• Corpus ID: 219177313

What limits the number of observations that can be effectively assimilated by EnKF

  title={What limits the number of observations that can be effectively assimilated by EnKF},
  author={Daisuke Hotta and Yoichiro Ota},
  journal={arXiv: Data Analysis, Statistics and Probability},
  • D. Hotta, Yoichiro Ota
  • Published 31 May 2020
  • Environmental Science
  • arXiv: Data Analysis, Statistics and Probability
The ability of ensemble Kalman filter (EnKF) algorithms to extract information from observations is analyzed with the aid of the concept of the degrees of freedom for signal (DFS). A simple mathematical argument shows that DFS for EnKF is bounded from above by the ensemble size, which entails that assimilating much more observations than the ensemble size automatically leads to DFS underestimation. Since DFS is a trace of the posterior error covariance mapped onto the normalized observation… 
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