Open AccessMixed-Resolution Ensemble Data Assimilation
Author(s) -
Sabrina Rainwater,
Brian R. Hunt
Publication year - 2013
Publication title -
monthly weather review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.862
H-Index - 179
eISSN - 1520-0493
pISSN - 0027-0644
DOI - 10.1175/mwr-d-12-00234.1
Subject(s) - data assimilation , ensemble kalman filter , kalman filter , covariance , computation , ensemble forecasting , covariance matrix , ensemble learning , computer science , algorithm , mathematics , statistics , artificial intelligence , extended kalman filter , meteorology , geography
Ensemble Kalman filters perform data assimilation by forming a background covariance matrix from an ensemble forecast. Most of the literature on ensemble Kalman filters assumes that all ensemble members come from the same model. This article presents and tests a modified local ensemble transform Kalman filter (LETKF) that takes its background covariance from a combination of a high-resolution ensemble and a low-resolution ensemble. The computational time and the accuracy of this mixed-resolution LETKF are explored and compared to the standard LETKF on a high-resolution ensemble, using simulated observation experiments with the Lorenz models II and III (more complex versions of the Lorenz-96 model). In a variety of scenarios, mixed-resolution analysis can obtain higher accuracy with similar computation time (or similar accuracy with a reduced computation time) compared to single-resolution analysis.