摘要
基于整体 Kalman 过滤器计划，在海洋建模的一个新数据吸收算法(Quasi-EnKF ) 在这份报纸被建议。这个算法吸收表面大小(海表面高度) 不仅，而且深(2000 m ) 从进地区性的海洋模型的墨西哥湾的温度观察。与普林斯顿海洋模型(POM ) 的使用，为由吸收表面和深观察的约二年综合，这个新算法在不同决定与一个存在吸收算法(Mellor-Ezer 计划) 相比。结果证明由比较观察，新算法超过存在那个[出版摘要]
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.
