Evaluating raw ensembles with the continuous ranked probability score

2012 ◽  
Vol 138 (667) ◽  
pp. 1611-1617 ◽  
Author(s):  
Jochen Bröcker
Keyword(s):  
Probability Score ◽  
Continuous Ranked Probability Score

scholarly journals Optimization of continuous ranked probability score using PSO

2015 ◽  
pp. 373-378 ◽  
Author(s):  
Seyedeh Atefeh Mohammadi ◽  
Morteza Rahmani ◽  
Majid Azadi
Keyword(s):  
Probability Score ◽  
Continuous Ranked Probability Score

scholarly journals Performance and reliability of multimodel hydrological ensemble simulations based on seventeen lumped models and a thousand catchments

2010 ◽  
Vol 14 (11) ◽  
pp. 2303-2317 ◽  
Author(s):  
J. A. Velázquez ◽  
F. Anctil ◽  
C. Perrin
Keyword(s):  
Predictive Distribution ◽  
Added Value ◽  
Average Error ◽  
Hydrological Models ◽  
Relative Operating Characteristic ◽  
Probability Score ◽  
Ensemble Simulations ◽  
Continuous Ranked Probability Score ◽  
The Mean ◽  
Improved Performance

Abstract. This work investigates the added value of ensembles constructed from seventeen lumped hydrological models against their simple average counterparts. It is thus hypothesized that there is more information provided by all the outputs of these models than by their single aggregated predictors. For all available 1061 catchments, results showed that the mean continuous ranked probability score of the ensemble simulations were better than the mean average error of the aggregated simulations, confirming the added value of retaining all the components of the model outputs. Reliability of the simulation ensembles is also achieved for about 30% of the catchments, as assessed by rank histograms and reliability plots. Nonetheless this imperfection, the ensemble simulations were shown to have better skills than the deterministic simulations at discriminating between events and non-events, as confirmed by relative operating characteristic scores especially for larger streamflows. From 7 to 10 models are deemed sufficient to construct ensembles with improved performance, based on a genetic algorithm search optimizing the continuous ranked probability score. In fact, many model subsets were found improving the performance of the reference ensemble. This is thus not essential to implement as much as seventeen lumped hydrological models. The gain in performance of the optimized subsets is accompanied by some improvement of the ensemble reliability in most cases. Nonetheless, a calibration of the predictive distribution is still needed for many catchments.

scholarly journals Evaluating epidemic forecasts in an interval format

2021 ◽  
Vol 17 (2) ◽  
pp. e1008618
Author(s):  
Johannes Bracher ◽  
Evan L. Ray ◽  
Tilmann Gneiting ◽  
Nicholas G. Reich
Keyword(s):  
Infectious Disease ◽  
Absolute Error ◽  
Forecast Evaluation ◽  
Evaluation Metrics ◽  
Practical Reasons ◽  
Disease Forecasting ◽  
Predictive Distributions ◽  
Probability Score ◽  
Continuous Ranked Probability Score ◽  
Probabilistic Forecasts

For practical reasons, many forecasts of case, hospitalization, and death counts in the context of the current Coronavirus Disease 2019 (COVID-19) pandemic are issued in the form of central predictive intervals at various levels. This is also the case for the forecasts collected in the COVID-19 Forecast Hub (https://covid19forecasthub.org/). Forecast evaluation metrics like the logarithmic score, which has been applied in several infectious disease forecasting challenges, are then not available as they require full predictive distributions. This article provides an overview of how established methods for the evaluation of quantile and interval forecasts can be applied to epidemic forecasts in this format. Specifically, we discuss the computation and interpretation of the weighted interval score, which is a proper score that approximates the continuous ranked probability score. It can be interpreted as a generalization of the absolute error to probabilistic forecasts and allows for a decomposition into a measure of sharpness and penalties for over- and underprediction.

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