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Spring • ANALYSIS • SOUTH-WEST MONSOON
Spring, Volume 2, Number 2, November 2013
SAVE NATURE, SAVE EARTH
☼
EISSN 2347 – 3851
ANALYSIS • SOUTH-WEST MONSOON
Spring
ISSN 2347 – 3800
The International Daily Journal for
Climate Change, Global Warming & Sustainability
Long Range Forecast of South-West Monsoon Rainfall for 2013 for Different
Regions of Gujarat
Chinchorkar SS1☼, Vaidya VB2, Vyas Pandey3
1. Assistant Professor, Polytechnic in Agricultural Engineering, AAU, Muvaliya Farm,Dahod-389151, India
2. Assistant Professor, Department of Agricultural Meteorology, BACA, ,AAU, Anand- 388 110, India
3. Professor and Head, Department of Agricultural Meteorology,BACA, ,AAU, Anand- 388 110, India
☼
Corresponding author: Assistant Professor, Polytechnic in Agricultural Engineering, AAU, Muvaliya Farm, Dahod-389151, Mail: [email protected]
Received 16 August; accepted 10 October; published online 01 November; printed 30 November 2013
ABSTRACT
The large spatial variability in monsoon rainfall over India demands for regional models for predicting the seasonal rainfall. Hence, models were
developed for predicting seasonal (June-September) rainfall of three regions (north, middle and south) of Gujarat using multiple regression
technique. The monthly weather data of 30 years of Anand (1980-2009), 22 years (1987-2009) of Navsari and 27 years (1983-2009) of SK
Nagar were used. The models were validated with independent data set of four year (2006-2011). The best models were selected based on
higher R
2
and lower model error. Four models were obtained; 2 for Anand (middle Gujarat) and one each for SK Nagar (north Gujarat) and
Navsari (south Gujarat). Anand (Model-1) has showed 8.5% error (2006 to 2011) while, model-2 shows -0.16% error. S K. Nagar station (North
Gujarat) has shown the -3.8% error. Navsari (South Gujarat) station has shown -5.6% error. Since all models have shown less than 10% error,
hence above operational models can be used for the rainfall forecast of monsoon. Results suggested that for Model 1 (November to March) is
predicting 1054.0 mm (Seasonal normal rainfall is 796.0 mm) higher rainfall than the normal by 32.4%. Model 2 (March to May) is predicting
1454.7mm is higher than normal by +82.7 % (Seasonal Normal 796 mm). For north Gujarat SK Nagar station is predicting 778.5mm rainfall
which is more by 31.5% than normal (591.1 mm) . In south Gujarat Navsari station is predicting 1646.6 higher (normal rainfall is 1363.0 mm) by
20.8%.
Key words: Multiple regression, rainfall forecasting, rainfall analysis, statistical model.
How to cite this article:
Chinchorkar SS, Vaidya VB, Vyas Pandey. Long Range Forecast of South-West Monsoon Rainfall for 2013 for Different Regions of Gujarat.
Spring, 2013, 2(2), 6-9
1. BACKGROUND
South-west monsoon rainfall (received during June to September) determines the fate of dry land farmers as well as
the status of national food security in India almost every year. The need for information about south-west monsoon
rainfall is great in these areas. An accurate long-range forecast can help farmers increasing agricultural productivity in
good rainfall years and negate the sudden downturns in agricultural production during anticipated drought years by
giving farmers sufficient time to adopt drought resistant crop varieties and appropriate crop, soil and water
management practices. The Indian meteorological Department (IMD) is now able to make all- India long- range
The India Meteorological Department (IMD) has been issuing operational long- range forecasts for summer
monsoon rainfall for more than one century. Since 1988, the operational forecasts have been issued using the 16
Parameter Power Regression and Parametric models for the summer monsoon rainfall over the country as a whole
Chinchorkar et al.
Long Range Forecast of South-West Monsoon Rainfall for 2013 for Different Regions of Gujarat,
Spring, 2013, 2(2), 6-9,
http://www.discovery.org.in/springjournal.htm
www.discovery.org.in
© 2013 discovery publication. All rights reserved
Page
parameters from 1988 onwards.
