We commonly refer to state estimation theory in geosciences as data assimilation (DA). This
term encompasses the entire sequence of operations that, starting from the observations of a …

Progress within physical oceanography has been concurrent with the increasing
sophistication of tools available for its study. The incorporation of machine learning (ML) …

Recent progress in machine learning has shown how to forecast and, to some extent, learn
the dynamics of a model from its output, resorting in particular to neural networks and deep …

We describe a new approach that allows for systematic causal attribution of weather and
climate-related events, in near-real time. The method is designed so as to facilitate its …

We review the field of data assimilation (DA) from a Bayesian perspective and show that, in
addition to its by now common application to state estimation, DA may be used for model …

Based on a limited number of noisy observations, estimation algorithms provide a complete
description of the state of a system at current time. Estimation algorithms that go under the …

This paper presents a review and analysis of approaches to data assimilation in problems of
geophysical hydrodynamics, from the simplest sequential assimilation schemes to modern …

Simulator imperfection, often known as model error, is ubiquitous in practical data
assimilation problems. Despite the enormous efforts dedicated to addressing this problem …

An alternative formulation of the extended Kalman filter for state and parameter estimation is
presented, referred to as Short‐Time Augmented Extended Kalman Filter (ST‐AEKF). In this …

This chapter provides various perspective on an important challenge in data assimilation:
model error. While the overall goal is to understand the implication of model error of any type …