We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Error Covariance Estimation for Coupled Data Assimilation Using a Lorenz Atmosphere and a Simple Pycnocline Ocean Model.
- Authors
Han, Guijun; Wu, Xinrong; Zhang, Shaoqing; Liu, Zhengyu; Li, Wei
- Abstract
Coupled data assimilation uses a coupled model consisting of multiple time-scale media to extract information from observations that are available in one or more media. Because of the instantaneous exchanges of information among the coupled media, coupled data assimilation is expected to produce self-consistent and physically balanced coupled state estimates and optimal initialization for coupled model predictions. It is also expected that applying coupling error covariance between two media into observational adjustments in these media can provide direct observational impacts crossing the media and thereby improve the assimilation quality. However, because of the different time scales of variability in different media, accurately evaluating the error covariance between two variables residing in different media is usually very difficult. Using an ensemble filter together with a simple coupled model consisting of a Lorenz atmosphere and a pycnocline ocean model, which characterizes the interaction of multiple time-scale media in the climate system, the impact of the accuracy of coupling error covariance on the quality of coupled data assimilation is studied. Results show that it requires a large ensemble size to improve the assimilation quality by applying coupling error covariance in an ensemble coupled data assimilation system, and the poorly estimated coupling error covariance may otherwise degrade the assimilation quality. It is also found that a fast-varying medium has more difficulty being improved using observations in slow-varying media by applying coupling error covariance because the linear regression from the observational increment in slow-varying media has difficulty representing the high-frequency information of the fast-varying medium.
- Subjects
ANALYSIS of covariance; CLIMATE research; KALMAN filtering; ATMOSPHERIC research; OCEANOGRAPHIC research
- Publication
Journal of Climate, 2013, Vol 26, Issue 24, p10218
- ISSN
0894-8755
- Publication type
Article
- DOI
10.1175/JCLI-D-13-00236.1