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Defining Southern Ocean fronts using unsupervised classification

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@sdat2 sdat2 released this 10 Sep 20:57
· 140 commits to main since this release
v-0.2
5b8445e

Documentation: https://so-fronts.readthedocs.io/en/latest/

Paper: https://doi.org/10.5194/os-2021-40

Changelog:

  • Improved the geographical plot of hard clustering vs. I-metric (Figure 4 in the paper).
  • Changed velocity comparison from 135m to 2m depth (lowers correlation with Sobel edge detection method slightly).
  • Made README.md more readable.
  • Added new plots to visualise the preprocessing steps:
    • Mean and standard deviation of profiles from sample.
    • Principal components in terms of their effect on the vertical profiles.

Short description

In the Southern Ocean, fronts delineate water masses, which correspond to upwelling and downwelling branches of the overturning circulation. Classically, oceanographers define Southern Ocean fronts as a small number of continuous linear features that encircle Antarctica. However, modern observational and theoretical developments are challenging this traditional framework to accommodate more localized views of fronts [Chapman et al. 2020].

Here we present code for implementing two related methods for calculating fronts from oceanographic data. The first method uses unsupervised classification (specifically, Gaussian Mixture Modeling or GMM) and a novel interclass metric to define fronts. This approach produces a discontinuous, probabilistic view of front location, emphasising the fact that the boundaries between water masses are not uniformly sharp across the entire Southern Ocean.

The second method uses Sobel edge detection to highlight rapid changes [Hjelmervik & Hjelmervik, 2019]. This approach produces a more local view of fronts, with the advantage that it can highlight the movement of individual eddy-like features (such as the Agulhas rings).

  1. Chapman, C. C., Lea, M.-A., Meyer, A., Sallee, J.-B. & Hindell, M. Defining Southern Ocean fronts and their influence on biological and physical processes in a changing climate. Nature Climate Change (2020). https://doi.org/10.1038/s41558-020-0705-4
  2. Maze, G. et al. Coherent heat patterns revealed by unsupervised classification of Argo temperature profiles in the North Atlantic Ocean. Progress in Oceanography (2017). https://doi.org/10.1016/j.pocean.2016.12.008, https://doi.org/10.5281/zenodo.3906236
  3. Hjelmervik, K. B. & Hjelmervik, K. T. Detection of oceanographic fronts on variable water depths using empirical orthogonal functions. IEEE Journal of Oceanic Engineering (2019). https://doi.org/10.1109/JOE.2019.2917456
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