Efficient Bayesian inference for large chaotic dynamical systems

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

doi:https://doi.org/10.5194/gmd-14-4319-2021

Articles | Volume 14, issue 7

https://doi.org/10.5194/gmd-14-4319-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-14-4319-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 7

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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Final revised paper (published on 09 Jul 2021)
Supplement to the final revised paper
Preprint (discussion started on 26 Oct 2020)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

- Printer-friendly version

- Supplement

SC1: 'executive editor comment on gmd-2020-350', Astrid Kerkweg, 14 Nov 2020
- AC1: 'Code availability', Sebastian Springer, 07 Dec 2020
RC1: 'Referee report for "Efficient Bayesian inference for large chaotic dynamical systems"', Anonymous Referee #1, 21 Dec 2020
RC2: 'Referee report for "Efficient Bayesian inference for large chaotic dynamical systems"', Anonymous Referee #2, 31 Dec 2020

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

AR by Sebastian Springer on behalf of the Authors (06 Jun 2021)

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.