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Development of an artificial neural network based multi-model ensemble to estimate the northeast monsoon rainfall over south peninsular India: an application of extreme learning machine

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Abstract

The south peninsular part of India gets maximum amount of rainfall during the northeast monsoon (NEM) season [October to November (OND)] which is the primary source of water for the agricultural activities in this region. A nonlinear method viz., Extreme learning machine (ELM) has been employed on general circulation model (GCM) products to make the multi-model ensemble (MME) based estimation of NEM rainfall (NEMR). The ELM is basically is an improved learning algorithm for the single feed-forward neural network (SLFN) architecture. The 27 year (1982–2008) lead-1 (using initial conditions of September for forecasting the mean rainfall of OND) hindcast runs (1982–2008) from seven GCM has been used to make MME. The improvement of the proposed method with respect to other regular MME (simple arithmetic mean of GCMs (EM) and singular value decomposition based multiple linear regressions based MME) has been assessed through several skill metrics like Spread distribution, multiplicative bias, prediction errors, the yield of prediction, Pearson’s and Kendal’s correlation coefficient and Wilmort’s index of agreement. The efficiency of ELM estimated rainfall is established by all the stated skill scores. The performance of ELM in extreme NEMR years, out of which 4 years are characterized by deficit rainfall and 5 years are identified as excess, is also examined. It is found that the ELM could expeditiously capture these extremes reasonably well as compared to the other MME approaches.

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Acknowledgments

The study was 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. Gridded rainfall data have been obtained from India Meteorological Department. We thank the IRI modeling and prediction group (USA) led by D. Dewitt for making six of their GCM-based seasonal forecasting systems available to us. The computing for GCM simulations made by IRI was partially sponsored by a grant from the NCAR Climate System Laboratory (CSL) program to the IRI. The IRI represents a cooperative agreement between the US National Oceanic and Atmospheric Administration (NOAA) Office of Global Programs and Columbia University. Sincere thanks are due to the two anonymous reviewers for constructive suggestions to enhance the quality of the manuscript.

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  1. Nachiketa Acharya & U. C. Mohanty

    Present address: School of Earth, Ocean and Climate Sciences, Indian Institute of Technology, Bhubaneswar, Odisha, India

Authors and Affiliations

  1. Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, New Delhi, 110016, India

    Nachiketa Acharya & U. C. Mohanty

  2. Electrical Engineering Departments, Indian Institute of Technology Delhi, New Delhi, India

    Nitin Anand Shrivastava & B. K. Panigrahi

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  1. Nachiketa Acharya
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Correspondence to Nachiketa Acharya.

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Acharya, N., Shrivastava, N.A., Panigrahi, B.K. et al. Development of an artificial neural network based multi-model ensemble to estimate the northeast monsoon rainfall over south peninsular India: an application of extreme learning machine. Clim Dyn 43, 1303–1310 (2014). https://doi.org/10.1007/s00382-013-1942-2

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