Add to ORKGThe ensemble Kalman filter (EnKF) takes an advantage of the ensemble prediction technique to estimate the background error covariance in a flow-dependent manner, which enables advanced data assimilation. This feature allows automatic adjustment of the background error covariance, which in general needs to be specified manually within the three- and four-dimensional variational data assimilation (3D/4D-Var) methods as well as optimu
An adaptive ensemble covariance localization technique, previously used in ‘‘local’ ’ forms of the e...
The background error covariance matrix, B, is often used in variational data assimilation for numeri...
This dissertation examines the performance of an ensemble Kalman filter (EnKF) implemented in a meso...
The ensemble Kalman filter (EnKF) is a 4-dimensional data-assimilation method that uses a Monte-Carl...
A new hybrid ensemble Kalman filter/four-dimensional variational data assimilation (EnKF/4D-VAR) app...
Ensemble Kalman Filters (EnKF) have been shown to be more accurate than 3D-Var in data assimilation ...
Local ensemble transform Kalman filter (LETKF) data assimilation, three-dimensional variational data...
Abstract. The ensemble Kalman filter (EnKF) is a recursive filter suitable for problems with a large...
Ensemble-based data assimilation is a state estimation technique that uses short-term ensemble forec...
The ensemble Kalman filter (EnKF) has been proposed for operational atmospheric data assimilation. S...
This study considers a new hybrid three-dimensional variational (3D-Var) and ensemble Kalman filter ...
This paper describes the Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme and...
To implement Bayes’ Theorem for data assimilation, an ensemble Kalman filter (EnKF) uses a set of mo...
The main goal of my research is to improve the performance of the EnKF in assimilating real observat...
A B S T R A C T We present a four-dimensional ensemble Kalman filter (4D-LETKF) that approximately a...
An adaptive ensemble covariance localization technique, previously used in ‘‘local’ ’ forms of the e...
The background error covariance matrix, B, is often used in variational data assimilation for numeri...
This dissertation examines the performance of an ensemble Kalman filter (EnKF) implemented in a meso...
The ensemble Kalman filter (EnKF) is a 4-dimensional data-assimilation method that uses a Monte-Carl...
A new hybrid ensemble Kalman filter/four-dimensional variational data assimilation (EnKF/4D-VAR) app...
Ensemble Kalman Filters (EnKF) have been shown to be more accurate than 3D-Var in data assimilation ...
Local ensemble transform Kalman filter (LETKF) data assimilation, three-dimensional variational data...
Abstract. The ensemble Kalman filter (EnKF) is a recursive filter suitable for problems with a large...
Ensemble-based data assimilation is a state estimation technique that uses short-term ensemble forec...
The ensemble Kalman filter (EnKF) has been proposed for operational atmospheric data assimilation. S...
This study considers a new hybrid three-dimensional variational (3D-Var) and ensemble Kalman filter ...
This paper describes the Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme and...
To implement Bayes’ Theorem for data assimilation, an ensemble Kalman filter (EnKF) uses a set of mo...
The main goal of my research is to improve the performance of the EnKF in assimilating real observat...
A B S T R A C T We present a four-dimensional ensemble Kalman filter (4D-LETKF) that approximately a...
An adaptive ensemble covariance localization technique, previously used in ‘‘local’ ’ forms of the e...
The background error covariance matrix, B, is often used in variational data assimilation for numeri...
This dissertation examines the performance of an ensemble Kalman filter (EnKF) implemented in a meso...