Submitted as: methods for assessment of models 05 Jul 2016
Submitted as: methods for assessment of models | 05 Jul 2016
Fundamentals of Data Assimilation
- 1School of Earth Sciences, University of Melbourne, Melbourne, Australia
- 2Dept. of Global Ecology, Carnegie Institution for Science, Stanford, USA
- 3Laboratoire des Sciences du Climat et de l’Environnement, Gif sur Yvette, France
- 1School of Earth Sciences, University of Melbourne, Melbourne, Australia
- 2Dept. of Global Ecology, Carnegie Institution for Science, Stanford, USA
- 3Laboratoire des Sciences du Climat et de l’Environnement, Gif sur Yvette, France
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 et al.


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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


-
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
Peter Rayner et al.
Peter Rayner et al.
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Cited
7 citations as recorded by crossref.
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- Global inverse modeling of CH<sub>4</sub> sources and sinks: an overview of methods S. Houweling et al. 10.5194/acp-17-235-2017
- 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
- Arctic Mission Benefit Analysis: impact of sea ice thickness, freeboard, and snow depth products on sea ice forecast performance T. Kaminski et al. 10.5194/tc-12-2569-2018
- Validation practices for satellite-based Earth observation data across communities A. Loew et al. 10.1002/2017RG000562