A Profile Classification Model from North-Atlantic Argo temperature data

Date 2017
Author(s) Maze GuillaumeORCID
DOI 10.17882/47106
Publisher SEANOE
Keyword(s) heat content, classification, North Atlantic, stratification, water mass, argo, profile, pattern

A quantitative understanding of the integrated ocean heat content depends on our ability to determine how heat is distributed in the ocean and what are the associated coherent patterns. This dataset contains the results of the Maze et al., 2017 (Prog. Oce.) study demonstrating how this can be achieved using unsupervised classification of Argo temperature profiles.

The dataset contains:

  • A netcdf file with classification~results (labels and probabilities) and coordinates (lat/lon/time) of 100,684 Argo temperature profiles in North Atlantic.

  • A netcdf file with a Profile Classification Model (PCM) that can be used to classify new temperature profiles from observations or numerical models.

The classification method used is a Gaussian Mixture Model that decomposes the Probability Density Function of the dataset into a weighted sum of Gaussian modes. North Atlantic Argo temperature profiles between 0 and 1400m depth were interpolated onto a regular 5m grid, then compressed using Principal Component Analysis and finally classified using a Gaussian Mixture Model.

To use the netcdf PCM file to classify new data, you can checkout our PCM Matlab and Python toolbox here: https://github.com/obidam/pcm

Licence CC-BY
Acknowledgments Herlé Mercier, Ronan Fablet, Pierre Tandéo, Manuel Lopez Radcenco, Philippe Lenca, Charlène Feucher, Clément Le Goff

Get the data via ERDDAP

File Size Format Processing Access
Profile Classification Model 56 KB NC, NetCDF Processed data Open access
Classification results 16 MB NC, NetCDF Processed data Open access
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How to cite 

Maze Guillaume (2017). A Profile Classification Model from North-Atlantic Argo temperature data. SEANOE. https://doi.org/10.17882/47106