Abstract

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 referenc... Show moreIdentifying 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. Show less