Journal cover Journal topic
Advances in Statistical Climatology, Meteorology and Oceanography An international open-access journal on applied statistics
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 171-192, 2016
http://www.adv-stat-clim-meteorol-oceanogr.net/2/171/2016/
doi:10.5194/ascmo-2-171-2016
© Author(s) 2016. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
14 Dec 2016
Weak constraint four-dimensional variational data assimilation in a model of the California Current System
William J. Crawford1, Polly J. Smith2, Ralph F. Milliff3, Jerome Fiechter1, Christopher K. Wikle4, Christopher A. Edwards1, and Andrew M. Moore1 1Department of Ocean Sciences, University of California, Santa Cruz, CA 95062, USA
2Department of Mathematics and Statistics, University of Reading, Reading RG6 6AX, UK
3Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO 80304, USA
4Department of Statistics, University of Missouri, Columbia, MO 65211, USA
Abstract. A new approach is explored for computing estimates of the error covariance associated with the intrinsic errors of a numerical forecast model in regions characterized by upwelling and downwelling. The approach used is based on a combination of strong constraint data assimilation, twin model experiments, linear inverse modeling, and Bayesian hierarchical modeling. The resulting model error covariance estimates Q are applied to a model of the California Current System using weak constraint four-dimensional variational (4D-Var) data assimilation to compute estimates of the ocean circulation. The results of this study show that the estimates of Q derived following our approach lead to demonstrable improvements in the model circulation estimates and isolate regions where model errors are likely to be important and that have been independently identified in the same model in previously published work.

Citation: Crawford, W. J., Smith, P. J., Milliff, R. F., Fiechter, J., Wikle, C. K., Edwards, C. A., and Moore, A. M.: Weak constraint four-dimensional variational data assimilation in a model of the California Current System, Adv. Stat. Clim. Meteorol. Oceanogr., 2, 171-192, doi:10.5194/ascmo-2-171-2016, 2016.
Publications Copernicus
Download
Short summary
We present a method for estimating intrinsic model error in a model of the California Current System. The estimated model error covariance matrix is used in the weak constraint formulation of the Regional Ocean Modeling System, four-dimensional variational data assimilation system, and comparison of the circulation estimates computed in this way show demonstrable improvement to those computed in the strong constraint formulation, where intrinsic model error is not taken into account.
We present a method for estimating intrinsic model error in a model of the California Current...
Share