Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 14, issue 7
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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Latest update: 22 Nov 2024
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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.
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