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Open AccessJournal ArticleDOI

Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems

Hans Hersbach
- 01 Oct 2000 - 
- Vol. 15, Iss: 5, pp 559-570
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TLDR
In this article, the continuous ranked probability score (CRPS) is decomposed into a reliability part and a resolution/uncertainty part, in a way similar to the decomposition of the Brier score.
Abstract
Some time ago, the continuous ranked probability score (CRPS) was proposed as a new verification tool for (probabilistic) forecast systems. Its focus is on the entire permissible range of a certain (weather) parameter. The CRPS can be seen as a ranked probability score with an infinite number of classes, each of zero width. Alternatively, it can be interpreted as the integral of the Brier score over all possible threshold values for the parameter under consideration. For a deterministic forecast system the CRPS reduces to the mean absolute error. In this paper it is shown that for an ensemble prediction system the CRPS can be decomposed into a reliability part and a resolution/uncertainty part, in a way that is similar to the decomposition of the Brier score. The reliability part of the CRPS is closely connected to the rank histogram of the ensemble, while the resolution/ uncertainty part can be related to the average spread within the ensemble and the behavior of its outliers. The usefulness of such a decomposition is illustrated for the ensemble prediction system running at the European Centre for Medium-Range Weather Forecasts. The evaluation of the CRPS and its decomposition proposed in this paper can be extended to systems issuing continuous probability forecasts, by realizing that these can be interpreted as the limit of ensemble forecasts with an infinite number of members.

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Citations
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Journal ArticleDOI

Strictly Proper Scoring Rules, Prediction, and Estimation

TL;DR: The theory of proper scoring rules on general probability spaces is reviewed and developed, and the intuitively appealing interval score is proposed as a utility function in interval estimation that addresses width as well as coverage.
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Using Bayesian Model Averaging to Calibrate Forecast Ensembles

TL;DR: The authors proposed a statistical method for postprocessing ensembles based on Bayesian model averaging (BMA), which is a standard method for combining predictive distributions from different sources, and demonstrated that BMA performs reasonably well when the underlying ensemble is calibrated, or even overdispersed.
BookDOI

Forecast verification: a practitioner's guide in atmospheric science

TL;DR: Jolliffe et al. as mentioned in this paper proposed a framework for verification of spatial fields based on binary and categorical events, and proved the correctness of the proposed framework with past, present and future predictions.
Journal ArticleDOI

Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation

TL;DR: This work proposes the use of ensemble model output statistics (EMOS), an easy-to-implement postprocessing technique that addresses both forecast bias and underdispersion and takes into account the spread-skill relationship.
References
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Book

Statistical Methods in the Atmospheric Sciences

TL;DR: The second edition of "Statistical Methods in the Atmospheric Sciences, Second Edition" as mentioned in this paper presents and explains techniques used in atmospheric data summarization, analysis, testing, and forecasting.
Journal ArticleDOI

The ECMWF Ensemble Prediction System: Methodology and validation

TL;DR: The European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) is described in this article, where each ensemble comprises 32 10-day forecasts starting from initial conditions in which dynamically defined perturbations have been added to the operational analysis.
Journal ArticleDOI

A New Vector Partition of the Probability Score

TL;DR: In this article, a new vector partition of the probability, or Brier, score (PS) is formulated and the nature and properties of this partition are described, as well as the relationships between the terms in this partition and terms in the original vector partition.
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