Comment on ‘Can multi‐model combination really enhance the prediction skill of probabilistic ensemble forecasts?’

This note refers to the study of Weigel et al. (2008), where the success of multi‐model ensemble combination has been evaluated with a Gaussian stochastic toy model. The authors concluded that multi‐models can outperform the best participating single models, but only if the single model ensembles ar...

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Bibliographic Details
Published in:Quarterly journal of the Royal Meteorological Society 2009-01, Vol.135 (639), p.535-539
Main Authors: Weigel, A. P., Bowler, N. E.
Format: Article
Language:English
Subjects:
ensemble dispersion
overconfidence
probabilistic verification
reliability
toy model
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Summary:This note refers to the study of Weigel et al. (2008), where the success of multi‐model ensemble combination has been evaluated with a Gaussian stochastic toy model. The authors concluded that multi‐models can outperform the best participating single models, but only if the single model ensembles are under‐dispersive. Here we introduce two improved versions of the toy model of Weigel et al. For one of these models the combination of well‐dispersed (i.e. reliable) forecasts can improve the prediction skill, but for the other model this possibility is excluded. It is argued that, for normally distributed variables, the first model may be applicable in the context of short‐ and medium‐range forecasting, but the latter may be more appropriate to seasonal forecasting. Copyright © Royal Meteorological Society and Crown Copyright, 2009.
ISSN:0035-9009
1477-870X
DOI:10.1002/qj.381