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
Peter Rayner,Anna M. Michalak,and Frédéric Chevallier
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.
Received: 09 Jun 2016 – Discussion started: 05 Jul 2016
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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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