Preprints
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
Review status: this preprint was under review for the journal GMD but the revision was not accepted.
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.
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.
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Peter Rayner et al.
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
Peter Rayner et al.
Peter Rayner et al.
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Cited
7 citations as recorded by crossref.
- Model-data interaction in groundwater studies: Review of methods, applications and future directions M. Rajabi et al. 10.1016/j.jhydrol.2018.09.053
- 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
Latest update: 28 Jan 2021
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...