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

Viewed

Total article views: 3,054 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,733 1,245 76 3,054 82 80
  • HTML: 1,733
  • PDF: 1,245
  • XML: 76
  • Total: 3,054
  • BibTeX: 82
  • EndNote: 80
Views and downloads (calculated since 23 Apr 2019)
Cumulative views and downloads (calculated since 23 Apr 2019)

Viewed (geographical distribution)

Total article views: 3,054 (including HTML, PDF, and XML) Thereof 2,543 with geography defined and 511 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
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.