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https://doi.org/10.5194/gmd-2016-148
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-2016-148
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
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
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.
How to cite. Rayner, P., Michalak, A. M., and Chevallier, F.: Fundamentals of Data Assimilation, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2016-148, 2016.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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SC1: 'Comment', Thomas Kaminski, 12 Jul 2016
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RC1: 'An Important Manuscript that will be great introductory reading after revisions', Anonymous Referee #1, 22 Jul 2016
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RC2: 'Inaccurate and does not consider relevant literature', Anonymous Referee #2, 06 Aug 2016
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RC3: 'Review of "Fundamentals of Data Assimilation"', Anonymous Referee #3, 14 Sep 2016
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AC1: 'response to reviews', Peter Rayner, 09 Jan 2017
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
- Printer-friendly version
- Supplement
-
SC1: 'Comment', Thomas Kaminski, 12 Jul 2016
-
RC1: 'An Important Manuscript that will be great introductory reading after revisions', Anonymous Referee #1, 22 Jul 2016
-
RC2: 'Inaccurate and does not consider relevant literature', Anonymous Referee #2, 06 Aug 2016
-
RC3: 'Review of "Fundamentals of Data Assimilation"', Anonymous Referee #3, 14 Sep 2016
-
AC1: 'response to reviews', Peter Rayner, 09 Jan 2017
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Cited
11 citations as recorded by crossref.
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- Spatiotemporal variations in atmospheric CH4 concentrations and enhancements in northern China based on a comprehensive dataset: ground-based observations, TROPOMI data, inventory data, and inversions P. Han et al. 10.5194/acp-25-4965-2025
- A Comprehensive Assessment of Anthropogenic and Natural Sources and Sinks of Australasia's Carbon Budget Y. Villalobos et al. 10.1029/2023GB007845
- Constraining sector-specific CO2 and CH4 emissions in the US S. Miller & A. Michalak 10.5194/acp-17-3963-2017
- Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling M. Schartau et al. 10.5194/bg-14-1647-2017
- Diagnostic methods for atmospheric inversions of long-lived greenhouse gases A. Michalak et al. 10.5194/acp-17-7405-2017
- Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems M. Scholze et al. 10.5194/bg-14-3401-2017
- Reviews and syntheses: guiding the evolution of the observing system for the carbon cycle through quantitative network design T. Kaminski & P. Rayner 10.5194/bg-14-4755-2017
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Latest update: 17 Jul 2025
Peter Rayner
School of Earth Sciences, University of Melbourne, Melbourne, Australia
Anna M. Michalak
Dept. of Global Ecology, Carnegie Institution for Science, Stanford, USA
Frédéric Chevallier
Laboratoire des Sciences du Climat et de l’Environnement, Gif sur Yvette, France
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.
Numerical models are among our most important tools for understanding and prediction. Models...