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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/gmd-2016-148
© Author(s) 2016. This work is distributed under
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.

  05 Jul 2016

05 Jul 2016

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This preprint was under review for the journal GMD but the revision was not accepted.

Fundamentals of Data Assimilation

Peter Rayner1, Anna M. Michalak2, and Frédéric Chevallier3 Peter Rayner et al.
  • 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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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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