Probabilistic Forecast Calibration Using ECMWF and GFS Ensemble Reforecasts. Part II: Precipitation

By Thomas M. Hamill, R. Hagedorn, J. Whitaker. 2008

248 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/1a21a9163d06d3d6438638beeffa6a0576877c3d

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Citations in the corpus

Medium‐range multimodel ensemble combination and calibration (2009, methodology): “Hagedorn et al. (2008) also found that a calibrated multi-model ensemble was slightly more skillful than a calibrated single model ensemble.”
Comparison of non-homogeneous regression models for probabilistic wind speed forecasting (2013, methodology): “The nonhomogeneous regression (NR) methodology was originally developed for sea level pressure and surface temperature under a normal predictive distribution (Gneiting et al., 2005), see also Hagedorn et al. (2008) and Kann et al. (2009) for further applications.”
Spatial Postprocessing of Ensemble Forecasts for Temperature Using Nonhomogeneous Gaussian Regression (2014, methodology): “errors jointly over a region or separately at each observation location (Gneiting et al. 2005; Thorarinsdottir and Gneiting 2010; Hagedorn et al. 2008; Kann et al. 2009).”
Spatial ensemble post‐processing with standardized anomalies (2016, background): “Numerical weather prediction (NWP) models provide spatial forecasts of different meteorological parameters. Due to limited computational power, the grid of NWP models is too coarse to resolve some small-scale processes sufficiently, especially in complex terrain such as the Alps. Therefore, parametr”
Generating and Calibrating Probabilistic Quantitative Precipitation Forecasts from the High-Resolution NWP Model COSMO-DE (2012, background): “However, quantitative precipitation forecasting (QPF) still represents a major challenge in numerical weather prediction (NWP) and the skill of QPF is low compared to other meteorological variables (Hense and Wulfmeyer 2008; Ebert et al. 2003).”
Ensemble Model Output Statistics for Wind Vectors (2012, methodology): “EMOS methods have been developed for temperature and surface pressure (Gneiting et 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 ”
Extending logistic regression to provide full‐probability‐distribution MOS forecasts (2009, methodology): “…standard deviation may improve the forecasts, as has been demonstrated for ensemble-MOS post-processing using conventional single-threshold logistic regression (Hamill et al., 2008; Wilks and Hamill, 2007), and incorporation of an ensemble spread predictor into f (x) would be straightforward.”
Locally Calibrated Probabilistic Temperature Forecasting Using Geostatistical Model Averaging and Local Bayesian Model Averaging (2011, methodology): “Some approaches 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…”
A Comparison between Raw Ensemble Output, (Modified) Bayesian Model Averaging, and Extended Logistic Regression Using ECMWF Ensemble Precipitation Reforecasts (2010, methodology): “(3b) Note that this expression for the probability of precipitation (POP) is similar to the one used often in ensemble model output statistics (e.g. Wilks and Hamill 2007; Hamill et al. 2008).”
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).”
Verification and Calibration of Neighborhood and Object-Based Probabilistic Precipitation Forecasts from a Multimodel Convection-Allowing Ensemble (2012, background): “…Sloughter et al. 2007), Gaussian ensemble dressing (GED; Roulston and Smith 2003; Wang and Bishop 2005; Fortin et al. 2006), logistic regression (LR; Hamill et al. 2004, 2008), analog forecasts (Zhu and Toth 2000; Hamill and Whitaker 2006), artificial neural networks (Yuan et al. 2007, 2008),…”