Preprints
https://doi.org/10.5194/npg-2022-19
https://doi.org/10.5194/npg-2022-19
06 Jan 2023
 | 06 Jan 2023
Status: a revised version of this preprint is currently under review for the journal NPG.

Using orthogonal vectors to improve the ensemble space of the EnKF and its effect on data assimilation and forecasting

Yung-Yun Cheng, Shu-Chih Yang, Zhe-Hui Lin, and Yung-An Lee

Abstract. The space spanned by the background ensemble provides a basis for correcting forecast errors in the ensemble Kalman filter. However, the ensemble space may not fully capture the forecast errors due to the limited ensemble size and systematic model errors, which affect the assimilation performance. This study proposes a new algorithm to generate pseudo members to properly expand the ensemble space during the analysis step. The pseudomembers adopt vectors orthogonal to the original ensemble and are included in the ensemble using the centered spherical simplex ensemble method. The new algorithm is investigated with a six-member ensemble Kalman filter implemented in the Lorenz 40-variable model. Our results suggest that orthogonal vectors with the ensemble singular vector or ensemble mean vector can serve as effective pseudomembers for improving the analysis accuracy, especially when the background has large errors.

Yung-Yun Cheng et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on npg-2022-19', Anonymous Referee #1, 06 Feb 2023
    • AC1: 'Reply on RC1', Shu-Chih Yang, 09 May 2023
  • RC2: 'Comment on npg-2022-19', Anonymous Referee #2, 07 Mar 2023
    • AC2: 'Reply on RC2', Shu-Chih Yang, 09 May 2023

Yung-Yun Cheng et al.

Yung-Yun Cheng et al.

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Short summary
In ensemble Kalman filter, the ensemble space may not fully capture the forecast errors due to the limited ensemble size and systematic model errors, which affect the accuracy of analysis and prediction. This study proposes a new algorithm to use cost-free pseudomembers to properly expand the ensemble space and improve analysis accuracy during the analysis step, without increasing the ensemble size during forecasting.