Comparison of Ensemble-MOS Methods Using GFS Reforecasts

By D. Wilks, Thomas M. Hamill. 2007

235 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/224e7710a1d4717f5201e9e60e7894c92e718332

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Citations in the corpus

Statistical Postprocessing of Ensemble Precipitation Forecasts by Fitting Censored, Shifted Gamma Distributions* (2015, unknown): “For this reason, statistical postprocessing of the output of an ensemble prediction system is commonly an integral part of the forecast process, since it can improve the reliability and skill of probabilistic guidance (e.g., Wilks and Hamill 2007; Hamill et al. 2008, and references therein).”
Spatial Postprocessing of Ensemble Forecasts for Temperature Using Nonhomogeneous Gaussian Regression (2014, methodology): “…forecasts are commonly postprocessed using statistical techniques to improve the calibration and correct for potential biases, and a diverse range of postprocessing techniques has been proposed (e.g. Gneiting et al., 2005; Raftery et al., 2005; Wilks and Hamill, 2007; Bröcker and Smith, 2008).”
Bias Correction and Bayesian Model Averaging for Ensemble Forecasts of Surface Wind Direction (2010, background): “Wilks (2006b), Wilks and Hamill (2007), and Brö cker and Smith (2008) review and compare techniques for doing this.”
Heteroscedastic Extended Logistic Regression for Postprocessing of Ensemble Guidance (2014, methodology): “Comparisons of these and other methods (Wilks 2006a; Wilks and Hamill 2007) showed that logistic regression is one of the better approaches.”
Locally Calibrated Probabilistic Temperature Forecasting Using Geostatistical Model Averaging and Local Bayesian Model Averaging (2011, methodology): “…include nonhomogeneous Gaussian regression (Gneiting et al. 2005; Hagedorn et al. 2008), the best member method (Roulston and Smith 2003; Wang and Bishop 2005; Fortin et al. 2006), and logistic regression (Hamill et al. 2004), all of which have been recently compared by Wilks and Hamill (2007).”
Probabilistic Forecast Calibration Using ECMWF and GFS Ensemble Reforecasts. Part II: Precipitation (2008, methodology): “…in the autumn of 2005, which was a much more high-resolution, skillful, and less biased forecast model than the 1998 Global Forecasting System (GFS) model used in previous reforecast experiments (Hamill et al. 2004, 2006; Hamill and Whitaker 2006, 2007; Whitaker et al. 2006; Wilks and Hamill 2007).”
Extending Extended Logistic Regression: Extended versus Separate versus Ordered versus Censored (2014, methodology): “(3) Often separate logistic regressions (i.e., with separate coefficient vectors b) are fitted for several thresholds qj of interest (e.g., Hamill et al. 2004; Wilks 2006; Wilks and Hamill 2007).”
Postprocessing of Ensemble Precipitation Predictions with Extended Logistic Regression Based on Hindcasts (2012, unknown): “…Global Forecast System (200-km grid spacing, 15 members, every day during a 25-yr period; Hamill et al. 2004; Hamill and Whitaker 2006; Wilks and Hamill 2007), and for the ECMWF EPS (80-km grid spacing, 15 members, weekly, during a 20-yr period; Hagedorn et al. 2008; Hamill et al. 2008;…”
A Comparison between Raw Ensemble Output, (Modified) Bayesian Model Averaging, and Extended Logistic Regression Using ECMWF Ensemble Precipitation Reforecasts (2010, methodology): “Because in this study the 15 ensemble members come from a single model, they are exchange-able; therefore, the BMA weights W k are constrained to be equal and have a value of 1 / 15 each (Raftery et al. 2005; Wilks 2006a; Hamill 2007; Fraley et al. 2010).”
Kalman Filter and Analog Schemes to Postprocess Numerical Weather Predictions (2011, background): “…and AQ (Delle Monache et al. 2006, 2008; Djalalova et al. 2010; Kang et al. 2010); 4) model output statistics approaches (e.g., Glahn and Lowry 1972; Carter et al. 1989; Jacks et al. 1990; Hart et al. 2004; Wilks and Hamill 2007); and 5) gene-expression algorithms (e.g., Bakhshaii and Stull 2009).”
Local proper scoring rules of order two (2011, background): “Wilks and Hamill (2007) and Bröcker and Smith (2008) review statistical postprocessing techniques for ensemble weather forecasts.”
Review of probabilistic pollen-climate transfer methods (2012, methodology): “referred to as ensemble post-processing or ensemble interpretation (e.g. Bröcker and Smith, 2008; Wilks and Hamill, 2007).”
Verification and Calibration of Neighborhood and Object-Based Probabilistic Precipitation Forecasts from a Multimodel Convection-Allowing Ensemble (2012, methodology): “…05/30/22 03:17 PM UTC Unauthenticated | Downloaded 05/30/22 03:17 PM UTC days are taken from the 10 days immediately preceding the day of the forecast.5 For 25-day training all days except the day of the forecast are used following the standard cross-validation method (e.g., Wilks and Hamill 2007).”
Estimation Methods for Nonhomogeneous Regression Models: Minimum Continuous Ranked Probability Score versus Maximum Likelihood (2018, methodology): “2005; Wilks 2009) but none of them has appeared as a best single postprocessing strategy (Wilks and Hamill 2007).”
Local Proper Scoring Rules (2009, background): “Wilks (2006), Wilks and Hamill (2007) and Br¨ocker and Smith (2008) review various approaches to the statistical postprocessing of ensemble forecasts.”