GOVERNMENT OF INDIAINDIA METEOROLOGICAL DEPARTMENT

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DEVELOPMENT OF A HIGH RESOLUTIONDAILY GRIDDED RAINFALL DATA

FOR THE INDIAN REGION

Met. Monograph Climatology No. 22/2005

M. Rajeevan, Jyoti Bhate, J.D. Kale & B. Lal

National Climate CentreIndia Meteorological Department

Pune 411 005, India.

2005

1

Summary

To fulfill the demand from the research community, a high resolution (10 X 10 Lat/Long)

gridded daily rainfall data set for the Indian region was developed. For the analysis, daily rainfall

data of 6329 stations were considered. However, there were only 1803 stations with a

minimum 90% data availability during the analysis period (1951-2003). We have used only

those 1803 stations for interpolation in order to minimize the risk of generating temporal

inhomogeneities in the data set due to varying station densities. The geographical area, 6.50

N to 37.50 N, 66.50E to 101.50 E was considered for interpolating the station rainfall data. For

the analysis, we have followed the interpolation method proposed by Shepard (1968). This

method is 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.

Quality of the present gridded rainfall analysis was compared with similar global gridded

rainfall data sets. Comparison revealed that the present gridded rainfall analysis is better in

more realistic representation of spatial rainfall distribution. The global data sets underestimated

the heavy rainfall along the west coast and over Northeast India. However, 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, statistical analyses of daily rainfall were carried out.

One such analysis was the identification of break periods during the southwest monsoon

season. Break periods were identified as the periods in which the standardized daily rainfall

anomaly averaged over the Central India (210- 270 N, 720 – 850 E) was less than -1.0. The

break periods thus identified for the period 1951-2003 were found to be comparable with the

periods identified by earlier studies. Further, no evidence was found for any significant trends in

number of break or active days during the period 1951-2003.

The present IMD gridded daily rainfall data set is available to the research community

for non-commercial purposes. The data set can be extensively used for several studies on

numerical model validation, intra-seasonal variability and monsoon predictability studies.

2

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.

Many modeling groups around the world are involved in simulating the mean monsoon

rainfall and its variability using general circulation models (GCM). These GCM results are to be

verified using observed rainfall data to understand the drawbacks in the model and to improve

its performance. High resolution observed rainfall data are also required to validate

regional/mesoscale models. Daily rainfall analyses are also required to examine and model the

intra-seasonal oscillations like Madden-Julian Oscillation (MJO) over the Indian region and

regional studies on the hydrological cycle and climate variability.

In the recent years, there has been a considerable interest among different research

groups in developing high resolution 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, Chen et al.

2002, Adler et al. 2003, Mitra et al, 2003, Beck et al. 2005). Better understanding of the global

and regional hydrologic cycles is at the heart of the World Climate Research Program (WCRP)

and Global Energy and Water Experiment (GEWEX) component. The Global Precipitation

Climatology Project (GPCP) is the WCRP GEWEX project devoted to producing community

analyses of global precipitation. The GPCP is an international project with input data sets and

techniques contributed to the monthly analysis by a number of investigators. Version 1 of the

GPCP monthly data set is described by Huffman et al. (1997) and the Version-2 data set is

described by Adler et al. (2003).

In this report, we discuss the development of a high resolution (10 X 10

Latitude/Longitude) daily gridded rainfall data set for the Indian region for 53 years (1951-2003).

The details of data used, quality control adopted and the methodologies of interpolation are

discussed in this report. The gridded data thus developed have been compared with other

similar global data sets. A few statistical analyses of gridded data also have been made to

demonstrate the use of this data set for monsoon research.

3

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.

Over India, monsoon variability has profound influence on agricultural sector, which in turn

affects the Indian economy. Using this large data source of rainfall, numerous research papers

have been published addressing the Indian monsoon variability, its teleconnection and

prediction.

Henry Blanford, the first head of the India Meteorological Department focussed on the

task of establishing a uniform system of rainfall monitoring and the careful scrutiny of the rainfall

records, received from the rain gauge network. 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).

