Evaluating Probabilistic Forecasts Using Information Theory

By M. Roulston, Leonard A. Smith. 2002

384 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/879d476046a63fbea52e1983c275368bc5315bdc

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Citations in the corpus

Skill of Multimodel ENSO Probability Forecasts (2008, background): “Forecast skill is accessed using ranked probability skill score and rate of return; ROR is a skill measure that attaches an investment value to the forecast information and has the same ranking as the ignorance or log-likelihood skill score (Roulston and Smith 2002).”
Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds (2008, background): “Further work might proceed from Roulston and Smith (2002) , DelSole and Tippett (2007), and the references therein.”
Strictly Proper Scoring Rules (2014, methodology): “The statistical logarithmic SR S log has found applications in a variety of areas, for example in information theory [8, 49], Neyman-Pearson Theory in statistics [16] and the health sciences [27].”
Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation (2005, methodology): “We also consider two scoring rules for the assessment of predictive PDFs: the continuous ranked probability score (Unger 1985; Hersbach 2000; Gneiting and Raftery 2004) and the ignorance score (Good 1952; Roulston and Smith 2002).”
The continuous ranked probability score for circular variables and its application to mesoscale forecast ensemble verification (2006, background): “The linear CRPS and the negative of the logarithmic score (information deficit or ignorance score) are seeing increased use in probabilistic weather forecast evaluations (Unger 1985; Hersbach 2000; Roulston and Smith 2002; Grimit 2004; Candille and Talagrand 2005; Gneiting et al. 2005).”
Ensemble flood forecasting: a review. (2009, positive): “The value of hydrological forecasts (discharge, water stage, soil moisture etc) based on ensemble predictions can be evaluated (verified) with scores developed for meteorological applications such as the Brier Score (Jolliffe and Stephenson, 2003), continuous rank probability score (Hersbach, 2000) ”
Probabilistic forecasts, calibration and sharpness (2007, background): “This scoring rule is proper and has many desirable properties (Roulston and Smith 2002), but lacks robustness (Selten 1998; Gneiting and Raftery 2004).”
Variogram-Based Proper Scoring Rules for Probabilistic Forecasts of Multivariate Quantities* (2015, background): “On the other hand, there are scoring rules [e.g., the logarithmic score by Roulston and Smith (2002), applied to a multivariate probability density function] that are more sensitive to misspecified correlations, but require that the forecast is given in terms of a predictive density, and are thus…”
Using Bayesian Model Averaging to Calibrate Forecast Ensembles (2005, methodology): “method of Roulston and Smith (2003) is designed for”
Ensemble size: How suboptimal is less than infinity? (2018, background): “The logarithmic score is also known as the ignorance score and is linked to user decisions corresponding to betting scenarios (Roulston and Smith, 2002; Hagedorn and Smith, 2009).”
Scoring Rules for Forecast Verification (2010, background): “As proposed by many authors (Lindley 1985; Leung and North 1990; Roulston and Smith 2002), Corresponding author address: Riccardo Benedetti, Laboratory for Environmental Monitoring and Modelling (LaMMA), Building D, via Madonna del Piano, Sesto Fiorentino 50019, Italy.”
Kullback–Leibler Divergence as a Forecast Skill Score with Classic Reliability–Resolution–Uncertainty Decomposition (2010, background): “closely related to relative entropy’’ as defined by Roulston and Smith (2002), but actually is also a relative entropy.”
Forecast verification: current status and future directions (2008, methodology): “The ignorance score (Good, 1952; Roulston and Smith, 2002) is a logarithmic score formulated in the same way as the Wilson et al. (1999) score.”
Postprocessing of Ensemble Precipitation Predictions with Extended Logistic Regression Based on Hindcasts (2012, neutral): “Then, the probability is assigned: In this equation, the uncertainty due to the ﬁnite ensemble size is taken into account by adding an additional ﬁctitious member distributed equally between the occurrence of the event and the nonoccurrence (Roulston and Smith 2002; Katz and Ehrendorfer 2006).”
Comparison between Singular Vectors and Breeding Vectors as Initial Perturbations for the ECMWF Ensemble Prediction System (2008, methodology): “To measure the probabilistic skill of the ensemble systems we have used the RPSS (Candille and Talagrand 2005), the ROC area (Wilks 2006), and the ISS (Roulston and Smith 2002).”