ORCID Profile
0000-0002-3834-8737
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Publisher: Elsevier BV
Date: 10-2017
Publisher: Wiley
Date: 03-12-2013
DOI: 10.1002/ASL2.477
Publisher: Wiley
Date: 10-10-2014
DOI: 10.1002/JOC.4178
Publisher: Wiley
Date: 10-02-2016
DOI: 10.1002/QJ.2730
Publisher: Springer Science and Business Media LLC
Date: 03-05-2017
Publisher: Springer Science and Business Media LLC
Date: 08-05-2015
Publisher: Elsevier BV
Date: 2015
Publisher: American Meteorological Society
Date: 07-2015
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.
Publisher: Springer Science and Business Media LLC
Date: 12-07-2013
No related grants have been discovered for Abhilash Sukumaraillai.