An important class of nonlinear weighted least‐squares problems arises from the
assimilation of observations in atmospheric and ocean models. In variational data …

It is well known that the background error covariance matrix in the data assimilation is
anisotropic and varied with the flow field, that is, flow-dependent. However, it is generally …

We have developed a global ocean and sea‐ice ensemble forecasting system based on the
operational forecasting ocean assimilation model (FOAM) system run at the Met Office. The …

A new global daily sea‐surface temperature (SST) analysis system has been developed at
Environment and Climate Change Canada (ECCC). All components of the new SST …

An Ensemble of Data Assimilations (EDA) can provide valuable information on the analysis
and short-range forecast uncertainties. The present ECMWF operational ocean analysis and …

An observation operator in data assimilation was formalized based on the signatures
extracted from the integral quantities contained within observed vertical profiles in the …

The joint state and parameter estimation problem is an important issue in data assimilation.
An adjoint free data assimilation method, namely analytical four-dimensional ensemble …

We investigate the use of multilevel Monte Carlo (MLMC) methods for estimating the
expectation of discretized random fields. Specifically, we consider a setting in which the …

An anisotropic diffusion filter can be used to model a flow-dependent background error
covariance matrix, which can be achieved by solving the advection-diffusion equation …

Variational data assimilation in ocean models depends on the ability to model general
correlation operators in the presence of coastlines. Grid-point filters based on diffusion …