The bulk of this paper contains a concise mathematical overview of the subject of data
assimilation, highlighting three primary ideas:(i) the standard optimization approaches of …

Data assimilation is briefly described in its variational formulation. Both three-and four-
dimensional variational assimilation are presented with an emphasis on their comparison …

Data assimilation aims at determining as accurately as possible the state of a dynamical
system by combining heterogeneous sources of information in an optimal way. Generally …

One of the most popular data assimilation techniques in use today are of the Kalman filter
type, which provide an improved estimate of the state of a system up to the current time level …

This open-access textbook's significant contribution is the unified derivation of data-
assimilation techniques from a common fundamental and optimal starting point, namely …

Dynamic data assimilation is the assessment, combination and synthesis of observational
data, scientific laws and mathematical models to determine the state of a complex physical …

A sequential estimator based on the Ensemble Kalman Filter for Data Assimilation of fluid
flows is presented in this research work. The main feature of this estimator is that the Kalman …

Data assimilation is a methodology to estimate weather or ocean variables by combining
(synchronizing) information from observational data with a numerical dynamical (forecast) …

In variational data assimilation (VDA) for meteorological and/or oceanic models, the
assimilated fields are deduced by combining the model and the gradient of a cost functional …

Data assimilation is a vital step in numerical modeling, particularly in the atmospheric
sciences and oceanography. It allows for problems with uneven spatial and temporal data …