6
forecasts of south-west monsoon rainfall accurately using power regression model based on 16 regional and global
Spring • ANALYSIS • SOUTH-WEST MONSOON
Table 1
Operational Statistical Forecast System. Statistical Forecasting system for June to September rainfall forecast, the following predictors are used
Sr.
No
1.
2.
3.
4.
Name of the
station
Anand
(Model 1)
Anand
(Model 2)
S.K. Nagar
( North
Gujarat)
Navsari
( South
Gujarat)
Period Used for the
months
Predictor
Maximum temperature, minimum temperature, relative humidity afternoon, Wind Speed
and Bright Sun Shine Hour.
March
Maximum temperature, minimum temperature, relative humidity afternoon, Wind Speed
and Bright Sun Shine Hour.
April
Maximum temperature, minimum temperature, relative humidity afternoon, Wind Speed
and Bright Sun Shine Hour.
May (up to 22
nd
November
Maximum Temperature, Relative humidity afternoon, Minimum Temperature.
December
Maximum Temperature, Relative humidity afternoon, Bright Sun Shine Hour, Wind
Speed, Minimum Temperature.
January
Maximum Temperature, Bright Sun Shine Hour, Minimum Temperature.
February
Maximum Temperature, Bright Sun Shine Hour, Minimum Temperature.
March
Wind Speed, Relative Humidity-II
November
Wind Speed, Maximum Temperature, Relative Humidity-I, Relative Humidity-II
December
Wind Speed, Pan Evaporation, Relative Humidity-I, Relative Humidity-II
January
Pan Evaporation, Relative Humidity-I, Relative Humidity-II
February
Wind Speed, Relative Humidity-II
March
Pan Evaporation, Bright Sun Shine Hour, Relative Humidity-I, Relative Humidity-II
March
Bright Sun Shine Hour, Minimum Temperature, Relative Humidity-I, Relative Humidity-II,
Maximum Temperature
May)
0.85
0.84
April
May (up to 22
2
0.74
Relative humidity Afternoon, Wind Speed, Maximum Temperature, Bright Sun Shine
Hour, Wind Speed.
Bright Sun Shine Hour, Minimum Temperature, Relative Humidity-I, Relative Humidity-II
R
0.93
nd
May)
(Gowariker et al., 1991). For review of these operational forecasts and other related research efforts and problems,
Thapliyal and Kulshrestha (1992), Krishna Kumar et al. (1995) and Hastenrath (1995) may also be referred.
These forecasts have provided useful information on rainfall fluctuations and abnormalities which have been
helpful to the planners. However for a country with inherent spatial variability of monsoon rainfall there would always
be some areas of deficient rains even in the best monsoon years or some areas of flood even in worst monsoons
(Parthasarathy et al., 1993). Walker (1924), Shukla (1987) and Gregory (1989) suggested that rainfall over several
subdivisions of India should be grouped together to deduce area averages for large homogeneous regions. They
further showed that the consideration of the local distribution characteristics of seasonal rainfall in dividing the country
into homogeneous regions yielded better formulae for forecasting than when India was treated as one unit. Indian
meteorological department (IMD) was giving long-range rainfall forecast every year on the basis of 16 parameters and
now reduces 8 parameters since 2003. IMD’s 8 parameters were 1. Arabian sea (SST), 2.Eurasian Snow Cover, 3.
NW Europe temperature, 4. Nino 3 SST Anomaly (Previous year), 5. South Indian Ocean (SST Index), 6. East Asia
Pressure, 7. Northern hemisphere 50Hpa wind pattern, 8.Europe Pressure Gradient and July South Indian Ocean 50
Hpa zonal wind and Niño 3.4 SST Tendency are also considered (Rajeevan et al., 2004).
The northern districts have a rainfall varying from 51 to 102 cms.As the Tropic of Cancer passes through the
northern border of Gujarat, the state has an intensely hot or cold climate. But the Arabian sea and the Gulf of Cambay
in the west and the forest covered hills in the east soften the rigors of climatic extremes. Now, Prof. M.C. Varshneya,
Ex-Vice-Chancellor, AAU, Anand has given valuable guidance for developing New Operational Models based on
multiple regression technique for long-range rainfall forecast based on 25 years meteorological data has made to
forecast the rainfall of Anand Location (40 km Periphery) with the help of Department of Agricultural Meteorology,
Anand Agricultural University, Anand (Gujarat). The Long Period Average (LPA) of monsoon rainfall for Anand was
data for the same period for Anand have also been considered to examine the extent to which the long-range rainfall
forecast was relevant to micro level.
