Articles | Volume 26, issue 3
https://doi.org/10.5194/npg-26-227-2019
https://doi.org/10.5194/npg-26-227-2019
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, Nils Weitzel, and Adam H. Monahan

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Latest update: 14 Dec 2024
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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.