Data assimilation (DA) aims to optimally combine model forecasts and observations that are
both partial and noisy. Multi‐model DA generalizes the variational or Bayesian formulation …

Because it uses a set of model integrations to simulate the temporally varying background
probability distribution function and implement Bayes' theorem, the ensemble Kalman filter …

Data assimilation combines information from models, measurements, and priors to obtain
improved estimates of the state of a dynamical system such as the atmosphere. Ensemble …

Abstract The ensemble Kalman filter (EnKF) has been widely used in atmosphere, ocean,
and land applications. The observing network has been significantly developed, and thus …

Abstract The ensemble Kalman filter (EnKF) is a widely used data assimilation method that
has the capacity to sequentially update system parameters and states as new observations …

In this work, we present an efficient matrix-free ensemble Kalman filter (EnKF) algorithm for
the assimilation of large data sets. The EnKF has increasingly become an essential tool for …

This study examines the performance of coupling the deterministic four-dimensional
variational assimilation system (4DVAR) with an ensemble Kalman filter (EnKF) to produce a …

Accounting for Model Error in Variational Data Assimilation: A Deterministic Formulation in:
Monthly Weather Review Volume 138 Issue 9 (2010) Jump to Content Jump to Main Navigation …

Abstract The ensemble Kalman filter (EnKF) is widely used for sequential data assimilation.
It operates as a succession of forecast and analysis steps. In realistic large-scale …

An adaptive estimation of forecast error covariance matrices is proposed for Kalman filtering
data assimilation. A forecast error covariance matrix is initially estimated using an ensemble …