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 …

A central research challenge for the mathematical sciences in the twenty-first century is the
development of principled methodologies for the seamless integration of (often vast) data …

This book places data assimilation (DA) into the broader context of inverse problems and the
theory, methods, and algorithms that are used for their solution. It strives to provide a …

This paper presents a new data assimilation approach based on the particle filter (PF) that
has potential for nonlinear/non-Gaussian applications in geoscience. Particle filters provide …

This article presents a novel approach to couple a deterministic four‐dimensional variational
(4DVAR) assimilation method with the particle filter (PF) ensemble data assimilation system …

Particle filtering is a generic weighted ensemble data assimilation method based on
sequential importance sampling, suited for nonlinear and non-Gaussian filtering problems …

Progress toward the Application of a Localized Particle Filter for Numerical Weather Prediction
in: Monthly Weather Review Volume 147 Issue 4 (2019) Jump to Content Jump to Main …

This book enables readers to understand, model, and predict complex dynamical systems
using new methods with stochastic tools. The author presents a unique combination of …

Abstract Solving the Fokker–Planck equation for high-dimensional complex turbulent
dynamical systems is an important and practical issue. However, most traditional methods …

Lagrangian data assimilation exploits the trajectories of moving tracers as observations to
recover the underlying flow field. One major challenge in Lagrangian data assimilation is the …