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

Related authors

A conditional approach for joint estimation of wind speed and direction under future climates
Qiuyi Wu, Julie Bessac, Whitney Huang, Jiali Wang, and Rao Kotamarthi
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 205–224, https://doi.org/10.5194/ascmo-8-205-2022,https://doi.org/10.5194/ascmo-8-205-2022, 2022
Short summary
Comparison of hidden and observed regime-switching autoregressive models for (u, v)-components of wind fields in the northeastern Atlantic
Julie Bessac, Pierre Ailliot, Julien Cattiaux, and Valerie Monbet
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 1–16, https://doi.org/10.5194/ascmo-2-1-2016,https://doi.org/10.5194/ascmo-2-1-2016, 2016
Short summary

Related subject area

Statistics
Parametric model for post-processing visibility ensemble forecasts
Ágnes Baran and Sándor Baran
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 105–122, https://doi.org/10.5194/ascmo-10-105-2024,https://doi.org/10.5194/ascmo-10-105-2024, 2024
Short summary
Spatiotemporal methods for estimating subsurface ocean thermal response to tropical cyclones
Addison J. Hu, Mikael Kuusela, Ann B. Lee, Donata Giglio, and Kimberly M. Wood
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 69–93, https://doi.org/10.5194/ascmo-10-69-2024,https://doi.org/10.5194/ascmo-10-69-2024, 2024
Short summary
Applying different methods to model dry and wet spells at daily scale in a large range of rainfall regimes across Europe
Giorgio Baiamonte, Carmelo Agnese, Carmelo Cammalleri, Elvira Di Nardo, Stefano Ferraris, and Tommaso Martini
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 51–67, https://doi.org/10.5194/ascmo-10-51-2024,https://doi.org/10.5194/ascmo-10-51-2024, 2024
Short summary
Comparison of climate time series – Part 5: Multivariate annual cycles
Timothy DelSole and Michael K. Tippett
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 1–27, https://doi.org/10.5194/ascmo-10-1-2024,https://doi.org/10.5194/ascmo-10-1-2024, 2024
Short summary
Regridding uncertainty for statistical downscaling of solar radiation
Maggie D. Bailey, Douglas Nychka, Manajit Sengupta, Aron Habte, Yu Xie, and Soutir Bandyopadhyay
Adv. Stat. Clim. Meteorol. Oceanogr., 9, 103–120, https://doi.org/10.5194/ascmo-9-103-2023,https://doi.org/10.5194/ascmo-9-103-2023, 2023
Short summary

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
Download
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.