Now showing items 1-10 of 35
Efficient methods for computing observation impact in 4D-Var data assimilation
An Ensemble Kalman Filter Implementation Based on Modified Cholesky Decomposition for Inverse Covariance Matrix Estimation
This paper develops an efficient implementation of the ensemble Kalman filter based on a modified Cholesky decomposition for inverse covariance matrix estimation. This implementation is named EnKF-MC. Background errors ...
ROSENBROCK-KRYLOV METHODS FOR LARGE SYSTEMS OF DIFFERENTIAL EQUATIONS
(SIAM PUBLICATIONS, 2014-01-01)
Robust data assimilation using $L_1$ and Huber norms
Data assimilation is the process to fuse information from priors, observations of nature, and numerical models, in order to obtain best estimates of the parameters or state of a physical system of interest. Presence of ...
Partitioned and Implicit-Explicit General Linear Methods for Ordinary Differential Equations
(SPRINGER/PLENUM PUBLISHERS, 2014-10-01)
An Efficient Implementation of the Ensemble Kalman Filter Based on Iterative Sherman Morrison Formula
(ELSEVIER SCIENCE BV, 2012-01-01)
HIGH ORDER IMPLICIT-EXPLICIT GENERAL LINEAR METHODS WITH OPTIMIZED STABILITY REGIONS
(SIAM PUBLICATIONS, 2016-01-01)
Cluster Sampling Filters for Non-Gaussian Data Assimilation
This paper presents a fully non-Gaussian version of the Hamiltonian Monte Carlo (HMC) sampling filter. The Gaussian prior assumption in the original HMC filter is relaxed. Specifically, a clustering step is introduced after ...
Low-rank approximations for computing observation impact in 4D-Var data assimilation
(PERGAMON-ELSEVIER SCIENCE LTD, 2014-07-01)