An Adjoint-Based Adaptive Ensemble Kalman Filter

Controlling balance in an ensemble Kalman filter

We present a method to control unbalanced fast dynamics in an ensemble Kalman filter by introducing a weak constraint on the imbalance in a spatially sparse observational network. We show that the balance constraint produces significantly more balanced analyses than ensemble Kalman filters without balance constraints and than filters implementing incremental analysis updates (IAU). Furthermore,...

An Image-based Ensemble Kalman Filter for Motion Estimation

This paper designs an Image-based Ensemble Kalman Filter (IEnKF), whose components are defined only from image properties, to estimate motion on image sequences. The key elements of this filter are, first, the construction of the initial ensemble, and second, the propagation in time of this ensemble on the studied temporal interval. Both are analyzed in the paper and their impact on results is ...

Estimating observation impact without adjoint model in an ensemble Kalman filter

We propose an ensemble sensitivity method to calculate observation impacts similar to Langland and Baker (2004) but without the need for an adjoint model, which is not always available for numerical weather prediction models. The formulation is tested on the Lorenz 40-variable model, and the results show that the observation impact estimated from the ensemble sensitivity method is similar to th...

Extended Kalman Filter Based Fuzzy Adaptive Filter

Adaptive Observation Strategies with the Local Ensemble Transform Kalman Filter

Adaptive observation strategies (AOS) aim to improve forecasts by adding additional observations at a few locations that have no standard observations. Lorenz and Emanuel (1998) designed experiments to evaluate different adaptive strategies with Lorenz 40-variable model. Routine observations are observed over “land” (grid points from 21 to 40) every 6 hours. One adaptive point is chosen from on...