Extending logistic regression to provide full‐probability‐distribution MOS forecasts

By D. Wilks. 2009

185 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/35bef55714790980e24e09233a921e06fe95c69c

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In Atmospheric Science

In Global and Planetary Change

Citations in the corpus

Statistical Postprocessing of Ensemble Precipitation Forecasts by Fitting Censored, Shifted Gamma Distributions* (2015, unknown): “This model has only three regression coefficients and is thus comparable in terms of model complexity with extended logistic regression (Wilks 2009).”
Generating and Calibrating Probabilistic Quantitative Precipitation Forecasts from the High-Resolution NWP Model COSMO-DE (2012, methodology): “An overview of state-of-the-art ensemble postprocessing techniques is given in Wilks (2006a).”
Postprocessing of Ensemble Precipitation Predictions with Extended Logistic Regression Based on Hindcasts (2012, unknown): “…that selecting the average of the transformed (square root) ensemble members as predictor performed slightly better than the transformed ensemble averages as in other studies (e.g., Wilks 2009), but this choice has been motivated here to enable the implementation of the ensemble size correction.”
Ensemble Model Output Statistics for Wind Vectors (2012, methodology): “al., 2005; Hagedorn et al., 2008; Kann et al., 2009), where the predictive density is normal and the method is often referred to as nonhomogeneous Gaussian regression, for quantitative precipitation (Wilks, 2009), and for windspeed (Thorarinsdottir and Gneiting,2010;Thorarinsdottir and Johnson, 2011”
Estimation Methods for Nonhomogeneous Regression Models: Minimum Continuous Ranked Probability Score versus Maximum Likelihood (2018, methodology): “In statistical postprocessing, various approaches have been developed to correct such ensembles (Roulston and Smith 2003; Raftery et al. 2005; Gneiting et al. 2005; Wilks 2009) but none of them has appeared as a best single postprocessing strategy (Wilks and Hamill 2007).”
Extending Extended Logistic Regression: Extended versus Separate versus Ordered versus Censored (2014, methodology): “Recently, Wilks (2009) extended logistic regression by including the (transformed) Denotes Open Access content.”
Locally Calibrated Probabilistic Temperature Forecasting Using Geostatistical Model Averaging and Local Bayesian Model Averaging (2011, methodology): “Related approaches include kernel dressing (Bröcker and Smith 2008), moving average estimation (Johnson and Swinbank 2009), model output statistics (MOS; Glahn et al. 2009b), ensemble regression (Unger et al. 2009) and extended logistic regression (Wilks 2009).”