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The Multiensemble Approach: The NAEFS Example

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  • 1 Université de Québec à Montréal, Montréal, Québec, Canada
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Abstract

The North American Ensemble Forecasting System (NAEFS) is the combination of two Ensemble Prediction Systems (EPS) coming from two operational centers: the Canadian Meteorological Centre (CMC) and the National Centers for Environmental Prediction (NCEP). This system provides forecasts of up to 16 days and should improve the predictability skill of the probabilistic system, especially for the second week. First, a comparison between the two components of the NAEFS is performed for several atmospheric variables with “objective” verification tools developed at CMC [i.e., the continuous ranked probability score (CRPS) and its reliability-resolution decomposition, the reduced centered random variable, and confidence intervals estimated with bootstrap methods]. The CMC system is more reliable, especially because of a better ensemble dispersion, while the NCEP system has better probabilistic resolution. The NAEFS, compared to the CMC and NCEP EPSs, shows significant improvements both in terms of reliability and resolution. The predictability has been improved by 1–2 forecast days in the second week. That improvement is not only a result of the increased ensemble size in the EPS—from 20 members to 40 in the present case—but also to the combination of different models and initial condition perturbations. By randomly mixing members from the CMC and NCEP systems in a 20-member EPS, an intrinsic skill improvement of the system is observed.

Corresponding author address: Guillem Candille, Department of Earth and Atmospheric Sciences, Université de Québec à Montréal, C.P. 8888, Succ. Centre-ville, Montréal, QC H3C 1P8, Canada. Email: candille.guillem@uqam.ca

Abstract

The North American Ensemble Forecasting System (NAEFS) is the combination of two Ensemble Prediction Systems (EPS) coming from two operational centers: the Canadian Meteorological Centre (CMC) and the National Centers for Environmental Prediction (NCEP). This system provides forecasts of up to 16 days and should improve the predictability skill of the probabilistic system, especially for the second week. First, a comparison between the two components of the NAEFS is performed for several atmospheric variables with “objective” verification tools developed at CMC [i.e., the continuous ranked probability score (CRPS) and its reliability-resolution decomposition, the reduced centered random variable, and confidence intervals estimated with bootstrap methods]. The CMC system is more reliable, especially because of a better ensemble dispersion, while the NCEP system has better probabilistic resolution. The NAEFS, compared to the CMC and NCEP EPSs, shows significant improvements both in terms of reliability and resolution. The predictability has been improved by 1–2 forecast days in the second week. That improvement is not only a result of the increased ensemble size in the EPS—from 20 members to 40 in the present case—but also to the combination of different models and initial condition perturbations. By randomly mixing members from the CMC and NCEP systems in a 20-member EPS, an intrinsic skill improvement of the system is observed.

Corresponding author address: Guillem Candille, Department of Earth and Atmospheric Sciences, Université de Québec à Montréal, C.P. 8888, Succ. Centre-ville, Montréal, QC H3C 1P8, Canada. Email: candille.guillem@uqam.ca

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