Articles | Volume 14, issue 7
https://doi.org/10.5194/gmd-14-4319-2021
https://doi.org/10.5194/gmd-14-4319-2021
Methods for assessment of models
 | 
09 Jul 2021
Methods for assessment of models |  | 09 Jul 2021

Efficient Bayesian inference for large chaotic dynamical systems

Sebastian Springer, Heikki Haario, Jouni Susiluoto, Aleksandr Bibov, Andrew Davis, and Youssef Marzouk

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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Sebastian Springer on behalf of the Authors (23 Feb 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (04 Mar 2021) by Rohitash Chandra
RR by Anonymous Referee #2 (05 Mar 2021)
RR by Anonymous Referee #1 (18 Mar 2021)
ED: Publish subject to minor revisions (review by editor) (12 Apr 2021) by Rohitash Chandra
AR by Sebastian Springer on behalf of the Authors (22 Apr 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to minor revisions (review by editor) (07 May 2021) by Rohitash Chandra
AR by Sebastian Springer on behalf of the Authors (17 May 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (29 May 2021) by Rohitash Chandra
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Short summary
Model predictions always contain uncertainty. But in some cases, such as weather forecasting or climate modeling, chaotic unpredictability increases the difficulty to say exactly how much uncertainty there is. We combine two recently proposed mathematical methods to show how the uncertainty can be analyzed in models that are simplifications of true weather models. The results can be extended in the future to show how forecasts from large-scale models can be improved.