Preprints
https://doi.org/10.5194/gmd-2016-148
https://doi.org/10.5194/gmd-2016-148
Submitted as: methods for assessment of models
 | 
05 Jul 2016
Submitted as: methods for assessment of models |  | 05 Jul 2016
Status: this preprint was under review for the journal GMD but the revision was not accepted.

Fundamentals of Data Assimilation

Peter Rayner, Anna M. Michalak, and Frédéric Chevallier

Abstract. This article lays out the fundamentals of data assimilation as used in biogeochemistry. It demonstrates that all of the methods in widespread use within the field are special cases of the underlying Bayesian formalism. Methods differ in the assumptions they make and information they provide on the probability distributions used in Bayesian calculations. It thus provides a basis for comparison and choice among these methods. It also provides a standardised notation for the various quantities used in the field.

Peter Rayner, Anna M. Michalak, and Frédéric Chevallier
 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Peter Rayner, Anna M. Michalak, and Frédéric Chevallier
Peter Rayner, Anna M. Michalak, and Frédéric Chevallier

Viewed

Total article views: 2,693 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,729 847 117 2,693 99 131
  • HTML: 1,729
  • PDF: 847
  • XML: 117
  • Total: 2,693
  • BibTeX: 99
  • EndNote: 131
Views and downloads (calculated since 05 Jul 2016)
Cumulative views and downloads (calculated since 05 Jul 2016)

Cited

Latest update: 18 Mar 2024
Download
Short summary
Numerical models are among our most important tools for understanding and prediction. Models include quantities or equations that we cannot verify directly. We learn about these unknowns by comparing model output with observations and using some algorithm to improve the inputs. We show here that the many methods for doing this are special cases of underlying statistics. This provides a unified way of comparing and contrasting such methods.