A soft-computing ensemble approach (SEA) to forecast Indian summer monsoon rainfall

Nisha Kurian, T. Venugopal, Jatin Singh, M. M. Ali

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Agriculture is the backbone of the Indian economy and contributes ∼16% of gross domestic product and about 10% of total exports. Hence, accurate and timely forecasting of monthly Indian summer monsoon rainfall is very much in demand for economic planning and agricultural practices. Several methods and models, comprising dynamic and statistical models and combinations of the two, exist for monsoon forecasting. Here, a multi-model ensemble approach, combined with an artificial neural networking technique, was used to develop a soft-computing ensemble algorithm (SEA) to forecast the monthly and seasonal rainfall over the Indian subcontinent. Forecasts using January to May initial conditions along with observations during 1982–2014 were used to develop the model. The SEA compares well with observations.

Original languageEnglish
Pages (from-to)308-314
Number of pages7
JournalMeteorological Applications
Volume24
Issue number2
DOIs
Publication statusPublished - 1 Apr 2017

Keywords

  • ensemble
  • monsoon forecasting
  • neural networks
  • TO-INTERANNUAL PREDICTION
  • PREDICTABILITY
  • WEATHER
  • SURFACE
  • NEURAL-NETWORK
  • INTRASEASONAL OSCILLATIONS
  • RADIATION

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