Chinchorkar et al.
Long Range Forecast of South-West Monsoon Rainfall for 2013 for Different Regions of Gujarat,
Spring, 2013, 2(2), 6-9,
http://www.discovery.org.in/springjournal.htm
www.discovery.org.in
© 2013 discovery publication. All rights reserved
Page
September) rainfall data for the years 1880 to 2005 (25 years) for the location Anand (Gujarat). The seasonal rainfall
7
calculated from 1980 to 2005 (25 years) for this purpose. The present investigation is based on seasonal (June to
Spring • ANALYSIS • SOUTH-WEST MONSOON
Table 2
Validation of models and Forecast for the year for 2013
Sr.No
Station
Rainfall (mm)
2006
2007
2008
2009
2010
2011
Average
Error (%)
2012
Anand
(model-1)
45-13 mw
Observed
1358.2
1140.7
957.4
380.9
922.3
877.8
939.6
882.7
1
813.0
-7.8
2
Anand
(model-2)
10-20 mw
3
4
S K.
Nagar
( North
Gujarat)
Navsari
(South
Gujarat)
Predicted
Deviation (%)
1266.6
-6.7
1079.5
-5.4
1251.6
30.7
338.4
-11.1
1363
47.7
916
-4.1
522.2
8.5
Observed
1358.2
1140.7
957.4
380.9
922.3
877.8
939.6
882.7
Predicted
1050.7
1004.9
804.5
492.9
1294
1102
479.0
1009.5
Deviation (%)
-22.6
-11.9
-15.9
29.4
40.3
-20.3
-0.17
14.3
Observed
1094.9
585
574
391.6
947
915.3
751.3
368.7
Predicted
1001.7
680.8
546.1
403.9
770
818.6
349.8
743.0
Deviation (%)
-8.5
16.4
-4.9
3.1
-18.6
-10.5
-3.8
-50.3
Observed
1916.2
1852.6
2063.2
1638.7
2033
1507.8
1835.3
Predicted
Deviation (%)
1657.5
-13.5
1913.8
3.3
2170.5
5.2
1732.3
5.7
1529
-24.7
1367
-9.3
861.4
-5.55
1244.0
1381.0mm
-9.9
Forecast
2013
1054.0 mm
Normal
(796 mm)
+32.4%
1454.7 mm
Normal
(796 mm)
+82.7%
778.5 mm
Normal
(591.9 mm)
+31.5%
1646.6 mm
Normal
(1363)
+20.8%
Anand Agricultural University has developed models for predicting seasonal rainfall of three regions (North,
Middle and South) of Gujarat using Multiple Regression Technique. The monthly weather data of 30 years of Anand
(1980-2009), 22 years (1987-2009) of Navsari and 27 years (1983-2009) of SK Nagar were used. The models were
2
validated with independent data set of four year (2006-2009). The best models were selected based on higher R and
lower model error. Four models were obtained; 2 for Anand (middle Gujarat) and one each for SK Nagar (north
Gujarat) and Navsari (south Gujarat). Different models explained 74 to 93% variability in seasonal rainfall with model
error ranging between -2.5 to 5.1%. During the validation period the performance of model was quite satisfactory with
model error ranging between -12.6 to 2.6%. All the models were used to predict the rainfall for 2010 season. The
model gave an idea about the possibility of getting deficient, normal or excess rain in mm which gives an idea about
drought.
From 2010 onwards, AAU has been using the following statistical models for preparing quantitative and
probabilistic forecasts of the south-west monsoon rainfall (June – September)
a) A 16- parameter statistical forecasting system requiring data up to Nov. to March, for the first forecast using model
– I while, a 15 parameter statistical forecasting system requiring data up to March for Anand station.
b) A 15- parameter statistical forecasting system requiring data up to Nov to March, for SK Nagar station.
c) A 15- parameter statistical forecasting system requiring data up to Nov to March, for Navsari station.
