A new multiscale, ensemble-based data assimilation (DA) method, multiscale local gain
form ensemble transform Kalman filter (MLGETKF), is introduced. MLGETKF allows …

To ameliorate suboptimality in ensemble data assimilation, methods have been introduced
that involve expanding the ensemble size. Such expansions can incorporate model space …

We have investigated a method to substantially reduce the analysis computations within the
Local Ensemble Transform Kalman Filter (LETKF) framework. Instead of computing the …

Ensemble-based data assimilation is a state estimation technique that uses short-term
ensemble forecasts to estimate flow-dependent background error covariance and is best …

Bédard, J., M. Buehner, J.-F. Caron, SJ Baek, and L. Fillion, 2018: Practical ensemble-based
approaches to estimate atmospheric background error covariances for limited-area …

Ensemble data assimilation methods have been improved consistently and have become a
viable choice in operational numerical weather prediction. A number of issues for further …

Ensemble data assimilation methods assimilate observations using state-space estimation
methods and low-rank representations of forecast and analysis error covariances. A key …

Ensemble Kalman filter methods are typically used in combination with one of two
localization techniques. One technique is covariance localization, or direct forecast error …

Many geophysical problems are characterized by high‐dimensional, nonlinear systems and
pose difficult challenges for real‐time data assimilation (updating) and forecasting. The …

Ensemble data assimilation methods are potentially attractive because they provide a
computationally affordable (and computationally parallel) means of obtaining flow …