A SST based large multi-model ensemble forecasting system for Indian summer monsoon rainfall

Details A SST based large multi-model ensemble forecasting system for Indian summer monsoon rainfall A SST based large multi-model ensemble forecasting system for Indian summer monsoon rainfall

Oct 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008georl..3519705s&link_type=abstract

Geophysical Research Letters, Volume 35, Issue 19, CiteID L19705

Physics

Geophysics

2

Atmospheric Processes: Tropical Meteorology, Mathematical Geophysics: Probabilistic Forecasting (3238), Atmospheric Processes: Ocean/Atmosphere Interactions (0312, 4504), Global Change: Climate Dynamics (0429, 3309)

Scientific paper

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.

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