Classification and Quantification of Snow Based on Spatial Variability of Radar Reflectivity

Sanghun LIM, Dmitri Moisseev, Chandrasekaran Venkatachalam, Dong-Ryul Lee

Research output: Contribution to journalArticleScientificpeer-review

Abstract

In this study, a classification methodology of snow particle types, i.e., crystals, aggregates, rimed snow, and graupel, by using spatial variability of the equivalent radar reflectivity factor is proposed. The methodology is formulated on the basis of the analysis of vertically pointing Doppler radar, scanning dual-polarization weather radar, and supporting surface observations. It is argued that by using the proposed snow-type identification methodology, it is possible to guide the choice of the particular parameters of power law relations of equivalent radar reflectivity factor liquid equivalent snowfall rate. The validity of the classification results are demonstrated by comparing the classification output to Vaisala WXT observations, which can be used to detect presence of high-density particles in snow. The performance of the proposed quantitative snowfall estimation algorithm is illustrated using an example of the data collected from the C-band operational Helsinki Vantaa radar and ground instruments (Vaisala PWD-11, Pluvio).
Original languageEnglish
JournalMeteorological Society of Japan. Journal
Volume91
Issue number6
Pages (from-to)763-774
Number of pages12
ISSN0026-1165
DOIs
Publication statusPublished - 2013
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 114 Physical sciences

Cite this

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title = "Classification and Quantification of Snow Based on Spatial Variability of Radar Reflectivity",
abstract = "In this study, a classification methodology of snow particle types, i.e., crystals, aggregates, rimed snow, and graupel, by using spatial variability of the equivalent radar reflectivity factor is proposed. The methodology is formulated on the basis of the analysis of vertically pointing Doppler radar, scanning dual-polarization weather radar, and supporting surface observations. It is argued that by using the proposed snow-type identification methodology, it is possible to guide the choice of the particular parameters of power law relations of equivalent radar reflectivity factor liquid equivalent snowfall rate. The validity of the classification results are demonstrated by comparing the classification output to Vaisala WXT observations, which can be used to detect presence of high-density particles in snow. The performance of the proposed quantitative snowfall estimation algorithm is illustrated using an example of the data collected from the C-band operational Helsinki Vantaa radar and ground instruments (Vaisala PWD-11, Pluvio).",
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author = "Sanghun LIM and Dmitri Moisseev and Chandrasekaran Venkatachalam and Dong-Ryul Lee",
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Classification and Quantification of Snow Based on Spatial Variability of Radar Reflectivity. / LIM, Sanghun ; Moisseev, Dmitri; Venkatachalam, Chandrasekaran; Lee, Dong-Ryul .

In: Meteorological Society of Japan. Journal, Vol. 91, No. 6, 2013, p. 763-774.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Classification and Quantification of Snow Based on Spatial Variability of Radar Reflectivity

AU - LIM, Sanghun

AU - Moisseev, Dmitri

AU - Venkatachalam, Chandrasekaran

AU - Lee, Dong-Ryul

PY - 2013

Y1 - 2013

N2 - In this study, a classification methodology of snow particle types, i.e., crystals, aggregates, rimed snow, and graupel, by using spatial variability of the equivalent radar reflectivity factor is proposed. The methodology is formulated on the basis of the analysis of vertically pointing Doppler radar, scanning dual-polarization weather radar, and supporting surface observations. It is argued that by using the proposed snow-type identification methodology, it is possible to guide the choice of the particular parameters of power law relations of equivalent radar reflectivity factor liquid equivalent snowfall rate. The validity of the classification results are demonstrated by comparing the classification output to Vaisala WXT observations, which can be used to detect presence of high-density particles in snow. The performance of the proposed quantitative snowfall estimation algorithm is illustrated using an example of the data collected from the C-band operational Helsinki Vantaa radar and ground instruments (Vaisala PWD-11, Pluvio).

AB - In this study, a classification methodology of snow particle types, i.e., crystals, aggregates, rimed snow, and graupel, by using spatial variability of the equivalent radar reflectivity factor is proposed. The methodology is formulated on the basis of the analysis of vertically pointing Doppler radar, scanning dual-polarization weather radar, and supporting surface observations. It is argued that by using the proposed snow-type identification methodology, it is possible to guide the choice of the particular parameters of power law relations of equivalent radar reflectivity factor liquid equivalent snowfall rate. The validity of the classification results are demonstrated by comparing the classification output to Vaisala WXT observations, which can be used to detect presence of high-density particles in snow. The performance of the proposed quantitative snowfall estimation algorithm is illustrated using an example of the data collected from the C-band operational Helsinki Vantaa radar and ground instruments (Vaisala PWD-11, Pluvio).

KW - 114 Physical sciences

U2 - 10.2151/jmsj.2013-603

DO - 10.2151/jmsj.2013-603

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JF - Meteorological Society of Japan. Journal

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