A brief historical account and description of the rainfall data collection by the IMD are

given by Walker (1910) and Parthasarathy and Mooley (1978). Hartman and Michelsen (1989)

used the IMD daily rainfall data of 1901-1970, to convert into a gridded data set by grouping the

station data into 10 Lat/Long grid boxes. Using this gridded rainfall data set, Hartman and

Michelsen (1989) and Krishnamurthy and Shukla (2000) 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. Before archiving the data, IMD makes multi-stage quality control of

observed values. The major source of error is the systematic measuring error which results

from evaporation out of the gauge and aerodynamic effects, when droplets are drifted by the

wind across the gauge funnel. For this analysis, this error was not corrected in the rainfall

records.

4

We have considered rainfall data for the period 1951-2003 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, 1951-2003, 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

had a minimum 90% data availability during the analysis period (1951-2003). 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 variation of number of rainfall stations, which

were available for the analysis. On an average, about 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. Fig.3. shows the mean number of stations (averaged

for the period 1951-2003) available for the analysis in a 10 X 10 square grid. Number of stations

per grid (station density) is very large over south Peninsula. Rain-gauge density is

comparatively less over east central India (Chattisgarh) and northern plains of India.

3. Interpolation Method

Numerical interpolation of irregularly distributed data to a regular N-dimensional array is

usually called “Objective analysis” (OA). Thiebaux and Pedder (1987) grouped the OA

techniques as follows: empirical interpolation, statistical interpolation and function fitting. In

empirical interpolation, array values are computed from a distance-weighted sum of the data.

The weighting function is usually predetermined. Some of the empirical interpolation techniques

are iterative: array values are generated by successive approximation on the basis of errors of

back-interpolation to the original data locations. In statistical interpolation, array values are also

computed from a weighted sum of input data, except that weights are based on statistics of

spatial covariance of the data. In the third technique, mathematical functions or surfaces (for

5

e.g., thin plate splines, Hutchinson, 1998) are fitted to the data. The array values are computed

from these functions.

Bussieres and Hogg (1989) studied the error of spatial interpolation using four different

objective methods. The techniques considered are the empirical techniques of Barnes,

Cressman and Shepard and a Gandian-based statistical technique. 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 GPCP 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), which is explained in detail below:

It is assumed that a finite number of N of triplets (Xi, Yi, Zi) are given, where Xi and Yi

are the locational coordinates of the data point Di, and Zi is the corresponding data value. Data

point locations may not be coincident. An interpolation function Z= f(x,y) to assign a value to

any location P(x,y) in the plane is sought. This two dimensional interpolation function is to be

smooth (continuous and once differentiable), to pass through the specified points, and to meet

the user’s intuitive expectations about the phenomenon under investigation. A surface based on

a weighted average of the values at the data points, where the weighting was a function of the

distances, to those points, would satisfy the criteria.

Let Zi be the value at data point Di, and d(P,Di) be the Cartesian distance between P

and D, where the P is the reference point, d(P, Di) will be shortened to di. The interpolated

value at P using this first interpolation function is:

if di ≠ 0 for all Di (u>0)

∑

∑

=

−

=

−

= N

i

ui

N

ii

ui

d

Zd)P(f

1

1

6

if di =0 for some Di

Empirical tests showed that higher exponents (u>2) tend to make the surface relatively

flat near all data points, with very steep gradients over small intervals between data points.

Lower exponents produce a surface relatively flat, with short blips to attain the proper values at

data points. An exponent of u=2, not only gives seemingly satisfactory empirical results for

purposes of general surface mapping and description, but also presents the easiest calculation.

In Cartesian coordinates,

(di)-2 = 1/{(x-xi)2+(y-yi)2}

Since the above weighting function means that only nearby data points are significant in

computing any interpolated value, a considerable saving in computation could be effected by

eliminating calculations with distance data points. As their inclusion tended to make the surface

of higher order, extra points of inflection could also be eliminated by interpolating from nearby

points only. To select nearby points, either (1) an arbitrary distance criterion (all data points

within some radius r of the point P) or (2) an arbitrary number criterion (the nearest n data

points) could be employed. The former choice, though computationally easier, allowed the

possibilities that no data points, might be found within the radius r. A combination of the two

criteria combined their advantages. In order to maintain the interpolation work reasonably well,

a minimum of one data point was chosen. A maximum four was established to limit the

complexity and amount of computation required. Further, an initial search radius Dx is

established according to the overall density of data points. We have fixed the search radius Dx

as 2.0 degrees.

