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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Latest update: 17 Jun 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.