A neurocomputing approach to predict monsoon rainfall in monthly scale using SST anomaly as a predictor

被引：29

作者：

Acharya, Nachiketa ^{[2
]}

Chattopadhyay, Surajit ^{[1
]}

Kulkarni, Makarand A. ^{[2
]}

Mohanty, Uma C. ^{[2
]}

机构：

[1] Pailan Coll Management & Technol, Dept Comp Applicat, Kolkata, India

[2] Indian Inst Technol, Ctr Atmospher Sci, New Delhi, India

来源：

ACTA GEOPHYSICA | 2012年 / 60卷 / 01期

关键词：

monthly rainfall forecast; sea surface temperature (SST); artificial neural network (ANN); INDIAN-SUMMER MONSOON; ARTIFICIAL NEURAL-NETWORK; SEA-SURFACE TEMPERATURE; CHAOS THEORY; OCEAN SST; OZONE; WEST; VARIABILITY; CIRCULATION; SIMULATION;

D O I：

10.2478/s11600-011-0044-y

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

A relationship between summer monsoon rainfall and sea surface temperature anomalies was investigated with the aim of predicting the monthly scale rainfall during the summer monsoon period over a section (80A degrees-90A degrees E, 14A degrees-24A degrees N) of eastern India that depends heavily upon the rainfall during the summer monsoon months for its agricultural practices. The association between area-averaged rainfall of June over the study zone and global sea surface temperature (SST) anomalies for the period 1982-2008 was examined and the variability of rainfall in monthly scale was calculated. With a view to significant variability in the rainfall in the monthly scale, it was decided to implement the artificial neural network (ANN) for forecasting the monthly scale rainfall using the SST anomalies as a predictor. Finally, the potential of ANN in this prediction has been assessed.

引用

页码：260 / 279

页数：20

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