For a reference point P, new weighting factors Si= S(di) may be defined from the

following function:

di =0, : Station data used directly 0<di < Dx/3 : Weight based on 1/di

Dx/3 < di < Dx : Weight based on xx

i D/)DD( 4127 2−

di > Dx : Station data not used Where, Dx is the search radius.

In order to improve the interpolation, a direction factor, in addition to a distance factor

was needed in defining weightings. Intuitively this represented the “shadowing” of the influence

iZ)p(f =

7

of a data point from P by a nearer one in the same direction. A directional weighting term for

each data point Di near P was defined by

The cosine of the angle DiPDj can be evaluated by the inner product as

{(x-xi) (x-xj) + (y-yi) (y-yj)} / di dj

Since for all angles θ, -1 ≤ cos(θ) ≤ 1, it follows that 0≤ ti ≤2. If other data points Dj are in

roughly the same direction from P as Di, then the (1-cos) factors are near zero and ti is near

zero. If, on the other hand, the other data points are roughly opposite P from DI, then the (1-

cos) factors and hence ti are near 2. Counting direction, a new weighting function is defined:

Wi = (Si)2 X (1 + ti).

The latest version of the interpolation function is

if di ≠ 0 for all Di

if di = 0, for some Di

The starting point of the rectangular grid is 6.50 N and 66.5 0 E. From this point, there

are 35 points towards east and 32 points towards north. A separate binary file (35 X 32 grids)

was created for each year with different name. For the leap year, the binary file contained the

data for 366 days. The size of each data file (per year) is 1.6 Mb.

4. Comparison of IMD data set with other data sets

After completing the rainfall analysis for the period 1951-2003, we have compared the

IMD gridded data set with two global gridded rainfall data sets, the GPCP and VASClimo data

sets. The GPCP is an international project under the WCRP GEWEX programme devoted to

producing community analyses of global precipitation (Huffman et al. 1997, Rudolf et al. 1994,

[ ]∑

∑ −=

j

jiji S

PDDcos(St

1

∑∑=

i

ii

WZW

)P(f

iZ)p(f =

8

Adler et al. 2003). These data are available at 1o latitude X 10 longitude and 2.50 latitude X 2.50

longitude resolutions. We have considered the 10 latitude X 10 longitude monthly GPCP data for

the period 1986-2003. 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 2001. 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 (0.50 lat/lon, 10 lat/lon, and 2.5o 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 1951-2000. 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.

Fig. 4.a shows the difference between the present analysis (IMD) and GPCP data set

and Fig. 4.b shows the correlation coefficient between the IMD analysis and GPCP analysis.

Fig.5.a shows the difference between the present analysis (IMD) and VASClimo data set and

Fig 5.b shows the correlation coefficient between the IMD analysis and VASClimo data set.

The differences between IMD and GPCP data are positive along the west coast,

implying that GPCP data set underestimates the heavy rainfall amounts along the west coast.

Similarly over eastern parts of central India, GPCP data under estimates the heavy rainfall

amounts. Over most parts of central India, the differences are of the order of 50mm only. The

correlations between GPCP and IMD rainfall data exceed 0.6 over central and NW India.

Over the most parts of India, the differences between the IMD data and VASClimo data

are of the order of 50mm only. However, along the west coast of India, IMD rainfall values are

more than the VASClimo values. The correlations between 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 sets.

For this purpose, area weighted rainfall for the southwest monsoon (June-September) season

was calculated with all the three 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

three data sets are given below:

9

Rainfall Product Mean Rainfall Coefficient of variation

IMD

(1951-2003)

836.3 mm

11.6 %

GPCP

(1986-2003)

747.8 mm

12.1%

VASClimo

(1951-2000)

844.0 mm

12.1%

GPCP data show smaller mean rainfall compared to IMD and VASClimo data sets. But

all the three data set shows similar coefficient of variation, i.e.12%. Fig. 6 shows the interannual

variation of southwest monsoon seasonal (June-September) rainfall calculated from IMD

analysis as well as GPCP data set. The similar plot for the VASClimo data set is shown in Fig.

7. 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 by all the three data sets. The

correlation coefficient between the IMD and GPCP data sets is 0.94 (for the period 1986-2003)

and the same between the IMD and VASClimo data sets is 0.95 (for the period 1951-2000).

