Empirical Prediction of the Summer Monsoon Rainfall over India

Shukla, J.; Mooley, D. A.

doi:10.1175/1520-0493(1987)115<0695:epotsm>2.0.co;2

Cited by 212 publications

(100 citation statements)

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“…Furthermore, 30% of the variation of the monthly number of rainy days at all stations may be explained by the occurrence frequency of the three wettest CPs of the 500 HPa level. Shukla and Mooley (1987) found a similar strong relation between the EL Nino Southern Oscillation (ENSO) and the temporal monsoon variability over the India.…”

Section: Discussionmentioning

confidence: 72%

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Modelling of monsoon rainfall for a mesoscale catchment in North-West India I: assessment of objective circulation patterns

Zehe

Singh

Bàrdossy

2006

Hydrol. Earth Syst. Sci.

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Abstract.Within the present study we shed light on the question whether objective circulation patterns (CP) classified from either the 500 HPa or the 700 HPa level may serve as predictors to explain the spatio-temporal variability of monsoon rainfall in the Anas catchment in North West India. To this end we employ a fuzzy ruled based classification approach in combination with a novel objective function as originally proposed by (Stehlik and Brdossy, 2002). After the optimisation we compare the obtained circulation classification schemes for the two pressure levels with respect to their conditional rainfall probabilities and amounts. The classification scheme for the 500 HPa level turns out to be much more suitable to separate dry from wet meteorological conditions during the monsoon season. As is shown during a bootstrap test, the CP conditional rainfall probabilities for the wet and the dry CPs for both pressure levels are highly significant at levels ranging from 95 to 99%. Furthermore, the monthly CP frequencies of the wettest CPs show a significant positive correlation with the variation of the total number of rainy days at the monthly scale. Consistently, the monthly frequencies of the dry CPs exhibit a negative correlation with the number of rainy days at the monthly scale. The present results give clear evidence that the circulation patterns from the 500 HPa level are suitable predictors for explaining spatio-temporal Monsoon variability. A companion paper shows that the CP time series obtained within this study are suitable input into a stochastical rainfall model.

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Section: Discussionmentioning

confidence: 72%

“…Shukla and Mooley (1987) used the EL Nino Southern Oscillation (ENSO) to explain 30% of the temporal monsoon variability over the Indian subcontinent. Early attempts to statistically link Eurasion snowfall in winter to the strength of the Indian monsoon did not yield convincing results (Dickson, 1984;Bamzai and Shukla, 1999).…”

Section: Introductionmentioning

confidence: 99%

Modelling of monsoon rainfall for a mesoscale catchment in North-West India I: assessment of objective circulation patterns

Zehe

Singh

Bàrdossy

2006

Hydrol. Earth Syst. Sci.

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“…The variability and predictability hinges on two widely regarded influences: local land warming and the distant El Niño-Southern Oscillation (ENSO) [e.g., Charney and Shukla, 1981;Shukla and Mooley, 1987]. Most statistical methods for forecasting monsoon rainfall, used by India Meteorological Department, use predictors that are in one way or another related to these influences [Krishna Kumar et al, 1995].…”

Section: Introductionmentioning

confidence: 99%

“…Linear regression is the standard method used, but nonlinear and multimodel combination methods have been proposed [Rajeevan et al, 2007], and regardless, the skills are limited by the strength of the predictors to monsoon rainfall. Although a combination of such predictors once engendered optimism [e.g., Shukla and Mooley, 1987], marked variations in correlations with ENSO indices in recent years cast doubt on such predictors [e.g., Krishna Kumar et al, 1999Kumar et al, , 2006. In general, such predictors when applied to the entire monsoon season account for less than 20% of the variance in observed monsoon rainfall over India [e.g., Delsole and Shukla, 2002].…”

Section: Introductionmentioning

confidence: 99%

“…Amounts of summer monsoon rainfall are widely known to correlate with sea surface temperatures (SSTs) in the tropical Pacific, and specifically with ENSO indices [e.g., Shukla and Mooley, 1987;Webster et al, 1998]. Yet the strong correlation that existed before~1980 has subsequently shrunk and become insignificant [Krishna Kumar et al, 1999].…”

Section: Introductionmentioning

confidence: 99%

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Combining regional moist static energy and ENSO for forecasting of early and late season Indian monsoon rainfall and its extremes

Rajagopalan

Molnár

2014

Geophysical Research Letters

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We exploit El Niño-Southern Oscillation (ENSO) indices and moist static energy of surface air over the Indian subcontinent and surroundings as predictors of monsoon rainfall over India during early and late seasons, defined here as 20 May to 15 June and 20 September to 15 October, respectively. Although these seasons contribute only~22% of the entire seasonal rainfall, they clearly affect planning of agriculture and water resources. A simple, nonlinear, statistical model applied to these predictors accounts for~40% and 45% of the observed variance of early and late season rainfall, respectively, and similar fractions for 3 day maximum rainfall intensity. Forecasted average and 3 day maximum rainfall at grid points covering India show greatest success over central India during the early season and over west central, northwestern, and northern India during the late season, regions where agriculture dominates land use. These predictors, however, offer virtually no predictability of peak season rainfall.

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Seasonal Variations of the Relationship Between Some Enso Parameters and Indian Rainfall

Prasad

Singh

1996

Int. J. Climatol.

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Canonical correlation analysis is used to examine the seasonal relationship between ENSO and Indian rainfall by analysing their 12 monthly values for an 80‐year period. Three ENSO indices are considered. These ENSO indices are the Darwin surface pressure, the sea‐surface temperature of the central and eastern equatorial Pacific, and rainfall of central equatorial Pacific islands (hereafter denoted as DSP, SST, and RAIN respectively). The ENSO indices are also analysed for relationships between themselves. The analysis reveals that the seasonal variations of these ENSO indices are highly intercoupled with no lag. These indices show the minimum association during April and the maximum after the monsoon season. Further, the seasonal variation of the Indian rainfall is found to be better associated with the seasonal variations of SST as compared with that of DSP or RAIN. This association is at its strongest during the period August–October. An apparent reversal in the relationship between ENSO and Indian rainfall is also observed from summer to winter. The warm ENSO years are associated with weak summer monsoon rainfall and at the same time high winter monsoon rainfall.

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Empirical Prediction of the Summer Monsoon Rainfall over India

Cited by 212 publications

References 0 publications

Modelling of monsoon rainfall for a mesoscale catchment in North-West India I: assessment of objective circulation patterns

Modelling of monsoon rainfall for a mesoscale catchment in North-West India I: assessment of objective circulation patterns

Combining regional moist static energy and ENSO for forecasting of early and late season Indian monsoon rainfall and its extremes

Seasonal Variations of the Relationship Between Some Enso Parameters and Indian Rainfall

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