Forecasting of Indian Monsoon for Agricultural Decision
Making
Melissa E. Merrill, mem54@uakron.edu
The University of Akron, Akron, OH
Advisor: Balaji Rajagopalan, rajagopalan.balaji@colorado.edu
University of Colorado, Boulder, CO
Abstract
In India nearly every aspect of day to day life is critically impacted by the annual
monsoons. The rainfall from monsoons is at the root of agricultural growth, electricity
production, and thus the economic, social and political climate of the country. There has
been nearly exhaustive study of the annual behavior of monsoons and as a result
monsoons forecasting has become wholly reliant on a previously robust correlation
between the El Niño Southern Oscillation (ENSO) and monsoon behavior. However, this
correlation and subsequent forecasts do not provide predictions of the inevitable spatial
and intra-seasonal variation of monsoons which is highly relevant for agricultural
decision making, and this correlation is progressively deteriorating (Kumar et al.,
Science, 1999, 2006). Therefore, this study will attempt a forecasting model that provides
predictions more relevant to the agricultural decision making that actually effects the
lives of Indian people and discover and employ enhanced physical mechanisms for
explaining monsoon activity.
Motivation
Life for over a billion people in India depends unfailingly on the summer
monsoons of the June-September months. Most of the country receives nearly all of its
yearly rainfall from the monsoons and more than half of the country’s agricultural
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economy and electric power generation is fed directly by this rain water. Therefore, the
monsoons do not only determine food supply and exports for the Indian nation, but also
the cost of other basic living needs, economic health and growth of the country, and thus
also social and political dynamics (Kumar et al., PNAS, 2008). This massive impact has
spawned an immense number of studies on the physical mechanisms defining the success
and failure of Indian monsoons in an effort to provide useful forecasts. Such previous and
ongoing studies have allowed for a useful and growing understanding of the Indian
monsoon as a large scale event and its effects on India as a whole. However, a complete
understanding of the effects global climate events have on monsoon success is still not
available to the extent that can supply forecasts valuable for agricultural decision making.
In particular, monsoon rainfall varies both spatially (Figure 1) and intra-seasonally
(Figure 2) within an Indian summer and such variation on the scale of months or weeks
for a farmer in a specific location is, intuitively, more useful for assessing agricultural
and related financial options than an estimate of the overall rainfall of the season. For
example, The rainfall anomolies shown in Figure 1 for 1997, a normal rainfall years,
shows that infact the whole country did not see normal amounts of rainfall with a wet
season in the northwest and dry in the south and central regions. Similarly Figure 2 shows
that during any specific time period within the summer monsoon season, different
geographical regions of India are receiving different amounts of rainfall. Currently
neither a mechanistic or probabilistic understanding of the spatial or intra-seasonal
variation of monsoon rainfall has been studied.
The primary predictive tool currently use for forecasting is based on a correlation
between the El Niño Southern Oscillation (ENSO) and the summer monsoon rainfall.
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Typically, the nature of the Walker circulation, the cycle of condensation over the pacific
and subsidence over various locations in southern Asia, causes a diminishing of summer
rainfall over areas west of India during non-El Niño years, and that same diminishing
over India during El Niño year monsoon seasons when the Walker Circulation is shifted
east. Thus a single index describing the general severity of and El Niño event has
primarily been used to predict the risk of drought in India, but recently correlation
between monsoon rainfall and El Niño severity has been steadily disintegrating (Figure
3). Normal or even wet monsoons appear to be occurring even during El Niño years since
around 1980 (Figure 4). The two Science publications (Kumar et al., 1999, 2006) explore
this developing paradox presenting the influence of geographical variation of the Pacific
sea surface temperature (SST) not reflected in the currently indices used for forecasts and
the influence of the Eurasian warming trend witnessed in recent years. The land-sea
temperature gradient, the direct cause of Indian monsoon rainfall, is magnified by the
general warming trend favoring stronger summer rains (Kumar et al., Science 1999)
while a seemingly new “flavor” of El Niño event shifted from its normal centralized
Pacific location to a more extreme southeastern location causing the monsoon-weakening
subsidence of the Walker Circulation to occur east of India and have less effect on
monsoon success (Kumar et al., Science 2006).
Proposed Procedures
This study thus intends to use the augmented global climate characteristic
quantification tools discussed in the Kumar et al. studies to develop a new forecasting
tool providing predictions of intra-seasonal and spatial variability of monsoon rainfall to
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eventually be used in agricultural decision making. Using rainfall data from the Indian
Meteorological Department (IMD) collected daily at locations separated by 0.5 degrees,
or approximately 50km, between 1901 and 2006 this study will statistically quantify a
number of data variables, incorporating specification of both spatial and intra-seasonal
trends, to assess risk of agriculturally significant rainfall events. Then forecasting models
will be developed combining these statistical risks with the predictive power of enhanced
understanding of physical mechanisms driving monsoon events to provide a forecasting
model useful for agricultural decision making. Specifically, the IMD data will be used to
i.
quantify the number of wet and dry spells, defined by a statistically
significant number of consecutive wet and dry days respectively,
ii.
develop probability distributions for wet and dry spells of various lengths
to indicate risk,
iii.
determine the probability of transition between wet and dry spells,
iv.
determine the probability of extreme events, both wet and dry,
v.
determine significant variation of the preceding statistics geographically
and between agriculturally important intra-seasonal time periods,
vi.
and similarly analyze any other useful rainfall events as determined
throughout the study.
