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Hybrid <Emphasis Type="Italic">N</Emphasis>-order Lagrangian interpolation Eulerian-Lagrangian method for salinity calculation
Authors:Yan-cheng Wu  Shou-xian Zhu  Lin Zhou  Xiao-bao You  Wen-jing Zhang
Institution:1.Institute of Meteorology and Oceanography,PLA University of Science and Technology,Nanjing,China;2.College of Oceanography,Hohai University,Nanjing,China;3.China Satellite Marine Tracking and Control Department,Jiangyin,China;4.Institute of Atmospheric Environment,PLA General Staff,Beijing,China
Abstract:The Eulerian-Lagrangian method (ELM) has been used by many ocean models as the solution of the advection equation, but the numerical error caused by interpolation imposes restriction on its accuracy. In the present study, hybrid N-order Lagrangian interpolation ELM (LiELM) is put forward in which the N-order Lagrangian interpolation is used at first, then the lower order Lagrangian interpolation is applied in the points where the interpolation results are abnormally higher or lower. The calculation results of a step-shaped salinity advection model are analyzed, which show that higher order (N=3-8) LiELM can reduce the mean numerical error of salinity calculation, but the numerical oscillation error is still significant. Even number order LiELM makes larger numerical oscillation error than its adjacent odd number order LiELM. Hybrid N-order LiELM can remove numerical oscillation, and it significantly reduces the mean numerical error when N is even and the current is in fixed direction, while it makes less effect on mean numerical error when N is odd or the current direction changes periodically. Hybrid odd number order LiELM makes less mean numerical error than its adjacent even number order LiELM when the current is in the fixed direction, while the mean numerical error decreases as N increases when the current direction changes periodically, so odd number of N may be better for application. Among various types of Hybrid N-order LiELM, the scheme reducing N-order directly to 1st-order may be the optimal for synthetic selection of accuracy and computational efficiency.
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