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

Crawford, Alice

doi:10.3390/atmos11121369

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

2020
By Crawford, Alice
Source: Atmosphere, 11(12)

[PDF-4.21 MB]

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Details:

Journal Title:

Atmosphere
Personal Author:

Crawford, Alice
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; ARL (Air Resources Laboratory)

OAR (Oceanic and Atmospheric Research) ; ARL (Air Resources Laboratory) Less -
Description:

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
Keywords:

[+]

Volcanic ash, tuff, etc.
Source:

Atmosphere, 11(12)
DOI:

https://doi.org/10.3390/atmos11121369
Document Type:

Journal Article
Rights Information:

CC BY
Compliance:

Submitted
Main Document Checksum:

[+]

urn:sha256:520c64d3f326d968166e4fe9b33312df24ae0087a5696ddc773e9ea377387ebb
Download URL:

https://repository.library.noaa.gov/view/noaa/27697/noaa_27697_DS1.pdf
File Type:

	Crawford_Atmosphere_2020.xml	xml
	Crawford_Atmosphere_2020.xml	xml

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