Prediction of Indian summer monsoon rainfall using Nino indices: A neural network approach

被引:74
作者
Shukla, Ravi P. [1 ]
Tripathi, Krishna C. [1 ,4 ]
Pandey, Avinash C. [1 ,2 ,3 ]
Das, I. M. L. [1 ,2 ,3 ]
机构
[1] Univ Allahabad, K Banerjee Ctr Atmospher & Ocean Studies, Allahabad 211002, Uttar Pradesh, India
[2] Univ Allahabad, MN Saha Ctr Space Studies, Allahabad 211002, Uttar Pradesh, India
[3] Univ Allahabad, Dept Phys, Allahabad 211002, Uttar Pradesh, India
[4] Inderprastha Engn Coll, Dept MCA, Ghaziabad, India
关键词
Indian summer monsoon rainfall; Nino indices; ANN model; Regression model; SEA-SURFACE TEMPERATURES; TELECONNECTIONS; MODELS; ENSO; TIME;
D O I
10.1016/j.atmosres.2011.06.013
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
It is an established fact that sea surface temperature (SST) anomalies in the central-eastern Pacific associated with the El Nino-Southern Oscillation (ENSO) act as predominant forcing of the All India Rainfall Index variability. However, the same has been found to be difficult to simulate. In the present study, we have attempted to improve the seasonal forecast skill of the Indian Summer Monsoon Rainfall Index (ISMRI). Correlation analysis is clone to see the effect of SST indices of Nino-1 + 2, Nino-3, Nino-3.4 and Nino-4 regions on ISMRI with a lag period of 1-8 seasons. Significant positive correlations, with confidence level above 99%, are found between ISMRI and (i) Nino-3 index with a lag of 4 (June-July-August) and 5 (March-April-May) seasons, (ii) Nino-3.4 index with a lag of 4 and 5 seasons and (iii) Nino-4 index, with a lag of 5 seasons before the onset of monsoon. These SST indices are used for prediction of ISMRI using multiple linear regression and Artificial Neural Networks (ANNs) models. A comparative examination of the results suggests that the ANN model has better predictive skills than all the linear regression models investigated, implying that the relationship between the Nino indices and the ISMRI is essentially non-linear in nature. (C) 2011 Elsevier B.V. All rights reserved.
引用
收藏
页码:99 / 109
页数:11
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