Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/490
Title: Southern Indian Ocean SST indices as early predictors of Indian summer monsoon
Authors: Tripathi, K. C.
Rai, Shailendra
Pandey, A. C.
Das, I. M. L.
Keywords: Artificial neural networks;Error back propagation;Monsoon;Southern Indian Ocean;Sea surface temperature indices;Indian summer monsoon;Predictors
Issue Date: Mar-2008
Publisher: CSIR
Abstract: Four indices of quarterly mean sea surface temperature (SST) values extracted for Southern Indian Ocean (SIO) region for which the maximum correlation with All India Rainfall Index (AIRI) was found with a lag up to 7 seasons w.r.t. the onset of monsoon. The Artificial Neural Network (ANN) technique has been used to study the predictability of the Indian summer monsoon with four indices individually as well as in various combinations. It has been found that two combinations of SST indices of SIO region, SIOI + ACCI and CSIOI + NWAI + SIOI + ACCI, show best predictive skill when used collectively. It has been found that the performance of the ANN model is better than the corresponding regression model in the prediction of ISMR indicating that the relationship between ISMR and SST indices are non-linear in nature.
Page(s): 70-76
URI: http://hdl.handle.net/123456789/490
ISSN: 0379-5136
Appears in Collections:IJMS Vol.37(1) [March 2008]

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