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Evaluating raw ensembles with the continuous ranked probability score
Author(s) -
Bröcker Jochen
Publication year - 2012
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.1891
Subject(s) - quantile , scoring rule , cumulative distribution function , raw score , statistics , rank (graph theory) , computer science , mathematics , probability distribution , piecewise , artificial intelligence , raw data , econometrics , probability density function , mathematical analysis , combinatorics
The continuous ranked probability score (CRPS) is a frequently used scoring rule. In contrast with many other scoring rules, the CRPS evaluates cumulative distribution functions. An ensemble of forecasts can easily be converted into a piecewise constant cumulative distribution function with steps at the ensemble members. This renders the CRPS a convenient scoring rule for the evaluation of ‘raw’ ensembles, obviating the need for sophisticated ensemble model output statistics or dressing methods prior to evaluation. In this article, a relation between the CRPS score and the quantile score is established. The evaluation of ‘raw’ ensembles using the CRPS is discussed in this light. It is shown that latent in this evaluation is an interpretation of the ensemble as quantiles but with non‐uniform levels. This needs to be taken into account if the ensemble is evaluated further, for example with rank histograms. Copyright © 2012 Royal Meteorological Society

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