Skill-assessments of statistical and Ensemble Kalman Filter data assimilative analyses using surface and deep observations in the Gulf of Mexico

Abstract

A new data assimilation algorithm (Quasi-EnKF) in ocean modeling, based on the Ensemble Kalman Filter scheme, is proposed in this paper. This algorithm assimilates not only surface measurements (sea surface height), but also deep (∼2000 m) temperature observations from the Gulf of Mexico into regional ocean models. With the use of the Princeton Ocean Model (POM), integrated for approximately two years by assimilating both surface and deep observations, this new algorithm was compared to an existing assimilation algorithm (Mellor-Ezer Scheme) at different resolutions. The results show that, by comparing the observations, the new algorithm outperforms the existing one.

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Correspondence to Zhibin Sun.

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Sun, Z., Oey, L. & Zhou, Y. Skill-assessments of statistical and Ensemble Kalman Filter data assimilative analyses using surface and deep observations in the Gulf of Mexico. Front. Earth Sci. 7, 271–281 (2013). https://doi.org/10.1007/s11707-013-0377-8

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Keywords

  • data assimilation
  • deep observation
  • Gulf of Mexico