Abstract Variational Data Assimilation (DA) has been broadly used in engineering problems
for field reconstruction and prediction by performing a weighted combination of multiple …

Data assimilation (DA) aims to optimally combine model forecasts and observations that are
both partial and noisy. Multi‐model DA generalizes the variational or Bayesian formulation …

Earth system models suffer from various structural and parametric errors in their
representation of nonlinear, multi‐scale processes, leading to uncertainties in their long …

Weather forecasting is crucial for scientific research and society. Recently, deep learning
(DL) methods have achieved significant advancements in medium-range weather …

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 …

Data assimilation (DA) is a powerful technique for improving the forecast accuracy of
dynamic systems by optimally integrating model forecasts with observations. Traditional DA …

Bayesian inference offers a robust framework for updating prior beliefs based on new data
using Bayes' theorem, but exact inference is often computationally infeasible, necessitating …

Advances in data assimilation (DA) methods and the increasing amount of observations
have continuously improved the accuracy of initial fields in numerical weather prediction …

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 …

The ensemble forecast dominates the computational cost of many data assimilation
methods, especially for high‐resolution and coupled models. In situations where the cost is …