A soft‐computing ensemble approach (SEA) to forecast Indian summer monsoon rainfall. (7th March 2017)

• Record Type: Journal Article
• Authors: Kurian, Nisha; Venugopal, T.; Singh, Jatin; Ali, M. M.
• Is Part Of: Meteorological applications. Volume 24:Number 2(2017)
• Page Start: 308
• Page End: 314
• Publication Date: 2017-03-07
• Subjects: monsoon forecasting -- ensemble -- neural networks; Meteorology -- Periodicals; Meteorological services -- Periodicals; 551.5
• DOI: 10.1002/met.1650
• Languages: English
• ISSNs: 1350-4827

Abstract

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.