All India summer monsoon rainfall prediction using an artificial neural network

Sahai, A. K.; Soman, M. K.; Satyan, V.

doi:10.1007/s003820050328

Cited by 159 publications

(75 citation statements)

References 32 publications

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“…Over India, for example, interdecadal variability in associations between ENSO and atmospheric precursors of the monsoon, which are likely related to interdecadal variability in ENSO variance, have led to interdecadal variability in monsoon predictability (Parthasarathy et al, 1991;Hastenrath and Greischar, 1993;Annamalai, 1995;Krishna Kumar et al, 1999a,b;Sahai et al, 2000). This lack of robustness in predictor -predictand relationships may reflect an absence of explicit references to physical processes in the construction of statistical models for seasonal climate prediction.…”

Section: Predicting Climatementioning

confidence: 99%

Current approaches to seasonal to interannual climate predictions

Goddard¹,

Mason²,

Zebiak³

et al. 2001

Int. J. Climatol.

118

144

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This review paper presents an assessment of the current state of knowledge and capability in seasonal climate prediction at the end of the 20th century. The discussion covers the full range of issues involved in climate forecasting, including (1) the theory and empirical evidence for predictability; (2) predictions of surface boundary conditions, such as sea surface temperatures (SSTs) that drive the predictable part of the climate; (3) predictions of the climate; and (4) a brief consideration of the application of climate forecasts. Within this context, the research of the coming decades that seeks to address shortcomings in each area is described.

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Section: Predicting Climatementioning

confidence: 99%

Current approaches to seasonal to interannual climate predictions

Goddard¹,

Mason²,

Zebiak³

et al. 2001

Int. J. Climatol.

118

144

View full text Add to dashboard Cite

show abstract

“…It has been proven by several studies that the artificial neural networks are useful tools for predicting summer rainfall (e.g. Guhathakurta et al, 1999;Navone and Ceccatto, 1994;Sahai et al, 2003;Sahai et al, 2000;Venkatesan et al, 1997). Nevertheless, all of these studies deal with the Indian summer monsoon rainfall.…”

Section: Introductionmentioning

confidence: 99%

Predicting summer rainfall in the Yangtze River basin with neural networks

Hartmann

Becker

King

2007

Intl Journal of Climatology

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Summer rainfall in the Yangtze River basin is predicted using neural network techniques. Input variables (predictors) for the neural network are the Southern Oscillation Index (SOI), the East Atlantic/Western Russia (EA/WR) pattern, the Scandinavia (SCA) pattern, the Polar/Eurasia (POL) pattern and several indices calculated from sea surface temperatures (SST), sea level pressures (SLP) and snow data from December to April for the period from 1993 to 2002. The output variable of the neural network is rainfall from May to September for the period from 1994 to 2002, which was previously classified into six different regions by means of a principal component analysis (PCA). Rainfall is predicted from May to September 2002.The winter SST and SLP indices are identified to be the most important predictors of summer rainfall in the Yangtze River basin. The Tibetan Plateau snow depth, the SOI and the other teleconnection indices seem to be of minor importance for an accurate prediction. This may be the result of the length of the available time series, which does not allow a deeper analysis of the impact of multi‐annual oscillations.The neural network algorithms proved to be capable of explaining most of the rainfall variability in the Yangtze River basin. For five out of six regions, our predictions explain at least 77% of the total variance of the measured rainfall. Copyright © 2007 Royal Meteorological Society

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“…The model building process consists of four sequential steps: The Backpropagation Algorithm (BP) and the method of steepest descent, opened up application of Multilayered ANN for many problems of practical interest (Sahai et al, 2000(Sahai et al, , 2003Kamarthi and Pittner, 1999;Sejnowski and Rosenberg, 1987;Widrow and Lehr, 1990). A multilayered ANN contains three basic type of layers: input layer, hidden layer and output layer.…”

Section: Ann Based Prediction Of Summer-monsoon Temperature In Indiamentioning

confidence: 99%

“…This is because the atmosphere is unstable and the systems responsible for the events are the culmination of the instabilities and involve nonlinear interaction between different spatial scales from kilometres to hundreds of kilometres (Holton, 1972;Gardner and Dorling, 1998). Sahai et al (2000Sahai et al ( , 2003 showed that the variability in the Indian monsoon rainfall depends heavily upon the sea surface temperature anomaly over the Indian Ocean. As the extra tropical circulation anomalies display energy dispersion away from the region of anomalous tropical convection, they have been interpreted as a Rossby wave response to latent heat release is associated with the tropical convection.…”

Section: Introductionmentioning

confidence: 99%

“…Conversely, in regions of reduced convection and anomalous cooling, the tropical response is one of anomalous descent and upper-tropospheric convergent inflow. Thus, it is obvious that surface temperature influences the monsoon rainfall to a large extent (Gardner and Dorling, 1998;Sahai et al, 2000Sahai et al, , 2003. The present paper aims to look into the patterns of temperature over India during the monsoon months using Artificial Neural Network (ANN).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Neural Network Based Prediction of Maximum and Minimum Temperature in the Summer Monsoon Months over India

De¹,

Debnath²

2009

APR

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In the present paper, Artificial Neural Network has been adopted to forecast the maximum and minimum temperature monsoon months. The temperature of June, July, August has been predicted with the help of January to May temperature. In both the cases, maximum and minimum temperature is greatly predicted in the month of August. In the largest part of the cases, prediction error lies below 5%.In formulating the ANN-based predictive model, three-layer network has been constructed. The data published by Indian Institute of Tropical Meteorology (http://www.tropmet.res.in) are explored to develop the predictive model. The analysis is found to produce a forecast with small prediction error.

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All India summer monsoon rainfall prediction using an artificial neural network

Cited by 159 publications

References 32 publications

Current approaches to seasonal to interannual climate predictions

Current approaches to seasonal to interannual climate predictions

Predicting summer rainfall in the Yangtze River basin with neural networks

Artificial Neural Network Based Prediction of Maximum and Minimum Temperature in the Summer Monsoon Months over India

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