The continuous ranked probability score for circular variables and its application to mesoscale forecast ensemble verification

By E. Grimit, Tilmann Gneiting, Veronica J. Berrocal, Nicholas Alexander Johnson. 2006

183 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/effc8bb55bdc9b179e22d42e3c3816bc2905f70f

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In Atmospheric Science

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Citations in the corpus

Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds (2008, background): “2006; Gneiting, Balabdaoui and Raftery 2007) or wind direction (Grimit et al. 2006), but not of wind vectors.”
Bias Correction and Bayesian Model Averaging for Ensemble Forecasts of Surface Wind Direction (2010, methodology): “For bias correction, we use the state of the art circular–circular regression approach of Downs and Mardia (2002) and Kato et al.”
Forecast verification for extreme value distributions with an application to probabilistic peak wind prediction (2012, methodology): “Both Selten (1998) and Grimit et al. (2006) have pointed out that although the ignorance score is simple to calculate, it attributes a very strong penalty to events with low probability and is thus very sensitive to outliers.580 wileyonlinelibrary.com/journal/environmetrics Copyright © 2012 John…”
Spatial Postprocessing of Ensemble Forecasts for Temperature Using Nonhomogeneous Gaussian Regression (2014, background): “The formulation in (14) further permits the evaluation of the CRPS for discrete distributions such as an ensemble (Grimit et al., 2006).”
Forecast verification: current status and future directions (2008, methodology): “The CRPS has the nice property that it reduces to the mean absolute error for a deterministic forecast, which means that it can be used directly to compare the accuracy of an ensemble forecast with respect to the accuracy of a deterministic forecast.”
Evaluating Probabilistic Forecasts with scoringRules (2017, background): “Derived by Grimit et al. (2006).”
Locally Calibrated Probabilistic Temperature Forecasting Using Geostatistical Model Averaging and Local Bayesian Model Averaging (2011, background): “…probability score (CRPS) assesses both sharpness and calibration, and is defined by CRPS(F, x) 5 ð1‘ 2‘ [F(y) 2 I(y $ x)]2 dy, (7) where F is the predictive distribution and x is the observed temperature (Matheson and Winkler 1976; Hersbach 2000; Grimit et al. 2006; Gneiting and Raftery 2007).”
Powering Up With Space-Time Wind Forecasting (2010, methodology): “The CRPS can be computed explicitly for the predictive truncated normal distribution as given in Gneiting et al. (2006), and the CRPS value for the BST model is computed using the approximation in equation (3) from Grimit et al. (2006).”
Evaluating raw ensembles with the continuous ranked probability score (2012, background): “The literature abounds in examples of scoring rules being applied to evaluate probability forecasts: Roulston and Smith (2002, 2003); Roulston et al. (2003); Gneiting et al. (2005); Raftery et al. (2005); Roulston et al. (2005); Grimit et al. (2006); Sloughter et al. (2007).”
Strictly Proper Scoring Rules, Prediction, and Estimation (2007, unknown): “Grimit et al. (2006) introduced (30) as an analog of the CRPS (21) that applies to directional variables, and used Fourier analytic tools to prove the propriety of the score.”