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Correlation-Cutoff Method for Covariance Localization in Strongly Coupled Data Assimilation
  • Takuma Yoshida,
  • Eugenia Kalnay
Takuma Yoshida
University of Maryland College Park

Corresponding Author:[email protected]

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Eugenia Kalnay
University of Maryland College Park
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Due to its inherent ability to estimate the background error covariances, an ensemble Kalman filter (EnKF) is thought to be a practical approach to the strongly coupled data assimilation problems, where an entire coupled model state is estimated as if it was a single integrated system. However, increased complexity and the multiple time scale of the coupled system aggravate the rank-deficiency and spurious correlation problems caused by limited ensemble size available for the analysis. To alleviate these problems, a distance-independent localization method to systematically select the observations to be assimilated into each model variable has been developed and successfully tested with a nine-variable coupled model with slow and fast modes. This method, called correlation-cutoff method, utilizes the mean squared ensemble error correlation between each observable and model variable to identify where the cross-update should be used, and we cut off the assimilation of observations when the squared error correlation becomes small. To implement the method on a more realistic model, we thoroughly investigate inter-fluid background covariances in an atmosphere-ocean coupled general circulation model where the spatiotemporal scales of coupled dynamics significantly vary by latitudes and driving processes.
01 Sep 2018Published in Monthly Weather Review volume 146 issue 9 on pages 2881-2889. 10.1175/MWR-D-17-0365.1