A New Open Source Implementation of Lagrangian Filtering: A Method to Identify Internal Waves in High‐Resolution Simulations
A New Open Source Implementation of Lagrangian Filtering: A Method to Identify Internal Waves in High‐Resolution Simulations
复制标题
拉格朗日滤波的新开源实现：一种在高分辨率仿真中识别内波的方法
DOI：
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发表时间：
2021
影响因子：
6.8
通讯作者：
Nick Velzeboer
中科院分区：
文献类型：
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作者：
C. Shakespeare;A. Gibson;A. Hogg;S. Bachman;S. Keating;Nick Velzeboer
复杂流场中内波的识别是流体动力学、海洋学和大气科学中一个长期存在的问题，这是由于内波的时空尺度与其他流态的重叠。拉格朗日滤波，也就是说，在一个参考帧的时间滤波与流动是一个建议的方法进行这种分离。在这里，我们（a）描述了拉格朗日滤波方法的改进实现，（B）介绍了一个新的免费提供的，并行化的Python包，应用该方法。我们表明，该软件包可用于直接过滤输出的各种常见的海洋模型，包括MITgcm，区域海洋建模系统和MOM5的区域和全球域的高分辨率。拉格朗日滤波被证明是一种有用的工具，既可以识别（从而量化）内波，也可以去除内波以隔离非波流场。
Identifying internal waves in complex flow fields is a long‐standing problem in fluid dynamics, oceanography and atmospheric science, owing to the overlap of internal waves temporal and spatial scales with other flow regimes. Lagrangian filtering—that is, temporal filtering in a frame of reference moving with the flow—is one proposed methodology for performing this separation. Here we (a) describe an improved implementation of the Lagrangian filtering methodology and (b) introduce a new freely available, parallelized Python package that applies the method. We show that the package can be used to directly filter output from a variety of common ocean models including MITgcm, Regional Ocean Modeling System and MOM5 for both regional and global domains at high resolution. The Lagrangian filtering is shown to be a useful tool to both identify (and thereby quantify) internal waves, and to remove internal waves to isolate the non‐wave flow field.
影响因子：
3.5
作者：
Naveira Garabato A
通讯作者：
Naveira Garabato A