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Title: Using snowflake surface-area-to-volume ratio to model and interpret snowfall triple-frequency radar signatures

Abstract

The snowflake microstructure determines the microwave scattering properties of individual snowflakes and has a strong impact on snowfall radar signatures. In this study, individual snowflakes are represented by collections of randomly distributed ice spheres where the size and number of the constituent ice spheres are specified by the snowflake mass and surface-area-to-volume ratio (SAV) and the bounding volume of each ice sphere collection is given by the snowflake maximum dimension. Radar backscatter cross sections for the ice sphere collections are calculated at X-, Ku-, Ka-, and W-band frequencies and then used to model triple-frequency radar signatures for exponential snowflake size distributions (SSDs). Additionally, snowflake complexity values obtained from high-resolution multi-view snowflake images are used as an indicator of snowflake SAV to derive snowfall triple-frequency radar signatures. The modeled snowfall triple-frequency radar signatures cover a wide range of triple-frequency signatures that were previously determined from radar reflectivity measurements and illustrate characteristic differences related to snow type, quantified through snowflake SAV, and snowflake size. The results show high sensitivity to snowflake SAV and SSD maximum size but are generally less affected by uncertainties in the parameterization of snowflake mass, indicating the importance of snowflake SAV for the interpretation of snowfall triple-frequency radar signatures.

Authors:
 [1];  [1];  [1]
  1. Univ. of Utah, Salt Lake City, UT (United States). Dept. of Atmospheric Sciences
Publication Date:
Research Org.:
Univ. of Utah, Salt Lake City, UT (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1502067
Grant/Contract Number:  
SC0016282
Resource Type:
Accepted Manuscript
Journal Name:
Atmospheric Chemistry and Physics (Online)
Additional Journal Information:
Journal Name: Atmospheric Chemistry and Physics (Online); Journal Volume: 17; Journal Issue: 19; Journal ID: ISSN 1680-7324
Publisher:
European Geosciences Union
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Gergely, Mathias, Cooper, Steven J., and Garrett, Timothy J. Using snowflake surface-area-to-volume ratio to model and interpret snowfall triple-frequency radar signatures. United States: N. p., 2017. Web. doi:10.5194/acp-17-12011-2017.
Gergely, Mathias, Cooper, Steven J., & Garrett, Timothy J. Using snowflake surface-area-to-volume ratio to model and interpret snowfall triple-frequency radar signatures. United States. doi:10.5194/acp-17-12011-2017.
Gergely, Mathias, Cooper, Steven J., and Garrett, Timothy J. Tue . "Using snowflake surface-area-to-volume ratio to model and interpret snowfall triple-frequency radar signatures". United States. doi:10.5194/acp-17-12011-2017. https://www.osti.gov/servlets/purl/1502067.
@article{osti_1502067,
title = {Using snowflake surface-area-to-volume ratio to model and interpret snowfall triple-frequency radar signatures},
author = {Gergely, Mathias and Cooper, Steven J. and Garrett, Timothy J.},
abstractNote = {The snowflake microstructure determines the microwave scattering properties of individual snowflakes and has a strong impact on snowfall radar signatures. In this study, individual snowflakes are represented by collections of randomly distributed ice spheres where the size and number of the constituent ice spheres are specified by the snowflake mass and surface-area-to-volume ratio (SAV) and the bounding volume of each ice sphere collection is given by the snowflake maximum dimension. Radar backscatter cross sections for the ice sphere collections are calculated at X-, Ku-, Ka-, and W-band frequencies and then used to model triple-frequency radar signatures for exponential snowflake size distributions (SSDs). Additionally, snowflake complexity values obtained from high-resolution multi-view snowflake images are used as an indicator of snowflake SAV to derive snowfall triple-frequency radar signatures. The modeled snowfall triple-frequency radar signatures cover a wide range of triple-frequency signatures that were previously determined from radar reflectivity measurements and illustrate characteristic differences related to snow type, quantified through snowflake SAV, and snowflake size. The results show high sensitivity to snowflake SAV and SSD maximum size but are generally less affected by uncertainties in the parameterization of snowflake mass, indicating the importance of snowflake SAV for the interpretation of snowfall triple-frequency radar signatures.},
doi = {10.5194/acp-17-12011-2017},
journal = {Atmospheric Chemistry and Physics (Online)},
number = 19,
volume = 17,
place = {United States},
year = {2017},
month = {10}
}

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