Although at many times the SW- monsoon rainfall of a country as whole had been normal but there have been
quite a large variation in regional rainfall distribution. IMD has predicted 828 mm rainfall in 2006 for a country as whole
(93% of normal), but Gujarat received 1072.8 mm rainfall which was 151.2 % as compared to the normal. While, in
2009 IMD predicted 881.1 mm (99% of normal.), rainfall but Gujarat received 613.7 mm (86.4% of normal) rainfall
which was a deficit year. So, it was felt necessary to give regional forecast/ prediction/ local station by using statistical
techniques. Thus, there is need to develop models for predicting regional rainfall.
2. METHODOLOGY
In 2010, Anand Agricultural University (AAU), Anand has introduced multiple regression parametric models for the
Long-range forecast of the south-west monsoon rainfall explored for Anand (Middle Gujarat), S.K. Nagar (North
Gujarat) and Navasari( South Gujarat). With this, it has become possible to issue the Long-range forecasts in two
stages (i.e. Preliminary and Final). The Preliminary Forecast is issued on 2
nd
of about 40 days. On 22
nd
April giving its users an extra lead time
may, AAU issues a final forecast for the Anand region. We have developed two models for
Anand. In view of its importance for agriculture, for the first time, the development of the new 16 parameters multiple
regression parametric models are discussed in this article.
predictors (monthly weather data) from November to March while Model 2 for Anand uses predictors (monthly
Chinchorkar et al.
Long Range Forecast of South-West Monsoon Rainfall for 2013 for Different Regions of Gujarat,
Spring, 2013, 2(2), 6-9,
http://www.discovery.org.in/springjournal.htm
www.discovery.org.in
© 2013 discovery publication. All rights reserved
Page
(i.e. Anand, Navsari, and SK.Nagar) by trial and error method (Varshneya et al., 2010). The model 1 for Anand uses
8
The daily data of three stations were converted into weekly means( Meteorological week).This weekly data is then
converted into monthly data from November to May (As per the IMD norms) were used to develop for the 3 stations
Spring • ANALYSIS • SOUTH-WEST MONSOON
weather data) from March to May period. The total number of predictors in model 1 was 16 while in Model 2, 15 in
number. For Navsari the best model obtained has 13 predictors from March to May period only. For SK Nagar with 15
predictors were from November to March period only. Model 1 for Anand has shown +8.5% average error when
validated with actual rainfall (2006-2011) whereas Model 2 has shown only –0.2% average error. For SK Nagar
average error was -3.5% while, for Navsari it was -5.5% when validated with actual rainfall during 2006-2011 (Table
2).
2.1. Average Error
Anand (Model-1) has shown 8.5% Error (2006 to 2011). Model-2 showed -0.16% average error. S K. Nagar (North
Gujarat) has shown the -3.8% error. For Navsari (South Gujarat) the error was -5.55%. Since all models have shown
less than 10% error, hence above operational models can be used for the rainfall forecast of monsoon.
2.2. Forecast for the Year-2013
The validated models were used to forecast the seasonal rainfall i.e. June-September for year 2013. It is seen that
both the models for Anand predicted higher than the normal rainfall (Table 1). The Model 1 (November to March) is
predicting 1054 mm rainfall (Seasonal normal rainfall is 796 mm) which is higher than the normal by 32.4 % (Above
Rainfall). Model 2 (March to May) is predicting 1454.7mm is higher than normal by 82.7% (Season Normal 796 mm).
For north Gujarat, SK Nagar station the rainfall prediction is 778.5 mm which is higher rainfall i.e. +31.5% more than
normal rainfall (591.1 mm). In south Gujarat Navsari station is predicting 1646.6 mm (higher than normal rainfall is
1363.0 mm) +20.8% more than normal rainfall.
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7.
Rajeevan M, Pai DS, Dikshit SK, Kelkar RR. IMD’s new
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Chinchorkar et al.
Long Range Forecast of South-West Monsoon Rainfall for 2013 for Different Regions of Gujarat,
Spring, 2013, 2(2), 6-9,
http://www.discovery.org.in/springjournal.htm
www.discovery.org.in
© 2013 discovery publication. All rights reserved