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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Cited articles

Andrieu, C. and Roberts, G. O.: The pseudo-marginal approach for efficient Monte Carlo computations, Ann. Statist., 37, 697–725, https://doi.org/10.1214/07-AOS574, 2009. a, b
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Borovkova, S., Burton, R., and Dehling, H.: Limit theorems for functionals of mixing processes with applications to U-statistics and dimension estimation, T. Am. Math. Soc., 353, 4261–4318, https://doi.org/10.1090/S0002-9947-01-02819-7, 2001. a
Cencini, M., Cecconi, F., and Vulpiani, A.: Chaos: From Simple Models to Complex Systems, World Scientific: Series on advances in statistical mechanics, WORLD SCIENTIFIC, https://doi.org/10.1142/7351, 2010. a
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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.