Year: 2024
Type: article
Abstract: Approaches are developed for splitting of data assimilation (DA) problems into manageable pieces so that different DA methods may be applied. A block matrix generalized inverse result allows for the decomposition of Gaussian likelihoods based upon projected data models. A sophisticated implementation of a prototype method is developed and promising results are obtained for different model problems. Numerical techniques are focused on a combined particle filter/Kalman filter (PF-EnKf) using low-dimensional projected optimal proposal-particle filter (OP-PF) and ensemble Kalman filter (EnKf) after stratified resampling. The technique is evaluated using a modified 'storm track' Lorenz '96 model in which the chaotic/laminar solution structure changes in space and time. Results obtained are shown to be competitive when compared with different EnKF methods and unprojected OP-PF.
Authors: John Maclean, Erik S. Van Vleck
Institution: University of Kansas
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4404753637