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dc.contributor.authorSiegert, S
dc.date.accessioned2017-06-08T12:00:35Z
dc.date.issued2013-08-14
dc.description.abstractThe Brier Score is a widely-used criterion to assess the quality of probabilistic predictions of binary events. The expectation value of the Brier Score can be decomposed into the sum of three components called reliability, resolution, and uncertainty which characterize different forecast attributes. Given a dataset of forecast probabilities and corresponding binary verifications, these three components can be estimated empirically. Here, propagation of uncertainty is used to derive expressions that approximate the sampling variances of the estimated components. Variance estimates are provided for both the traditional estimators, as well as for refined estimators that include a bias correction. Applications of the derived variance estimates to artificial data illustrate their validity, and application to a meteorological prediction problem illustrates a possible use case. The observed increase of variance of the bias-corrected estimators is discussed.en_GB
dc.identifier.citationVol. 140 (682) Part A, pp. 1771–1777en_GB
dc.identifier.doi10.1002/qj.2228
dc.identifier.urihttp://hdl.handle.net/10871/27869
dc.language.isoenen_GB
dc.publisherWiley / Royal Meteorological Societyen_GB
dc.rights© 2013 Royal Meteorological Societyen_GB
dc.subjectprobabilistic predictionen_GB
dc.subjectforecast verificationen_GB
dc.subjectreliabilityen_GB
dc.subjectresolutionen_GB
dc.titleVariance estimation for Brier Score decompositionen_GB
dc.typeArticleen_GB
dc.date.available2017-06-08T12:00:35Z
pubs.declined2017-06-08T11:04:23.760+0100
dc.descriptionThis is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.en_GB
dc.identifier.journalQuarterly Journal of the Royal Meteorological Societyen_GB


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