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Ocean Profile Classification Model in python

Authors: Maze, Guillaume;

Ocean Profile Classification Model in python

Abstract

pyXpcm is a python package to create and work with ocean Profile Classification Model that consumes and produces Xarray objects. Xarray objects are N-D labeled arrays and datasets in Python. An ocean Profile Classification Model allows to automatically assemble ocean profiles in clusters according to their vertical structure similarities. The geospatial properties of these clusters can be used to address a large variety of oceanographic problems: front detection, water mass identification, natural region contouring (gyres, eddies), reference profile selection for QC validation, etc… The vertical structure of these clusters furthermore provides a highly synthetic representation of large ocean areas that can be used for dimensionality reduction and coherent intercomparisons of ocean data (re)-analysis or simulations. Documentation available here: https://pyxpcm.readthedocs.io

{"references": ["Maze G. et al. Coherent heat patterns revealed by unsupervised classification of Argo temperature profiles in the North Atlantic Ocean. Progress in Oceanography (2017). http://dx.doi.org/10.1016/j.pocean.2016.12.008", "Maze, G., et al. Profile Classification Models. Mercator Ocean Journal (2017). http://archimer.ifremer.fr/doc/00387/49816"]}

Keywords

data, classification, ocean, argo

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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