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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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NPG | Articles | Volume 26, issue 3
Nonlin. Processes Geophys., 26, 227–250, 2019
https://doi.org/10.5194/npg-26-227-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Nonlin. Processes Geophys., 26, 227–250, 2019
https://doi.org/10.5194/npg-26-227-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 14 Aug 2019

Research article | 14 Aug 2019

Joint state-parameter estimation of a nonlinear stochastic energy balance model from sparse noisy data

Fei Lu et al.

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AR by Anna Wenzel on behalf of the Authors (09 Jul 2019)  Author's response    Manuscript
ED: Publish as is (18 Jul 2019) by Stefano Pierini
AR by Fei Lu on behalf of the Authors (19 Jul 2019)  Author's response    Manuscript
Publications Copernicus
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Short summary
ll-posedness of the inverse problem and sparse noisy data are two major challenges in the modeling of high-dimensional spatiotemporal processes. We present a Bayesian inference method with a strongly regularized posterior to overcome these challenges, enabling joint state-parameter estimation and quantifying uncertainty in the estimation. We demonstrate the method on a physically motivated nonlinear stochastic partial differential equation arising from paleoclimate construction.
ll-posedness of the inverse problem and sparse noisy data are two major challenges in the...
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