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

Skill-assessments of statistical and Ensemble Kalman Filter data assimilative analyses using surface and deep observations in the Gulf of Mexico
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摘要 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 out-performs the existing one. 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 out-performs the existing one.
作者 Zhibin SUN Lie-Yauw OEY Yi-Hui ZHOU
机构地区 Universities Space Research Association Princeton University Department of Biostatistics
出处 《Frontiers of Earth Science》 SCIE CAS CSCD 2013年第3期271-281,共11页 地球科学前沿（英文版）
关键词 data assimilation deep observation Gulf of Mexico data assimilation, deep observation, Gulf of Mexico
分类号 P456.7 [天文地球—大气科学及气象学] TN713 [电子电信—电路与系统]
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参考文献27

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Frontiers of Earth Science
2013年 第3期

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