Add to ORKGThe ensemble Kalman lter (EnKF) is a 4D data assimilation method that uses a Monte-Carlo ensemble of short-range forecasts to estimate the covariances of the forecast error (Evensen 1994; Burgers et al. 1998; Houtekamer and Mitchell 1998). It is a close approximation to the standard Kalman lter. The approximation becomes more accurat
Abstract The ensemble Kalman filter (EnKF) has been widely used in atmosphere, ocean, and land appli...
The ensemble Kalman filter (EnKF) is a Monte Carlo-based implementation of the Kalman filter (KF) fo...
Ensemble-based data assimilation is a state estimation technique that uses short-term ensemble forec...
The ensemble Kalman filter (EnKF) is a 4-dimensional data-assimilation method that uses a Monte-Carl...
Abstract. The ensemble Kalman filter (EnKF) is a recursive filter suitable for problems with a large...
To implement Bayes’ Theorem for data assimilation, an ensemble Kalman filter (EnKF) uses a set of mo...
The ensemble Kalman filter (EnKF; Evensen 1994; Houtekamer and Mitchell 1998) is currently being tes...
The ensemble Kalman filter (EnKF) has been proposed for operational atmospheric data assimilation. S...
The ensemble Kalman filter (EnKF) takes an advantage of the ensemble prediction technique to estimat...
The term ‘asynchronous data assimilation’ (ADA) refers to modifications of sequential data assimilat...
A 4-dimensional ensemble Kalman filter method (4DEnKF), which adapts ensemble Kalman filtering to th...
A four-dimensional variational data assimilation (4DVAR) algorithm is compared to an ensemble Kal-ma...
Data assimilation considers the problem of using a variety of data to calibrate model-based estimate...
This study examines the performance of coupling the deterministic four-dimensional variational assim...
International audienceThe ensemble Kalman filter (EnKF) is a powerful data assimilation method meant...
Abstract The ensemble Kalman filter (EnKF) has been widely used in atmosphere, ocean, and land appli...
The ensemble Kalman filter (EnKF) is a Monte Carlo-based implementation of the Kalman filter (KF) fo...
Ensemble-based data assimilation is a state estimation technique that uses short-term ensemble forec...
The ensemble Kalman filter (EnKF) is a 4-dimensional data-assimilation method that uses a Monte-Carl...
Abstract. The ensemble Kalman filter (EnKF) is a recursive filter suitable for problems with a large...
To implement Bayes’ Theorem for data assimilation, an ensemble Kalman filter (EnKF) uses a set of mo...
The ensemble Kalman filter (EnKF; Evensen 1994; Houtekamer and Mitchell 1998) is currently being tes...
The ensemble Kalman filter (EnKF) has been proposed for operational atmospheric data assimilation. S...
The ensemble Kalman filter (EnKF) takes an advantage of the ensemble prediction technique to estimat...
The term ‘asynchronous data assimilation’ (ADA) refers to modifications of sequential data assimilat...
A 4-dimensional ensemble Kalman filter method (4DEnKF), which adapts ensemble Kalman filtering to th...
A four-dimensional variational data assimilation (4DVAR) algorithm is compared to an ensemble Kal-ma...
Data assimilation considers the problem of using a variety of data to calibrate model-based estimate...
This study examines the performance of coupling the deterministic four-dimensional variational assim...
International audienceThe ensemble Kalman filter (EnKF) is a powerful data assimilation method meant...
Abstract The ensemble Kalman filter (EnKF) has been widely used in atmosphere, ocean, and land appli...
The ensemble Kalman filter (EnKF) is a Monte Carlo-based implementation of the Kalman filter (KF) fo...
Ensemble-based data assimilation is a state estimation technique that uses short-term ensemble forec...