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Advances in Statistical Climatology, Meteorology and Oceanography An international open-access journal on applied statistics
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Here, we propose a classification methodology of various space-time atmospheric datasets into discrete air mass groups homogeneous in temperature and humidity through a probabilistic point of view: both the classification process and the data are probabilistic. Unlike conventional classification algorithms, this methodology provides the probability of belonging to each class as well as the corresponding uncertainty, which can be used in various applications.
Articles | Volume 2, issue 2
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 115–136, 2016
https://doi.org/10.5194/ascmo-2-115-2016
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 115–136, 2016
https://doi.org/10.5194/ascmo-2-115-2016

  12 Oct 2016

12 Oct 2016

Mixture model-based atmospheric air mass classification: a probabilistic view of thermodynamic profiles

Jérôme Pernin et al.

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Short summary
Here, we propose a classification methodology of various space-time atmospheric datasets into discrete air mass groups homogeneous in temperature and humidity through a probabilistic point of view: both the classification process and the data are probabilistic. Unlike conventional classification algorithms, this methodology provides the probability of belonging to each class as well as the corresponding uncertainty, which can be used in various applications.
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