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 …

Variational and ensemble methods have been developed separately by various research
and development groups and each brings its own benefits to data assimilation. In the last …

Characterizing and forecasting the state of the ocean is essential for various scientific,
management, commercial, and recreational applications. This is, however, a challenging …

Hybrid ensemble–variational assimilation methods that combine static and flow-dependent
background error covariances have been widely applied for numerical weather predictions …

We investigate how ideas from covariance localization in numerical weather prediction can
be used in Markov chain Monte Carlo (MCMC) sampling of high-dimensional posterior …

This article provides a detailed theoretical analysis of methods to approximate the solutions
of high‐dimensional (> 106) linear Bayesian problems. An optimal low‐rank projection that …

The ocean state off Oregon-Washington, US West coast, is highly variable in time. Under
these conditions the assumption made in traditional 4-dimensional variational data …

Can artificial intelligence (AI) models beat traditional numerical weather prediction (NWP)
models based on physical principles? The rapid advancement of AI, inherent computational …

Given the success of 4D‐variational methods (4D‐Var) in numerical weather prediction, and
recent efforts to merge ensemble Kalman filters with 4D‐Var, we revisit how one can use …

We show how to replace an ensemble of variational data assimilation minimizations by
minimizations for the ensemble mean and the perturbations from it. Tests using the Met …