With these analyses this study can then deliver geographic maps of the seasonal statistical
risk of the determined critical rainfall events as well similar spatial risk maps for both
critical intra-seasonal time periods and specific predictive physical mechanisms as
determined. With such maps trends can be discovered and explored, and those planning
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agricultural activity could make educated decisions based on the prevailing global
climate condition, intra-seasonal period, and location.
Subsequently, this study will further improve predictive abilities for yearly,
geographical and sub-seasonal agricultural decision making by incorporating vital
physical mechanisms, determined in previous studies and with the previously mentioned
statistical analysis, with statistical risk based on past events. This will be done in the
following steps:
i.
rainfall events determined and quantified with the aforementioned steps
can then be correlated with potentially significant physical mechanisms
like the large scale climatological events mentioned previously, ENSO
activity and Pacific SST, to determine possible predictive qualities on
seasonal, and various spatial and intra-seasonal scales as,
ii.
combining these physical predictors and statistical risk analysis with
multi-modal ensemble forecasting techniques discussed in the first listed
work from Regonda et al. (Water Resources Research, 2006 (4)) to
achieve predict rainfall estimates,
iii.
And use a complimentary logistic regression technique relating predictors
and probabilities to provide probability of agriculturally significant
monsoon rainfall events (Regonda et al., Water Resources Research, 2006
(5)).
Then, perhaps beyond the scope of this study, these enhanced forecast techniques will
then be compiled in a user-friendly software tool for agricultural decision making on a
case by case basis.
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Timeline
An estimated timeline for these tasks is as follows. The next one to two weeks of
June will likely be spent with the basic IMD data analysis tasks for risk analysis for
rainfall variables discussed above followed by correlation analysis in search of physical
mechanisms for prediction, with results in the form of spatial maps of critical variables
by the end of June. July will likely be spent with further identification of global
climatological characteristics and implementation of the above stated techniques for
combining predictive factors, optimization of the forecasting model and evaluation of
skill of the model. August will be spent with finalization of the above tasks and
preparation of the final report and presentation.
References
1. Kumar K.K., K. Kamala, Balaji Rajagopalan, Martin P. Hoerling, Jon K.
Eischeid, S. K. Patwardhan, G. Srinivasan, B.N. Goswami1 and Ramakrishna
Nemani, The Once and Future Pulse of Indian Monsoonal Climate, Proceedings
of the Nation Academy of Sciences, 2008.
2. Kumar, K.K., Rajagopalan, B. and Cane, M., On the Weakening Relationship
Between the Indian Monsoon and ENSO, Science, 248: 2156-2159, 1999.
3. Kumar, K.K, Rajagopalan, B., Hoerling, M., Bates, G. and Cane, M., Unraveling
the Mystery of Indian Monsoon Failure During El Niño , Science, 314: 115-119,
2006.
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4. Regonda, S.K., B. Rajagopalan, M. Clark, and E. Zagona, A Multi-model
Ensemble Forecast Framework: Application to Spring Seasonal Flows in the
Gunnison River Basin , Water Resources Research, 42,W09404,2006.
5. Regonda, S.K., B. Rajagopalan, and M. Clark, A New Method to Produce
Categorical Streamflow Forecasts, Water Resources Research, 42,W09501, 2006.
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Figures
Figure 1. shows the percent departure from the overall 1997 summer rainfall mean for 35
meteorological subdivisions of India, indicating the necessity for a forecasting model that
accounts for spatial rainfall variability.
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Figure 2. Shows maps based on the weekly mean rainfall anomalies (percentage
departures from the long-term mean for the corresponding week) for 35 meteorological
subdivisions of India, indicating the necessity for a forecasting model that accounts for
intra-seasonal rainfall variability.
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Figure 3.
Is a demonstration of the disintegration of the ENSO-Indian monsoon
correlation since approximately 1980. In Figure 2A the solid line is the 21-year sliding
average rainfall for monsoon rainfall and the dashed line is a plot of the 21-year sliding
average NINO 3 index for corresponding years. Figure 2B shows the 21-year sliding
correlations of monsoon rainfall and the NINO3 index. (Kumar et al., Science, 1999)
Figure 4. Plots monsoon rainfall against the NINO2 index for 1871-2002 where green
indicates wet or normal years and red indicates drought. (Kumar et al., Science, 2006)
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