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Open AccessArticle

The Use of Gaussian Mixture Models with Atmospheric Lagrangian Particle Dispersion Models for Density Estimation and Feature Identification

NOAA Air Resources Laboratory, College Park, MD 20740, USA
Current address: 5830 University Research Court, College Park, MD 20740, USA.
Atmosphere 2020, 11(12), 1369; https://doi.org/10.3390/atmos11121369
Received: 16 October 2020 / Revised: 5 December 2020 / Accepted: 12 December 2020 / Published: 17 December 2020
(This article belongs to the Special Issue Forecasting the Transport of Volcanic Ash in the Atmosphere)
Atmospheric Lagrangian particle dispersion models, LPDM, simulate the dispersion of passive tracers in the atmosphere. At the most basic level, model output consists of the position of computational particles and the amount of mass they represent. In order to obtain concentration values, this information is then converted to a mass distribution via density estimation. To date, density estimation is performed with a nonparametric method so that output consists of gridded concentration data. Here we introduce the use of Gaussian mixture models, GMM, for density estimation. We compare to the histogram or bin counting method for a tracer experiment and simulation of a large volcanic ash cloud. We also demonstrate the use of the mixture model for automatic identification of features in a complex plume such as is produced by a large volcanic eruption. We conclude that use of a mixture model for density estimation and feature identification has potential to be very useful. View Full-Text
Keywords: HYSPLIT; LPDM; modeling; atmospheric; dispersion; volcanic; ash; tracer; Gaussian mixture model HYSPLIT; LPDM; modeling; atmospheric; dispersion; volcanic; ash; tracer; Gaussian mixture model
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  • Externally hosted supplementary file 1
    Link: https://github.com/noaa-oar-arl/hysplit_gmm
    Description: The python code developed for this project is available from github. The CONTROL and SETUP.CFG files needed to run the simulations are also included in the repository. We expect that Jupyter notebooks provided with the repository will be helpful for anyone wishing to reproduce or extend the work.
MDPI and ACS Style

Crawford, A. The Use of Gaussian Mixture Models with Atmospheric Lagrangian Particle Dispersion Models for Density Estimation and Feature Identification. Atmosphere 2020, 11, 1369. https://doi.org/10.3390/atmos11121369

AMA Style

Crawford A. The Use of Gaussian Mixture Models with Atmospheric Lagrangian Particle Dispersion Models for Density Estimation and Feature Identification. Atmosphere. 2020; 11(12):1369. https://doi.org/10.3390/atmos11121369

Chicago/Turabian Style

Crawford, Alice. 2020. "The Use of Gaussian Mixture Models with Atmospheric Lagrangian Particle Dispersion Models for Density Estimation and Feature Identification" Atmosphere 11, no. 12: 1369. https://doi.org/10.3390/atmos11121369

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