Ice particle type identification for shallow Arctic mixed-phase clouds using X-band polarimetric radar

Guang Wen, Mariko Oue, Alain Protat, Johannes Verlinde, Hui Xiao

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Ice particle type identification for shallow Arctic mixed-phase clouds is studied using X-band polarimetric radar variables: horizontal reflectivity factor Zh, differential reflectivity Zdr, specific differential phase Kdp, and cross-correlation coefficient ρhv The problem is formulated in a Bayesian classification framework, which consists of a probability density function (PDF) and a prior probability. The PDF is approximated using a Gaussian mixture model with parameters obtained by a clustering technique. The prior probability is constructed with the spatial contextual information based on a Markov random field. The PDF and prior probability are incorporated to produce the posterior probability, the maximum of which indicates the most likely particle type. The proposed algorithm is used to first derive the PDFs for the X-band polarimetric radar observations, and then identify the particle types within Arctic precipitating cloud cases sampled in Barrow, Alaska. The results are consistent with ground-based observations and the technique is capable of detecting and characterizing the variability of cloud microphysics in Arctic clouds.

Original languageEnglish (US)
Pages (from-to)114-131
Number of pages18
JournalAtmospheric Research
Volume182
DOIs
StatePublished - Dec 15 2016

Fingerprint

probability density function
radar
ice
reflectivity
cloud microphysics
particle

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

Cite this

@article{562089bf46054ebf9c3354b61c472084,
title = "Ice particle type identification for shallow Arctic mixed-phase clouds using X-band polarimetric radar",
abstract = "Ice particle type identification for shallow Arctic mixed-phase clouds is studied using X-band polarimetric radar variables: horizontal reflectivity factor Zh, differential reflectivity Zdr, specific differential phase Kdp, and cross-correlation coefficient ρhv The problem is formulated in a Bayesian classification framework, which consists of a probability density function (PDF) and a prior probability. The PDF is approximated using a Gaussian mixture model with parameters obtained by a clustering technique. The prior probability is constructed with the spatial contextual information based on a Markov random field. The PDF and prior probability are incorporated to produce the posterior probability, the maximum of which indicates the most likely particle type. The proposed algorithm is used to first derive the PDFs for the X-band polarimetric radar observations, and then identify the particle types within Arctic precipitating cloud cases sampled in Barrow, Alaska. The results are consistent with ground-based observations and the technique is capable of detecting and characterizing the variability of cloud microphysics in Arctic clouds.",
author = "Guang Wen and Mariko Oue and Alain Protat and Johannes Verlinde and Hui Xiao",
year = "2016",
month = "12",
day = "15",
doi = "10.1016/j.atmosres.2016.07.015",
language = "English (US)",
volume = "182",
pages = "114--131",
journal = "Atmospheric Research",
issn = "0169-8095",
publisher = "Elsevier BV",

}

Ice particle type identification for shallow Arctic mixed-phase clouds using X-band polarimetric radar. / Wen, Guang; Oue, Mariko; Protat, Alain; Verlinde, Johannes; Xiao, Hui.

In: Atmospheric Research, Vol. 182, 15.12.2016, p. 114-131.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Ice particle type identification for shallow Arctic mixed-phase clouds using X-band polarimetric radar

AU - Wen, Guang

AU - Oue, Mariko

AU - Protat, Alain

AU - Verlinde, Johannes

AU - Xiao, Hui

PY - 2016/12/15

Y1 - 2016/12/15

N2 - Ice particle type identification for shallow Arctic mixed-phase clouds is studied using X-band polarimetric radar variables: horizontal reflectivity factor Zh, differential reflectivity Zdr, specific differential phase Kdp, and cross-correlation coefficient ρhv The problem is formulated in a Bayesian classification framework, which consists of a probability density function (PDF) and a prior probability. The PDF is approximated using a Gaussian mixture model with parameters obtained by a clustering technique. The prior probability is constructed with the spatial contextual information based on a Markov random field. The PDF and prior probability are incorporated to produce the posterior probability, the maximum of which indicates the most likely particle type. The proposed algorithm is used to first derive the PDFs for the X-band polarimetric radar observations, and then identify the particle types within Arctic precipitating cloud cases sampled in Barrow, Alaska. The results are consistent with ground-based observations and the technique is capable of detecting and characterizing the variability of cloud microphysics in Arctic clouds.

AB - Ice particle type identification for shallow Arctic mixed-phase clouds is studied using X-band polarimetric radar variables: horizontal reflectivity factor Zh, differential reflectivity Zdr, specific differential phase Kdp, and cross-correlation coefficient ρhv The problem is formulated in a Bayesian classification framework, which consists of a probability density function (PDF) and a prior probability. The PDF is approximated using a Gaussian mixture model with parameters obtained by a clustering technique. The prior probability is constructed with the spatial contextual information based on a Markov random field. The PDF and prior probability are incorporated to produce the posterior probability, the maximum of which indicates the most likely particle type. The proposed algorithm is used to first derive the PDFs for the X-band polarimetric radar observations, and then identify the particle types within Arctic precipitating cloud cases sampled in Barrow, Alaska. The results are consistent with ground-based observations and the technique is capable of detecting and characterizing the variability of cloud microphysics in Arctic clouds.

UR - http://www.scopus.com/inward/record.url?scp=84979695352&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84979695352&partnerID=8YFLogxK

U2 - 10.1016/j.atmosres.2016.07.015

DO - 10.1016/j.atmosres.2016.07.015

M3 - Article

AN - SCOPUS:84979695352

VL - 182

SP - 114

EP - 131

JO - Atmospheric Research

JF - Atmospheric Research

SN - 0169-8095

ER -