Ensemble Kalman methods are widely used for state estimation in the geophysical sciences.
Their success stems from the fact that they take an underlying (possibly noisy) dynamical …

The modeling of multiphase flow in a pipe presents a significant challenge for high-
resolution computational fluid dynamics (CFD) models due to the high aspect ratio (length …

Complex dynamical systems are ubiquitous in many areas, including geoscience,
engineering, neural science, material science, etc. Modeling and predicting complex …

Flow‐dependent background error covariances estimated from short‐term ensemble
forecasts suffer from sampling errors due to limited ensemble sizes. Covariance localization …

Data assimilation (DA) is a key component of many forecasting models in science and
engineering. DA allows one to estimate better initial conditions using an imperfect dynamical …

The accuracy of initial conditions is an important driver of the forecast skill of numerical
weather prediction models. Increases in the quantity of available measurements, particularly …

Deep learning has recently gained attention in the atmospheric and oceanic sciences for its
potential to improve the accuracy of numerical simulations or to reduce computational costs …

This paper introduces a computational framework to reconstruct and forecast a partially
observed state that evolves according to an unknown or expensive-to-simulate dynamical …

We present a data-driven or non-intrusive reduced-order model (NIROM) which is capable
of making predictions for a significantly larger domain than the one used to generate the …

A hybrid data assimilation algorithm is developed for complex dynamical systems with
partial observations. The method starts with applying a spectral decomposition to the entire …