Full scripts to generate figures for "Defining Southern Ocean fronts using unsupervised classification"
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).
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
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
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
Make the environment:
make env
Activate the environment in conda:
conda activate ./env
Change the settings in src.constants to set download location etc.
Download data (get_zip: 1694.64639 s):
python3 src/data_loading/bsose_download.py
Make I-metric:
python3 src/models/batch_i_metric.py
Make figures:
python3 main.py
├── LICENSE├── Makefile <- Makefile with commands like `make env` or `make `├── README.md <- The top-level README for developers using this project.├── main.py <- The main python script to run.|├── figures <- .png images with non-enumerated names.│├── requirements <- Directory containing the requirement files.│├── setup.py <- makes project pip installable (pip install -e .) so src can be imported from jupyter notebooks etc.|├── src <- Source code for use in this project.| |│ ├── __init__.py <- Makes src a Python module| |│ ├── data <- KO fronts to plot, other data.│ ││ ├── data_loading <- Scripts to download and name data.│ ││ ├── models <- Make I-metric and Sobel edge detection directory.│ ││ ├── plot <- plotting functions directory| |│ ├── plot_utils <- plotting utilities directory| |│ ├── preprocessing <- preprocessing scripts (to transform to density etc.).| || ├── tests <- Scripts for unit tests of your functions| || ├── animate.py <- animate i-metric.| ├── constants.py <- contains majority of run parameters that can be changed.| ├── make_figures.py <- make all figures in one long script.| ├── move_figures.py <- Move figures script (now unnecessary).| | Changes figure names to Figure-X.png etc.| └── time_wrapper.py <- time wrapper to time parts of the program.│└── setup.cfg <- setup configuration file for linting rules
conda working in shell.make in shell.python==3.8.8)Project template created by the
Cambridge AI4ER Cookiecutter.