A Tutorial on Bayesian Data Assimilation

TL;DR

This tutorial introduces Bayesian data assimilation, emphasizing its conceptual framework, practical interpretation, and relevance to geophysical applications, with a focus on pedagogical clarity and intuition.

Contribution

It provides a comprehensive overview of Bayesian data assimilation, highlighting its theoretical foundations, practical schemes, and application to geosciences, aimed at practitioners and researchers.

Findings

Bayesian analysis offers a consistent framework for data assimilation.

Different estimation schemes can be formulated within the Bayesian framework.

The tutorial emphasizes intuition and practical interpretation over formal derivations.

Abstract

This tutorial provides a broad introduction to Bayesian data assimilation that will be useful to practitioners, in interpreting algorithms and results, and for theoretical studies developing novel schemes with an understanding of the rich history of geophysical data assimilation and its current directions. The simple case of data assimilation in a 'perfect' model is primarily discussed for pedagogical purposes. Some mathematical results are derived at a high-level in order to illustrate key ideas about different estimators. However, the focus of this work is on the intuition behind these methods, where more formal and detailed treatments of the data assimilation problem can be found in the various references. In surveying a variety of widely used data assimilation schemes, the key message of this tutorial is how the Bayesian analysis provides a consistent framework for the estimation problem and how this allows one to formulate its solution in a variety of ways to exploit the operational challenges in the geosciences.