Jul 27-Jul DATE Fig.6. The standardized rainfall anomaly time series for the year a) 1988 and b) 2002 for the period 1 June to 30 September. 3-Aug 10-Aug 17-Aug 24-Aug 31-Aug 7-Sep 14-Sep 21-Sep 28-Sep

19 Lag (-2) Lag (0) Lag (+2) Lag (+4) Lag (+6) Lag (+8) Lag (+10) Lag (+12) Fig.7. Lagged Break phase composites of daily rainfall anomalies (mm/ day) for June-Sept season Lag 0 corresponds to the mid point of each break phase.

20 40 35 Number of Break Days: June-September y = x R 2 = C.C. With ISMR = days Year Fig.8. Time series of Active Days during the monsoon season ( ) Number of Active Days: June to September y = 0.03x R 2 = C.C. with ISMR = Days Year Fig.9. Time series of Break Days during the monsoon season ( )