Preprints
https://doi.org/10.5194/npg-2022-19
https://doi.org/10.5194/npg-2022-19
 
06 Jan 2023
06 Jan 2023
Status: 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 Cheng1, Shu-Chih Yang2,3, Zhe-Hui Lin2, and Yung-An Lee2 Yung-Yun Cheng et al.
  • 1Center for Weather Climate and Disaster Research, National Taiwan University, Taipei, Taiwan
  • 2Department of Atmospheric Sciences, National Central University, Taoyuan, Taiwan
  • 3GPS Science and Application Research Center, National Central University, Taoyuan, Taiwan

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: open (until 03 Mar 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

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.