Articles | Volume 2, issue 4
https://doi.org/10.5194/wcd-2-1209-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wcd-2-1209-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Dec 2021
Research article |
| 15 Dec 2021
| 15 Dec 2021
Bimodality in ensemble forecasts of 2 m temperature: identification
Cameron Bertossa
CORRESPONDING AUTHOR
Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, New York, USA
Peter Hitchcock
Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, New York, USA
Arthur DeGaetano
Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, New York, USA
Riwal Plougonven
LMD-IPSL, École Polytechnique, Institut Polytechnique de Paris, ENS, PSL Research University, Sorbonne Université, CNRS, Paris, France
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This work has identified characteristic spatial and temporal scales for non-Gaussian outbreaks in forecasts, specifically, bimodality. Methodology is introduced which allows one to connect meteorological phenomena to bimodal outbreaks. Large-scale circulation interacting with local processes is uncovered as a frequent ingredient to such outbreaks. These insights not only provide a deeper understanding of the dynamical processes involved, but also have drastic implications for forecast skill.
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Short summary
While the assumption of Gaussianity leads to many simplifications, ensemble forecasts often exhibit non-Gaussian distributions. This work has systematically identified the presence of a specific case of
non-Gaussianity, bimodality. It has been found that bimodality occurs in a large portion of global 2 m temperature forecasts. This has drastic implications on forecast skill as the minimum probability in a bimodal distribution often lies at the maximum probability of a Gaussian distribution.
While the assumption of Gaussianity leads to many simplifications, ensemble forecasts often...
