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A SST based large multi‐model ensemble forecasting system for Indian summer monsoon rainfall
Author(s) -
Sahai A. K.,
Chattopadhyay R.,
Goswami B. N.
Publication year - 2008
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2008gl035461
Subject(s) - climatology , probabilistic logic , environmental science , monsoon , sea surface temperature , meteorology , ensemble forecasting , computer science , geology , geography , artificial intelligence
An ensemble mean and probabilistic approach is essential for reliable forecast of the All India Summer Monsoon Rainfall (AIR) due to the seminal role played by internal fast processes in interannual variability (IAV) of the monsoon. In this paper, we transform a previously used empirical model to construct a large ensemble of models to deliver useful probabilistic forecast of AIR. The empirical model picks up predictors only from global sea surface temperature (SST). Methodology of construction implicitly incorporates uncertainty arising from internal variability as well as from the decadal variability of the predictor‐predictand relationship. The forecast system demonstrates the capability of predicting monsoon droughts with high degree of confidence. Results during independent verification period (1999–2008) suggest a roadmap for generating empirical probabilistic forecast of monsoon IAV for practical delivery to the user community.