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 outperforms the existing one. 其他題名： Front Earth Sci 其他題名： Front. Earth Sci 其他題名： Frontiers of Earth Science 出版者： Berlin/Heidelberg: Springer-Verlag 出版日期： 2013-09-01 出處： Frontiers of earth science, 2013-09, Vol.7 (3), p.271-281 資源來源： Agricultural & Environmental Science Collection 版權： Copyright reserved, 2014, Higher Education Press and Springer-Verlag Berlin Heidelberg 版權： Higher Education Press and Springer-Verlag Berlin Heidelberg 2013 識別號： ISSN: 2095-0195 識別號： EISSN: 2095-0209 識別號： DOI: 10.1007/s11707-013-0377-8
