Many ensemble Kalman filter data assimilation schemes benefit from spatial localization, often in both the horizontal and vertical coordinates. On the other hand, satellite observations are often sensitive to the dynamics over a broad layer of the atmosphere; that is, the observation operator that maps the model state to the observed satellite radiances is a nonlocal function of the state. Simi...

The ensemble Kalman filter (EnKF) approximates background error covariance by using a finite number of ensemble members. Although increasing the ensemble size consistently improves the EnKF analysis, typical applications of the EnKF to realistic atmospheric simulations are conducted with a small ensemble size due to limited computational resources. The finite ensemble size introduces a sampling...

Ensemble-based data assimilation techniques are being explored as possible alternatives to current operational analysis techniques such as threeor four-dimensional variational assimilation. Ensemble-based assimilation techniques utilise an ensemble of parallel data assimilation and forecast cycles. The background-error covariances are estimated using the forecast ensemble and are used to produc...

Kalman filters are widely used for data assimilation into ocean models. The aim of this study is to discuss the relevance of these filters with high resolution ocean models. This was investigated through the comparison of two advanced Kalman filters: the singular evolutive extended Kalman (SEEK) filter and its ensemble-based variant, called SEIK filter. The two filters were implemented with the...

Ensemble Kalman Filters (EnKF) have been shown to be more accurate than 3D-Var in data assimilation simulations under the assumption of a perfect model. However, in reality, the forecast model has deficiencies and does not represent the atmospheric behavior precisely due to lack of resolution, approximate parameterizations of subgrid scale physical processes, and numerical dispersion. For assim...

A variety of filtering methods enable the recursive estimation of system state variables and inference of model parameters. These methods have found application in a range of disciplines and settings, including engineering design and forecasting, and, over the last two decades, have been applied to infectious disease epidemiology. For any system of interest, the ideal filter depends on the nonl...