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Combining the Bayesian processor of output with Bayesian model averaging for reliable ensemble forecasting
Author(s) -
Marty R.,
Fortin V.,
Kuswanto H.,
Favre A.C.,
Parent E.
Publication year - 2015
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12062
Subject(s) - bayesian probability , ensemble forecasting , computer science , bayesian inference , bayesian average , ensemble learning , bayesian statistics , statistical ensemble , set (abstract data type) , machine learning , artificial intelligence , statistics , monte carlo method , mathematics , canonical ensemble , programming language
Summary Weather predictions are uncertain by nature. This uncertainty is dynamically assessed by a finite set of trajectories, called ensemble members. Unfortunately, ensemble prediction systems underestimate the uncertainty and thus are unreliable. Statistical approaches are proposed to post‐process ensemble forecasts, including Bayesian model averaging and the Bayesian processor of output. We develop a methodology, called the Bayesian processor of ensemble members, from a hierarchical model and combining the two aforementioned frameworks to calibrate ensemble forecasts. The Bayesian processor of ensemble members is compared with Bayesian model averaging and the Bayesian processor of output by calibrating surface temperature forecasting over eight stations in the province of Quebec (Canada). Results show that ensemble forecast skill is improved by the method developed.