In this review, data assimilation (DA) techniques used for tropical cyclones (TCs) are briefly
overviewed. The strength and weakness of variational methods, ensemble methods, hybrid …

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 …

While contemporary numerical weather prediction models represent the large‐scale
structure of moist atmospheric processes reasonably well, they often struggle to maintain …

Abstract The Met Office Global and Regional Ensemble Prediction System–Global
(MOGREPS‐G) used an ensemble transform Kalman filter (ETKF) to perturb its initial …

A new approach is presented and evaluated for efficiently applying scale-dependent spatial
localisation to ensemble background-error covariances within an ensemble-variational data …

We synthesize knowledge from numerical weather prediction, inverse theory, and statistics
to address the problem of estimating a high‐dimensional covariance matrix from a small …

Variational data assimilation methods are reviewed and compared in the Met Office global
numerical weather prediction system. This supports hybrid background‐error covariances …

Regional estimates of biogenic carbon fluxes over North America from both atmospheric
inversions (“top‐down” approach) and terrestrial biosphere models (“bottom‐up”) remain …

The iterative ensemble Kalman smoother (IEnKS) is a data assimilation method meant for
tracking the state of nonlinear geophysical models efficiently. It combines an ensemble of …

A scale-dependent localization (SDL) method was formulated and implemented in the
Gridpoint Statistical Interpolation (GSI)-based four-dimensional ensemble-variational …