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contributor authorPrimo, Cristina
contributor authorFerro, Christopher A. T.
contributor authorJolliffe, Ian T.
contributor authorStephenson, David B.
date accessioned2017-06-09T16:26:31Z
date available2017-06-09T16:26:31Z
date copyright2009/03/01
date issued2009
identifier issn0027-0644
identifier otherams-67939.pdf
identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4209441
description abstractProbabilistic forecasts of atmospheric variables are often given as relative frequencies obtained from ensembles of deterministic forecasts. The detrimental effects of imperfect models and initial conditions on the quality of such forecasts can be mitigated by calibration. This paper shows that Bayesian methods currently used to incorporate prior information can be written as special cases of a beta-binomial model and correspond to a linear calibration of the relative frequencies. These methods are compared with a nonlinear calibration technique (i.e., logistic regression) using real precipitation forecasts. Calibration is found to be advantageous in all cases considered, and logistic regression is preferable to linear methods.
publisherAmerican Meteorological Society
titleCalibration of Probabilistic Forecasts of Binary Events
typeJournal Paper
journal volume137
journal issue3
journal titleMonthly Weather Review
identifier doi10.1175/2008MWR2579.1
journal fristpage1142
journal lastpage1149
treeMonthly Weather Review:;2009:;volume( 137 ):;issue: 003
contenttypeFulltext


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