Combining the Bayesian processor of output with Bayesian model averaging for reliable ensemble forecasting

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 ensemb...

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Bibliographic Details
Published in:Journal of the Royal Statistical Society 2015-01, Vol.64 (1), p.75-92
Main Authors: Marty, R., Fortin, V., Kuswanto, H., Favre, A.-C., Parent, E.
Format: Article
Language:English
Subjects:
Applied statistics
Bayesian analysis
Bayesian method
Bayesian model averaging
Bayesian networks
Bayesian processor of output
Calibration
Canada
Climate
Climatology
Datasets
Dynamical systems
Economic analysis
Economic forecasts
Ensemble post-processing
Ensemble prediction system
Forecasting
Forecasting models
Hierarchical Bayesian model
Hierarchy
Linear models
Mathematical models
Mathematics
Microprocessors
Predictive distribution
Probability
Probability forecasts
Reliability
Skills
Statistical analysis
Statistical weather forecasting
Statistics
Studies
Training
Uncertainty
Weather forecasting
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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.
ISSN:0035-9254
1467-9876
DOI:10.1111/rssc.12062