5. Analysis of Daily rainfall Data

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 (1901-1970) were used to examine the intra-

seasonal variability (Hartmann and Michelsen 1989, Krishnamurthy and Shukla, 2003) of the

Indian summer monsoon.

In this section, some results of a similar analysis are presented. Daily climatological

mean (mm/day) of rainfall for selected days (5 June, 15 June, 1 July, 31 July, 31 August and 20

September) are shown in Fig. 8. The climatological mean monsoon onset over Kerala is 1 June

with a standard deviation of 7 days. By 5 June, monsoon covers southwestern and

northeastern parts of India. Along the west coast and over NE India, rainfall exceeds 20

mm/day. By 15 June, monsoon advances further northwards along the west coast of India and

by 1 July, monsoon covers a major portion of the country except northwest parts of India.

Heavy rainfall (exceeding 20mm/day) is reported along the west coast and northeast India.

Over central parts of India, rainfall of the order of 10 mm/day is observed. On 31 August, good

rainfall amounts (of the order of 8-10 mm/day) are observed throughout the country. By last

10

week of September, monsoon is in the withdrawal stage. Rainfall is mostly confined over east

central India and south peninsula.

The daily climatological maps of rainfall for the entire year were also examined. As in

Krishnamurthy and Shukla (2000), we have considered four representative regions of India

(Southwest, Southeast, Central and Northwest) as shown in Fig.9. The daily mean

climatological rainfall charts in these four regions are shown in Fig.10. Rainfall amounts are the

highest in the southwest region, wherein July rainfall is of the order of 20 mm/day. Daily rainfall

variations averaged over all India is similar to the rainfall variations over the central India. Over

the country as a whole, rainfall peaks in July, which is of the order of 8 mm/ day. Rainfall

amounts are the lowest over NW India, where the southwest monsoon arrives late and

withdraws early. Over Southeast India, rainfall extends beyond September due to the onset of

northeast monsoon rainfall.

Fig.11 shows the spatial variation of southwest monsoon seasonal (June-September)

mean rainfall (calculated for the period 1951-2003) and Fig.12. shows the standard deviation of

southwest monsoon seasonal (June-September) rainfall during the same period. Rainfall is

maximum along the west coast and NE India. Another maximum rainfall zone is observed over

Chattisgarh and neighbourhood.

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 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

(1901-1989) 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 1951-2003 has been prepared by simply taking arithmetic mean of all rainfall

at all grid points over the central India (210 - 27oN, 720-850 E). For each calendar day, the

climatological mean and standard deviation of rainfall were calculated using the data of 1951-

11

2003. 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 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. 13. 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.

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 break and active periods during the period 1951-

2003 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).

Recently, Joseph and Simon (2005) reported that duration of break (weak) monsoon

spells in a monsoon season has increased by 30% during the period 1950-2002. Number of

days with daily average rainfall less than 8 mm/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 (210 – 270 N, 720 to 850 E), number break

and active days during the period June to September were calculated for each year for the

period 1951-2003. The time series of number of break and active days for the period 1951-

2003 are shown in Fig. 14 and 15 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 +0.62. 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 1950-2002. Their study revealed

weaker correlations of -0.58 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. 14 and 15 do not show any

12

significant trend either in number of break days or number of active days, which is contrary to

the results of Joseph and Simon (2005).

6. Conclusions

In this report, we discussed the results of the development of a high resolution (10 X 10

Lat/Long) daily gridded rainfall data. We have considered the period 1951-2003 for the

analysis. We have considered in all 6329 stations. However, only 1803 stations had a minimum

90% data availability during the analysis period (1951-2003). 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.

We have considered the Shepard (1968) method with directional effects for the interpolation to

10 X 10 Latitude / Longitude regular grids. Before the interpolation, we have carried out certain

quality controls of the data. The gridded rainfall data set, thus developed, was compared with

other similar global gridded rainfall data sets. The present IMD analysis shows more accurate

representation of rainfall over the Indian region, especially along the west-coast and central

India. The correlation coefficients between the IMD rainfall time series and other global data

sets are more than 0.90. The present rainfall data set is used to identify the active and break

periods during the southwest monsoon season using an objective criterion. The active and

break periods thus identified were found comparable to the periods identified by earlier studies.

Analysis of break days does not show any significant trends.

