Improved spread-error relationship and probabilistic prediction from the CFS-based grand ensemble prediction system

S. Abhilash, A. K. Sahai, N. Borah, S. Joseph, R. Chattopadhyay, S. Sharmila, M. Rajeevan, Brian E Mapes, A. Kumar

Research output: Contribution to journalArticle

14 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)1569-1578
Number of pages10
JournalJournal of Applied Meteorology and Climatology
Volume54
Issue number7
DOIs
StatePublished - 2015

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climate
prediction
forecast
climate modeling
monsoon
timescale
atmosphere

Keywords

  • Ensembles
  • Forecast verification/skill
  • Forecasting techniques

ASJC Scopus subject areas

  • Atmospheric Science

Cite this

Improved spread-error relationship and probabilistic prediction from the CFS-based grand ensemble prediction system. / Abhilash, S.; Sahai, A. K.; Borah, N.; Joseph, S.; Chattopadhyay, R.; Sharmila, S.; Rajeevan, M.; Mapes, Brian E; Kumar, A.

In: Journal of Applied Meteorology and Climatology, Vol. 54, No. 7, 2015, p. 1569-1578.

Research output: Contribution to journalArticle

Abhilash, S, Sahai, AK, Borah, N, Joseph, S, Chattopadhyay, R, Sharmila, S, Rajeevan, M, Mapes, BE & Kumar, A 2015, 'Improved spread-error relationship and probabilistic prediction from the CFS-based grand ensemble prediction system', Journal of Applied Meteorology and Climatology, vol. 54, no. 7, pp. 1569-1578. https://doi.org/10.1175/JAMC-D-14-0200.1
Abhilash, S. ; Sahai, A. K. ; Borah, N. ; Joseph, S. ; Chattopadhyay, R. ; Sharmila, S. ; Rajeevan, M. ; Mapes, Brian E ; Kumar, A. / Improved spread-error relationship and probabilistic prediction from the CFS-based grand ensemble prediction system. In: Journal of Applied Meteorology and Climatology. 2015 ; Vol. 54, No. 7. pp. 1569-1578.
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