dc.contributor.author | Ferro, CAT | |
dc.date.accessioned | 2016-03-09T14:06:01Z | |
dc.date.issued | 2014-07 | |
dc.description.abstract | The notion of fair scores for ensemble forecasts was introduced recently to
reward ensembles whose members behave as though they and the verifying
observation are sampled from the same distribution. In the case of forecasting
binary outcomes, a characterization is given of a general class of fair scores
for ensembles that are interpreted as random samples. This is also used to
construct classes of fair scores for ensembles that forecast multi-category and
continuous outcomes. The usual Brier, ranked probability, and continuous
ranked probability scores for ensemble forecasts are shown to be unfair,
while adjusted versions of these scores are shown to be fair. A definition of
fairness is also proposed for ensembles whose members are interpreted as
being dependent, and it is shown that fair scores exist only for some forms of
dependence. | en_GB |
dc.identifier.citation | Vol. 140, pp. 1917 - 1923 | en_GB |
dc.identifier.doi | 10.1002/qj.2270 | |
dc.identifier.uri | http://hdl.handle.net/10871/20641 | |
dc.language.iso | en | en_GB |
dc.publisher | Wiley for Royal Meteorological Society | en_GB |
dc.subject | Brier score | en_GB |
dc.subject | continuous ranked probability score | en_GB |
dc.subject | scoring rules | en_GB |
dc.subject | forecast verification | en_GB |
dc.title | Fair scores for ensemble forecasts | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2016-03-09T14:06:01Z | |
dc.identifier.issn | 0035-9009 | |
dc.description | This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record. | en_GB |
dc.identifier.journal | Quarterly Journal of the Royal Meteorological Society | en_GB |