Relationship between ESI tendency and Indian monsoon rainfall: a possible mechanism

Kakade, S. B.; Kulkarni, Ashwini

doi:10.1002/asl.357

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…ESI-tendency from winter to spring is defined as the difference, i.e., April minus January ESI values. Kakade and Kulkarni (2012) pointed out that the relationship between ISMR and ESI-tendency is statistically significant in negative phase and is insignificant in positive phase of ESI-tendency. Further, they have also shown that the evolution of surface temperature from winter to spring is exactly opposite during contrasting phases of ESI-tendency.…”

Section: Datamentioning

confidence: 99%

“…Thus the behaviour of ESI from January to April is the critical factor between excess and deficit monsoon activity over India. Kakade and Kulkarni (2012) have shown that there is a clear line of demarcation in the relationship of ESI-tendency and Indian summer monsoon rainfall (ISMR) during contrasting phases of ESItendency. The relationship between ISMR and ESI-tendency is statistically significant in negative phase and is insignificant in positive phase of ESI-tendency.…”

Section: Lrf Of Summer Monsoon Rainfall Over Rc Regionsmentioning

confidence: 99%

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Seasonal prediction of summer monsoon rainfall over cluster regions of India

Kakade

Kulkarni

2017

J Earth Syst Sci

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Shared nearest neighbour (SNN) cluster algorithm has been applied to seasonal (June-September) rainfall departures over 30 subdivisions of India to identify the contiguous homogeneous cluster regions over India. Five cluster regions are identified. Rainfall departure series for these cluster regions are prepared by area weighted average rainfall departures over respective subdivisions in each cluster. The interannual and decadal variability in rainfall departures over five cluster regions is discussed. In order to consider the combined effect of North Atlantic Oscillation (NAO) and Southern Oscillation (SO), an index called effective strength index (ESI) has been defined. It has been observed that the circulation is drastically different in positive and negative phases of ESI-tendency from January to April. Hence, for each phase of ESI-tendency (positive and negative), separate prediction models have been developed for predicting summer monsoon rainfall over identified clusters. The performance of these models have been tested and found to be encouraging.

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Section: Datamentioning

confidence: 99%

Section: Lrf Of Summer Monsoon Rainfall Over Rc Regionsmentioning

confidence: 99%

Seasonal prediction of summer monsoon rainfall over cluster regions of India

Kakade

Kulkarni

2017

J Earth Syst Sci

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“…Therefore their influence on European temperature is different in two phases of ESI-tendency [10]. With this background, we have considered monthly evolution of AO pattern during contrasting phases of ESI-tendency which is important to understand the relationship of geophysical parameters over the Arctic with ISMR.…”

Section: Arctic Circulation and Ismr During Contrasting Phases Of Esimentioning

confidence: 99%

“…Thus ESI-tendency from January to April is a precursor for monsoon circulation over Indian subcontinent. Kakade and Kulkarni [10,11] have discussed the evolution of NAO and SO from winter to spring and their impact on European temperature changes during contrasting phases of ESI-tendency. The present study mainly deals with the relationships among AO, Arctic weather and ISMR on interannual scale during positive and negative ESI-tendency.…”

Section: Introductionmentioning

confidence: 99%

Association between Arctic Circulation and Indian Summer Monsoon Rainfall

Kakade¹,

Kulkarni²

2017

J Climatol Weather Forecasting

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The cluster regions for different meteorological fields at lower, mid and upper troposphere are identified using Shared Nearest Neighbor (SNN) algorithm. The relationships among weather conditions over the Arctic cluster regions, Indian Summer Monsoon Rainfall (ISMR) and Arctic oscillation are studied. It is observed that Arctic oscillation and mid tropospheric zonal wind anomaly over cluster region [60°N-70°N; 5°W-55°W] are directly correlated with each other in winter season and this relationship in turn influences ISMR. During contrasting phases of ESI (Effective Strength Index) tendency, the significant inverse relationship between zonal wind anomaly over Arctic clusters and ISMR show spatial and temporal variability. During positive (negative) ESI-tendency, lower tropospheric (upper tropospheric) temperature and pressure anomalies over respective cluster regions in Arctic have relationship with ISMR.

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“…Kakade and Kulkarni (, ) have demonstrated that evolution of ST and SLP from winter to spring changes with the phase of effective strength index (ESI) tendency. Kakade and Kulkarni () obtained two separate multiple regression equations for LRF of the ISMR during contrasting phases of the ESI tendency using independent cluster parameters of ST and SLP.…”

Section: Introductionmentioning

confidence: 99%

Prediction of summer monsoon rainfall over India and its homogeneous regions

Kakade

Kulkarni

2016

Meteorological Applications

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ABSTRACT:The coherent regions for various fields such as sea level pressure, temperature, geopotential height and zonal wind anomalies at the surface, 850, 500 and 200 hPa levels in pre-monsoon months (January through May) and seasons (winter, spring) have been identified by applying the shared nearest neighbour algorithm. The fields over the corresponding cluster regions could be possible predictors for Indian summer monsoon rainfall as well as the rainfall over various homogeneous regions of India. The time series have been constructed by averaging the parameters over the respective clusters. The relationship between these time series and the summer monsoon rainfall over India and its well-defined homogeneous regions over India, (northwest India, central northeast India, northeast India, west central India and peninsular India), has been examined during the positive and negative phases of effective strength index tendency using the simple technique of multiple regression. Along with the linear relationship, the non-linear relationships between the cluster parameters and the seasonal rainfall have also been considered. Independent cluster parameters have been selected by cross-validation procedure and the performance of each predictive model is tested. The extreme yearly rainfall departures over India are qualitatively well predicted by the model. Also, the unprecedented droughts over India in 2002 and 2009, where all earlier models have failed to forecast, are well predicted by the present model. The performances of models for summer monsoon rainfall prediction over homogeneous regions of India are convincing.KEY WORDS shared nearest neighbour (SNN); cross validation; effective strength index (ESI) tendency

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Relationship between ESI tendency and Indian monsoon rainfall: a possible mechanism

Cited by 6 publications

References 18 publications

Seasonal prediction of summer monsoon rainfall over cluster regions of India

Seasonal prediction of summer monsoon rainfall over cluster regions of India

Association between Arctic Circulation and Indian Summer Monsoon Rainfall

Prediction of summer monsoon rainfall over India and its homogeneous regions

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