Locally Calibrated Probabilistic Temperature Forecasting Using Geostatistical Model Averaging and Local Bayesian Model Averaging

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Ensemble Model Output Statistics for Wind Vectors (2012, methodology): “tting approach as in Pinson (2011) could be implemented, where the parameter estimates are updated in a computationally efﬁcient, adaptive way. Furthermore, a geostatistical app roach such as that of Kleiber et al. (2011) in the context of Bayesian model averaging (Raftery et al., 2005) could be dev”
Combining Predictive Distributions (2011, background): “Berrocal et al. (2007), Glahn et al. (2009) and Kleiber et al. (2011) observed this empirically in the context of probabilistic weather forecasts, and proposed a special case of the spread-adjusted linear pool as an ad hoc remedy.”
Spatial Postprocessing of Ensemble Forecasts for Temperature Using Nonhomogeneous Gaussian Regression (2014, methodology): “complete resolution of the orography or different land use characteristics. Similarly, the prediction uncertainty may differ between locations in a way that is not represented by the ensemble spread (Kleiber et al., 2011; Scheuerer and Büermann, 2014; Scheuerer and König, 2014). As an alternative to”