Articles | Volume 7, issue 2
https://doi.org/10.5194/ascmo-7-53-2021
https://doi.org/10.5194/ascmo-7-53-2021
23 Sep 2021
 | 23 Sep 2021

Forecast score distributions with imperfect observations

Julie Bessac and Philippe Naveau

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Cited articles

Anderson, J. L.: A method for producing and evaluating probabilistic forecasts from ensemble model integrations, J. Clim., 9, 1518–1530, 1996. a
Bessac, J., Constantinescu, E., and Anitescu, M.: Stochastic simulation of predictive space–time scenarios of wind speed using observations and physical model outputs, Ann. Appl. Stat., 12, 432–458, 2018. a, b, c, d, e
Bessac, J.: Codes for scoring under uncertain verification data, available at: https://github.com/jbessac/uncertainty_scoring, GitHub [code], last access: 8 September 2021. a
Bolin, D. and Wallin, J.: Scale invariant proper scoring rules Scale dependence: Why the average CRPS often is inappropriate for ranking probabilistic forecasts, arXiv preprint arXiv:1912.05642, available at: https://arxiv.org/abs/1912.05642 (last access: 8 September 2021), 2019. a, b, c
Bowler, N. E.: Accounting for the effect of observation errors on verification of MOGREPS, Meteorol. Appl., 15, 199–205, 2008. a
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Short summary
We propose a new forecast evaluation scheme in the context of models that incorporate errors of the verification data. We rely on existing scoring rules and incorporate uncertainty and error of the verification data through a hidden variable and the conditional expectation of scores. By considering scores to be random variables, one can access the entire range of their distribution and illustrate that the commonly used mean score can be a misleading representative of the distribution.
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