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

Lu, Fei, Weitzel, Nils and Monahan, Adam H. (2019) Joint state-parameter estimation of a nonlinear stochastic energy balance model from sparse noisy data. Open Access Nonlinear Processes in Geophysics, 26 (3). pp. 227-250. DOI 10.5194/npg-26-227-2019.

npg-26-227-2019.pdf - Published Version
Available under License Creative Commons: Attribution 4.0.

Download (5Mb) | Preview

Supplementary data:


While nonlinear stochastic partial differential equations arise naturally in spatiotemporal modeling, inference for such systems often faces two major challenges: sparse noisy data and ill-posedness of the inverse problem of parameter estimation. To overcome the challenges, we introduce a strongly regularized posterior by normalizing the likelihood and by imposing physical constraints through priors of the parameters and states.

We investigate joint parameter-state estimation by the regularized posterior in a physically motivated nonlinear stochastic energy balance model (SEBM) for paleoclimate reconstruction. The high-dimensional posterior is sampled by a particle Gibbs sampler that combines a Markov chain Monte Carlo (MCMC) method with an optimal particle filter exploiting the structure of the SEBM. In tests using either Gaussian or uniform priors based on the physical range of parameters, the regularized posteriors overcome the ill-posedness and lead to samples within physical ranges, quantifying the uncertainty in estimation. Due to the ill-posedness and the regularization, the posterior of parameters presents a relatively large uncertainty, and consequently, the maximum of the posterior, which is the minimizer in a variational approach, can have a large variation. In contrast, the posterior of states generally concentrates near the truth, substantially filtering out observation noise and reducing uncertainty in the unconstrained SEBM.

Document Type: Article
Refereed: Yes
Open Access Journal?: Yes
DOI etc.: 10.5194/npg-26-227-2019
ISSN: 1607-7946
Projects: PalMod
Date Deposited: 24 Jun 2020 07:37
Last Modified: 08 Jan 2021 11:13

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...