Prediction of summer monsoon rainfall over India and its homogeneous regions

Kakade, S. B.; Kulkarni, Ashwini

doi:10.1002/met.1524

Cited by 10 publications

(8 citation statements)

References 33 publications

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“…The geo-potential height of north-western and central Russia above the Indian subcontinent and the region of Artic Ocean above the longitudinal stretch of Russia are ascertained as the predictors of north-west India. The same region of Artic Ocean over Russia was also identified by Kakade and Kulkarni (2016) for influencing north-west Indian monsoon. A combination of the regions of the North Pacific Ocean and South Atlantic Ocean is also evaluated as an important monsoon predictor.…”

Section: Predictors Identified For Different Homogenous Regions Of Indiamentioning

confidence: 78%

“…Regional prediction of the Indian subcontinent has also been attempted with the application of canonical correlation analysis (Sinha et al 2013). Recently, Indian Institute of Tropical Meteorology, Pune has performed the aggregate Indian monsoon prediction along with the prediction of its regional sub-divisions using shared nearest neighbour clustering approach (Kakade and Kulkarni 2016). Their method has engrossed the non-linear connections between seasonal monsoon and cluster parameters along with the linear relations, and has provided promising prediction accuracies.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning for predicting the monsoon over the homogeneous regions of India

2017

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Indian monsoon varies in its nature over the geographical regions. Predicting the rainfall not just at the national level, but at the regional level is an important task. In this article, we used a deep neural network, namely, the stacked autoencoder to automatically identify climatic factors that are capable of predicting the rainfall over the homogeneous regions of India. An ensemble regression tree model is used for monsoon prediction using the identified climatic predictors. The proposed model provides forecast of the monsoon at a long lead time which supports the government to implement appropriate policies for the economic growth of the country. The monsoon of the central, northeast , northwest , and south-peninsular India regions are predicted with errors of 4.1%, 5.1%, 5.5%, and 6.4%, respectively. The identified predictors show high skill in predicting the regional monsoon having high variability. The proposed model is observed to be competitive with the state-of-the-art prediction models.

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Section: Predictors Identified For Different Homogenous Regions Of Indiamentioning

confidence: 78%

Section: Introductionmentioning

confidence: 99%

Deep learning for predicting the monsoon over the homogeneous regions of India

2017

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“…7. Form clusters from the core points by averaging the grids in the cluster: For detailed description of the method, please refer Kakade and Kulkarni (2016).…”

Section: Shared Nearest Neighbour (Snn)mentioning

confidence: 99%

“…This algorithm identifies the clusters of different shapes, sizes and densities. Kakade and Kulkarni (2016) described the SNN algorithm to obtain cluster regions of various meteorological fields. In this paper, an attempt has been made to identify the regions of India depending upon monsoon rainfall over sub-divisions of India.…”

Section: Introductionmentioning

confidence: 99%

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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“…Thus evolution of AO pattern, in contrasting phases of ESI-tendency, is exactly opposite. The relationship between predictor and ISMR drastically changes with the phase of ESI-tendency [31]. So here we have considered association of CTSs with ISMR during opposite phases of ESItendency.…”

Section: Arctic Circulation and Ismr During Contrasting Phases Of Esimentioning

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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Prediction of summer monsoon rainfall over India and its homogeneous regions

Cited by 10 publications

References 33 publications

Deep learning for predicting the monsoon over the homogeneous regions of India

Deep learning for predicting the monsoon over the homogeneous regions of India

Seasonal prediction of summer monsoon rainfall over cluster regions of India

Association between Arctic Circulation and Indian Summer Monsoon Rainfall

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