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, Dr. Ravi Nanjundiah, we thank them for their guidance. We also would like to thank

Shri._G.S.Prakasa Rao, Director, NDC, IMD Pune for his encouragement and greatly

supporting us during this important project.

13

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14

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15

Table-1

Break days identified in the present analysis and previous studies

YEAR BREAK DAYS – JULY & AUGUST (1951-1989)

Gadgil and Joseph (2003)

Ramamurthy (1969) up to 1967

De et al. (1998) from 1968 to 1989

Present analysis

1951 14-15J, 24-30A 1-3 J, 11-13 J, 15-17 J,

24-29 A

9-14 J, 21-24 J,

25-30 A

1952 1-3 J, 10-13J, 27-30A 9-12J 9-15J

1953 - 24-26J -

1954 22-29A 18-29J, 21-25A 22-29A

1955 24-25J 22-29J -

1956 23-30A 23-26A 23-30A

1957 28-29J 27-31J, 5-7A -

1958 - 10-14A -

1959 - 16-18A -

1960 20-24J, 30-31A 16-21J 20-23J

1961 - - -

1962 27-28J, 1-2A, 7-8A, 25-26A 18-22A 27-29A

1963 18-19J, 22-23J 10-13J, 17-21J 16-18J, 21-23J

1964 - 14-18J, 28J-3A 1-5 A

1965 7-11J, 4-14A 6-8J, 4-15A 6-14J, 3-14A, 17-19 A

1966 2-12J, 22-31A 2-11J, 23-27A 2-13J, 24-31A

1967 6-15J 7-10J 10-14J

1968 25-31A 25-29A 25-31A

1969 27-31A 17-20A, 25-27A 29-31A

1970 14-19J, 23-26J 12-25J 14-19J, 23-26J

1971 8-10J, 5-6A, 18-19A 17-20A 5-7 A, 18-20 A

1972 19J-3A 17J-3A 11-14J, 19J-3A

1973 24-26J, 30J-1A 23J-1A 24- 26 J

1974 24-26A, 29-31A 30-31A 29-31 A

1975 - 24-28J -

1976 3-4J, 21-22A - 1-4 J

1977 15-19A 15-18A 14-21A

1978 - 16-21J -

1979 2-6J, 15-31A 17-23J, 15-31A 2-7J, 12-31A

16

1980 17-20J, 14-15A 17-20J -

1981 19-20A, 24-31A 26-30J, 23-27A 26-31 A

1982 1-8J - 1-9J , 16-19 J

1983 8-9J, 24-26A 22-25A 7-9J , 14-16 J

1984 - 20-24J 12-14 A

1985 2-3J, 23-25A 22-25A 11-13 A, 24-27A

1986 1-4J, 31J-2A, 22-31A 23-26A, 29-31A 3-5 J, 26-31A

1987

16-17J, 23-24J, 31J-4A,

11-13A 28J-1A

16-18 J, 30 J- 3A,

8-10A, 14-18A

1988 14-17A 5-8J, 13-15A 15-17A

1989 30-31J 10-12J, 29-31J 18-20J, 31J- 3A

YEAR

BREAK DAYS

(1990-2003)

Present Analysis

1990 -

1991 2-8J

1992 4-10J

1993 19-23J, 8-14A

1994 -

1995 4-7 J

1996 3-5 J

1997 13-17A

1998 21-26J

1999 1-5 J , 18-20 A, 22-24 A

2000 22-24 J, 2-8A, 24-27A

2001 -

2002 5-16J, 22 J- 31 J

2003 -

17

Fig.1. Locations of 1803 Rain gauge stations

NO. OF STATIONS PER DAY (1951-2003)

0

200

400

600

800

1000

1200

1400

1600

1800

2000

1951

1953

1955

1957

1959

1961

1963

1965

1967

1969

1971

1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

YEAR

NO

. OF

STA

TIO

NS

Fig.2. Number of stations per day available for the analysis

18

Fig.3. Number of stations available for the analysis in a 10 X 10 square grid for which data are

available for at least 40 years. Total number of stations: 1803.

19

Fig.4.a. The difference (in mm) between the IMD gridded rainfall analysis and GPCP gridded

rainfall for the southwest monsoon season (June to September). Period: 1986-2003

Fig.4.b. Correlation Coefficient between the IMD rainfall data and GPCP rainfall data.

Period of analysis: June to September 1986-2003.

