Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
doi:10.5194/gmd-2016-148
© Author(s) 2016. This work is distributed
under the Creative Commons Attribution 3.0 License.
Methods for assessment of models
05 Jul 2016
Review status
This discussion paper is under review for the journal Geoscientific Model Development (GMD).
Fundamentals of Data Assimilation
Peter Rayner1, Anna M. Michalak2, and Frédéric Chevallier3 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.

Citation: Rayner, P., Michalak, A. M., and Chevallier, F.: Fundamentals of Data Assimilation, Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-148, in review, 2016.
Peter Rayner et al.
Peter Rayner et al.
Peter Rayner et al.

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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...
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