@article{101081,
  keywords = {data assimilation, Coupled models, Ensembles, Kalman filters, Mathematical and statistical techniques, Models and modeling},
  author = {Feiyu Lu and Zhengyu Liu and Shaoqing Zhang and Yun Liu and Robert Jacob},
  title = {Strongly Coupled Data Assimilation Using Leading Averaged Coupled Covariance (LACC). Part II: CGCM Experiments*},
  abstract = { This paper uses a fully coupled general circulation model (CGCM) to study the leading averaged coupled covariance (LACC) method in a strongly coupled data assimilation (SCDA) system. The previous study in a simple coupled climate model has shown that, by calculating the coupled covariance using the leading averaged atmospheric states, the LACC method enhances the signal-to-noise ratio and improves the analysis quality of the slow model component compared to both the traditional weakly coupled data assimilation without cross-component adjustments (WCDA) and the regular SCDA using the simultaneous coupled covariance (SimCC).Here in Part II, the LACC method is tested with a CGCM in a perfect-model framework. By adding the observational adjustments from the low-level atmosphere temperature to the sea surface temperature (SST), the SCDA using LACC significantly reduces the SST error compared to WCDA over the globe; it also improves from the SCDA using SimCC, which performs better than the WCDA only in ... },
  year = {2015},
  journal = {Monthly Weather Review},
  volume = {143},
  pages = {4645{\textendash}4659},
  month = {11/2015},
  issn = {0027-0644},
  url = {http://journals.ametsoc.org/doi/abs/10.1175/MWR-D-15-0088.1},
  doi = {10.1175/MWR-D-15-0088.1},
  language = {eng},
}