Abstract: A new method to quantify the nonlinearity of data assimilation problems is proposed. The method includes the effects of system errors, measurement errors, observational network, and sampling interval. It is based on ...

Abstract: A number of algorithms to solve large-scale Kalman filtering problems have been introduced recently. The ensemble Kalman filter represents the probability density of the state estimate by a finite number of randomly generated ...

Abstract: This article explores the use of the ensemble Kalman filter technique to improve the accuracy of North Sea wave field analyses. A nonhydrostatic convective-permitting mesoscale model was used to provide high-resolution ...

Abstract: In the past, a number of algorithms have been introduced to solve data assimilation problems for large-scale applications. Here, several Kalman filters, coupled to the European Operational Smog (EUROS) atmospheric chemistry ...