Calibrated Surface Temperature Forecasts from the Canadian Ensemble Prediction System Using Bayesian Model Averaging

By L. Wilson, Stephane Beauregard, Adrian E. Raftery, R. Verret. 2007

117 citations recorded by the source corpus.

Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/a3caac9b6b6b7f34d7a92193d5681f4054cf3bd4

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Citations in the corpus

Bias Correction and Bayesian Model Averaging for Ensemble Forecasts of Surface Wind Direction (2010, background): “The initial development was for linear weather quantities, such as surface temperature, quantitative precipitation, and wind speed (Raftery et al. 2005; Sloughter et al. 2007; Wilson et al. 2007; Sloughter et al. 2009), for which the component PDFs are probability distributions on the real line.”
Performance of Optimally Merged Multisatellite Precipitation Products Using the Dynamic Bayesian Model Averaging Scheme Over the Tibetan Plateau (2017, methodology): “Thus, it might induce higher uncertainty during the merging process (Wilson et al., 2007).”
Medium‐range multimodel ensemble combination and calibration (2009, methodology): “A competing method to compute the weights is Bayesian model averaging (BMA) (Raftery et al., 2005; Wilson et al., 2007), where the weights and variances are computed simultaneously through a maximum likelihood estimation which aims to fit the pdfs to the calibration data.”
Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging (2007, background): “Good results for temperature have been obtained with a 25-day training period in the Pacific Northwest (Raftery et al. 2005), and with a 40-day training period for stations across Canada (Wilson et al. 2007).”
A Comparison between Raw Ensemble Output, (Modified) Bayesian Model Averaging, and Extended Logistic Regression Using ECMWF Ensemble Precipitation Reforecasts (2010, methodology): “Wilson et al. (2007a) and Hamill (2007) also proposed similar additive bias corrections, but those corrections did not generally improve the skill of BMA for Gaussian predictands (Wilson et al. 2007a,b).”
Bayesian Modeling of Uncertainty in Ensembles of Climate Models (2009, background): “Apart from the preceding literature on climate change, the field of ensemble-based weather forecasting has advanced extensively in recent years, and new statistical approaches have been developed in that context, especially in a series of articles by Gneiting, Raftery, and coworkers (Gel et al. 2004”