Open AccessA New Open Source Implementation of Lagrangian Filtering: A Method to Identify Internal Waves in High‐Resolution Simulations
Author(s) -
Shakespeare Callum J.,
Gibson Angus H.,
Hogg Andrew McC.,
Bachman Scott D.,
Keating Shane R.,
Velzeboer Nick
Publication year - 2021
Publication title -
journal of advances in modeling earth systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.03
H-Index - 58
ISSN - 1942-2466
DOI - 10.1029/2021ms002616
Subject(s) - internal wave , python (programming language) , lagrangian , computer science , flow (mathematics) , filter (signal processing) , high resolution , physical oceanography , geology , algorithm , remote sensing , meteorology , physics , mechanics , mathematics , computer vision , operating system
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.