A Profile Classification Model from North-Atlantic Argo temperature data
|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:
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
|Acknowledgements||Herlé Mercier, Ronan Fablet, Pierre Tandéo, Manuel Lopez Radcenco, Philippe Lenca, Charlène Feucher, Clément Le Goff|