Laboratoire 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.
Received: 09 Jun 2016 – Discussion started: 05 Jul 2016
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...