20

Fig.5.a. The difference (in mm) between the IMD gridded rainfall data and VASClimo gridded

rainfall data for the southwest monsoon season. Period: 1951-2000.

Fig.5.b. Correlation Coefficient between the IMD rainfall data and VASClimo rainfall data during

the southwest monsoon season (June-September). Period of analysis: 1951-2000.

21

COMPARISON OF IMD DATA FOR ALL INDIA ZONAL AVG. FOR JUNE-SEPTWITH GPCC ZONAL AVG (1986-2003)

40

50

60

70

80

90

100

110

120

130

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

YEAR

% O

F AVG

IMD DATAGPCC

Fig. 6. Interannual variation of southwest monsoon season (June- September) rainfall from the

IMD gridded analysis and GPCP analysis. Period: 1986-2003

COMPARISON OF IMD DATA FOR ALL INDIA ZONAL AVG. FOR JUNE-SEPTWITH VASCLIM ZONAL AVG (1951-2000)

60

70

80

90

100

110

120

130

140

1951

1953

1955

1957

1959

1961

1963

1965

1967

1969

1971

1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

YEAR

% O

F LP

A

IMD DATAVASCLIM

Fig. 7. Interannual variation of southwest monsoon season (June- September) rainfall from the

IMD gridded analysis and VASClimo analysis. Period: 1951-2000

22

Fig.8. Daily climatological mean (mm/day) rainfall for selected days (5 June, 15 June, 1 July, 31

July, 31 August and 20 September).

23

Fig. 9 Four representative regions of India (Southeast, Southwest, Central and Northwest India)

Fig.10. Daily rainfall climatology (mm/day) for the four representative regions

1

2

3

4

DAILY RAINFALL : CLIMATOLOGY : 5 DAY RUNNING MEAN (1951-2003)

0

3

6

9

12

15

18

21

24

Jan Feb Mar Apr May Jun Jul Aug Aug Sep Oct Nov Dec

MONTH

RA

INFA

LL (M

M/D

AY)

INDIABOX-1BOX-2BOX-3BOX-4

ALL INDIA

NW INDIA

CENTRAL INDIA

SOUTHEAST INDIA

SOUTHWESTINDIA

24

Fig.11. Spatial pattern of southwest monsoon seasonal (June to September) mean rainfall,

1951-2003 (Unit: mm/day)

Fig.12. Spatial pattern of standard deviation pf southwest monsoon seasonal (June to

September) rainfall, 1951-2003 (Unit: mm/day)

25

Fig.13. The standardized rainfall anomaly time series for the year a) 1988 and b) 2002 for the period 1 June to 30 September.

STANDARDISED ANOMALY FOR DAILY RAINFALL : 1988

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

1-Ju

n

8-Ju

n

15-J

un

22-J

un

29-J

un

6-Ju

l

13-J

ul

20-J

ul

27-J

ul

3-A

ug

10-A

ug

17-A

ug

24-A

ug

31-A

ug

7-S

ep

14-S

ep

21-S

ep

28-S

ep

DATE

STD

. AN

OM

ALY

STANDARDISED ANOMALY FOR DAILY RAINFALL : 2002

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

1-Ju

n

8-Ju

n

15-J

un

22-J

un

29-J

un

6-Ju

l

13-J

ul

20-J

ul

27-J

ul

3-A

ug

10-A

ug

17-A

ug

24-A

ug

31-A

ug

7-S

ep

14-S

ep

21-S

ep

28-S

ep

DATE

STD

. AN

OM

ALY

26

NUMBER OF BREAK DAYS : JUNE - SEPTEMBER

y = 0.0782x + 13.19R2 = 0.0194

0

5

10

15

20

25

30

35

4019

51

1953

1955

1957

1959

1961

1963

1965

1967

1969

1971

1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

YEAR

DA

YS

C.C. With ISMR = - 0.86

Fig.14. Time series of Break Days during the monsoon season (1951-2003)

NUMBER OF ACTIVE DAYS : JUNE - SEPTEMBER

y = 0.03x + 18.605R2 = 0.0043

0

5

10

15

20

25

30

35

40

1951

1953

1955

1957

1959

1961

1963

1965

1967

1969

1971

1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

YEAR

DA

YS

C.C. with ISMR = 0.62

Fig.15 Time series of Active Days during the monsoon season (1951-2003)