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

Pernin, Jérôme; Vrac, Mathieu; Crevoisier, Cyril; Chédin, Alain

doi:https://doi.org/10.5194/ascmo-2-115-2016

Articles | Volume 2, issue 2

https://doi.org/10.5194/ascmo-2-115-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/ascmo-2-115-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 2, issue 2

12 Oct 2016

| 12 Oct 2016

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

Jérôme Pernin, Mathieu Vrac, Cyril Crevoisier, and Alain Chédin

Data sets

EM algorithm CRAN https://cran.r-project.org/web/packages/Rmixmod/index.html

Decision tree CRAN https://cran.r-project.org/web/packages/rpart/index.html

ERA Interim, Daily ECMWF http://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/

U.S. Navy 10-Minute Global Elevation and Geographic Characteristics U.S. Navy Fleet Numerical Oceanography Center http://rda.ucar.edu/datasets/ds754.0/

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.