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Improved Spread–Error Relationship and Probabilistic Prediction from the CFS-Based Grand Ensemble Prediction System

Abhilash, S and Sahai, AK and Borah, N and Joseph, S and Chattopadhyay, R and Sharmila, S and Rajeevan, M and Mapes, BE and Kumar, A (2015) Improved Spread–Error Relationship and Probabilistic Prediction from the CFS-Based Grand Ensemble Prediction System. Journal of Applied Meteorology and Climatology, 54. pp. 1569-1578.

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Abstract

This study describes an attempt to overcome the underdispersive nature of single-model ensembles (SMEs). As an Indo–U.S. collaboration designed to improve the prediction capabilities of models over the Indian monsoon region, the Climate Forecast System (CFS) model framework, developed at the National Centers for Environmental Prediction (NCEP-CFSv2), is selected. This article describes a multimodel ensemble prediction system, using a suite of different variants of the CFSv2 model to increase the spread without relying on very different codes or potentially inferior models. The SMEs are generated not only by perturbing the initial condition, but also by using different resolutions, parameters, and coupling configurations of the same model (CFS and its atmosphere component, the Global Forecast System). Each of these configurations was created to address the role of different physical mechanisms known to influence error growth on the 10–20-day time scale. Last, the multimodel consensus forecast is developed, which includes ensemble-based uncertainty estimates. Statistical skill of this CFS-based Grand Ensemble Prediction System (CGEPS) is better than the best participating SME configuration, because increased ensemble spread reduces overconfidence errors.

Item Type: Article
Additional Information: Copyright of this article belongs to American Meteorological Society
Subjects: Meteorology and Climatology
Depositing User: IITM Library
Date Deposited: 15 Dec 2017 11:01
Last Modified: 15 Dec 2017 11:01
URI: http://moeseprints.incois.gov.in/id/eprint/4082

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