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Prediction of Monthly Summer Monsoon Rainfall Using Global Climate Models Through Artificial Neural Network Technique

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Abstract

The monthly prediction of summer monsoon rainfall is very challenging because of its complex and chaotic nature. In this study, a non-linear technique known as Artificial Neural Network (ANN) has been employed on the outputs of Global Climate Models (GCMs) to bring out the vagaries inherent in monthly rainfall prediction. The GCMs that are considered in the study are from the International Research Institute (IRI) (2-tier CCM3v6) and the National Centre for Environmental Prediction (Coupled-CFSv2). The ANN technique is applied on different ensemble members of the individual GCMs to obtain monthly scale prediction over India as a whole and over its spatial grid points. In the present study, a double-cross-validation and simple randomization technique was used to avoid the over-fitting during training process of the ANN model. The performance of the ANN-predicted rainfall from GCMs is judged by analysing the absolute error, box plots, percentile and difference in linear error in probability space. Results suggest that there is significant improvement in prediction skill of these GCMs after applying the ANN technique. The performance analysis reveals that the ANN model is able to capture the year to year variations in monsoon months with fairly good accuracy in extreme years as well. ANN model is also able to simulate the correct signs of rainfall anomalies over different spatial points of the Indian domain .

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Acknowledgements

This study is conducted as part of a research project entitled ‘Development and Application of Extended Range Weather Forecasting System for Climate Risk Management in Agriculture’, sponsored by the Department of Agriculture and Cooperation, Government of India. India Meteorological Department (IMD) is duly acknowledged for Gridded rainfall data. We are thankful to the IRI modeling and prediction groups (USA) led by D. Dewitt for making the GCMs available for users. The authors are also thankful to the anonymous reviewers for their valuable comments and suggestions by which the quality of the paper is enhanced. We also thank the NCAR Climate System Laboratory (CSL) program that partially sponsored the computations of GCMs.

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  1. School of Earth Ocean and Climate Sciences, Indian Institute of Technology Bhubaneswar, Bhubaneswar, Odisha, India

    Archana Nair & U. C. Mohanty

  2. Department of Civil Engineering, School of Infrastructure, Indian Institute of Technology Bhubaneswar, Bhubaneswar, Odisha, India

    Gurjeet Singh

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Nair, A., Singh, G. & Mohanty, U.C. Prediction of Monthly Summer Monsoon Rainfall Using Global Climate Models Through Artificial Neural Network Technique. Pure Appl. Geophys. 175, 403–419 (2018). https://doi.org/10.1007/s00024-017-1652-5

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