A new open source implementation of Lagrangian filtering: A method to identify internal waves in high�resolution simulations
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.
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http://n2t.net/ark:/85065/d70z76rs
eng
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publication
2016-01-01T00:00:00Z
publication
2021-10-01T00:00:00Z
Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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