Preprints
https://doi.org/10.5194/gmd-2022-40
https://doi.org/10.5194/gmd-2022-40
Submitted as: development and technical paper
04 Mar 2022
Submitted as: development and technical paper | 04 Mar 2022
Status: this preprint is currently under review for the journal GMD.

An ensemble Kalman filter system with the Stony Brook Parallel Ocean Model v1.0

Shun Ohishi1,2,3, Tsutomu Hihara4, Hidenori Aiki3,5, Joji Ishizaka3, Yasumasa Miyazawa5, Misako Kachi6, and Takemasa Miyoshi1,2,7 Shun Ohishi et al.
  • 1RIKEN Center for Computational Science, Kobe, 6500047, Japan
  • 2RIKEN Cluster for Pioneering Research, Kobe, 6500047, Japan
  • 3Institute for Space-Earth Environmental Research, Nagoya University, Nagoya, 4648601, Japan
  • 4Japan Fisheries Information Service Center, Tokyo, 1040055, Japan
  • 5Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama, 2360001, Japan
  • 6Earth Observation Research Center, Japan Aerospace Exploration Agency, Tsukuba, 3058505, Japan
  • 7RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS), Kobe, 6500047, Japan

Abstract. This study develops an ensemble Kalman filter (EnKF)-based regional ocean data assimilation system, in which the local ensemble transform Kalman filter (LETKF) is implemented with the Stony Brook Parallel Ocean Model (sbPOM) version 1.0 to assimilate satellite and in-situ observations at a daily frequency. A series of sensitivity experiments are performed with various settings of the incremental analysis update (IAU) and covariance inflation methods, for which the relaxation-to-prior perturbations and spread (RTPP and RTPS, respectively) and multiplicative inflation (MULT) are considered. We evaluate the geostrophic balance and the analysis accuracy compared with the control experiment in which the IAU and covariance inflation are not applied. The results show that the IAU improves the geostrophic balance, degrades the accuracy, and reduces the ensemble spread, and that the RTPP and RTPS have the opposite effect. The experiment using the combination of the IAU and RTPP results in significant improvement for both balance and accuracy when the RTPP parameter is 0.8–0.9. The combination of the IAU and RTPS improves the balance when the RTPS parameter is ≤ 0.8 and increases the accuracy for the parameter values between 1.0 and 1.1, but the balance and accuracy are not improved significantly at the same time. The experiments with MULT do not demonstrate sufficient skill in maintaining the balance and reproducing the surface flow field regardless of whether the IAU is applied or not. Therefore, the combination of the IAU and RTPP with the parameter of 0.8–0.9 is found to be the best setting for the EnKF-based ocean data assimilation system.

Shun Ohishi 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 gmd-2022-40', Anonymous Referee #1, 30 Mar 2022
    • AC2: 'Reply on RC1', Shun Ohishi, 19 May 2022
  • RC2: 'Review of "An ensemble Kalman filter system with the Stony Brook Parallel Ocean Model v1.0" by Shun Ohishi et al.', Anonymous Referee #2, 30 Mar 2022
    • AC3: 'Reply on RC2', Shun Ohishi, 19 May 2022
  • RC3: 'Comment on gmd-2022-40', Anonymous Referee #3, 03 Apr 2022
    • AC4: 'Reply on RC3', Shun Ohishi, 19 May 2022
  • CEC1: 'Comment on gmd-2022-40', Juan Antonio Añel, 21 Apr 2022
    • AC1: 'Reply on CEC1', Shun Ohishi, 01 May 2022

Shun Ohishi et al.

Shun Ohishi et al.

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Short summary
We develop an ensemble Kalman filter-based regional ocean data assimilation system, in which satellite and in-situ observations are assimilated at a daily frequency. We find the best setting for dynamical balance and analysis accuracy based on sensitivity experiments on how to inflate the ensemble spread and how to apply the analysis update to the model evolution. This study has a broader impact on more general data assimilation systems in which the initial shocks are a significant issue.