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article · Atmospheric chemistry and physics · 2017

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

Mathias Gergely, Steven J. Cooper, Timothy J. Garrett

Cited by 16Open access

Abstract

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.

Cite this paper

APA 7

Gergely, M., Cooper, S. J., & Garrett, T. J. (2017). Using snowflake surface-area-to-volume ratio to model and interpret snowfall triple-frequency radar signatures. Atmospheric chemistry and physics, 17(19), 12011–12030. https://doi.org/10.5194/acp-17-12011-2017

MLA 9

Gergely, Mathias, et al. “Using snowflake surface-area-to-volume ratio to model and interpret snowfall triple-frequency radar signatures.” Atmospheric chemistry and physics, vol. 17, no. 19, 2017, pp. 12011–30, https://doi.org/10.5194/acp-17-12011-2017.

Chicago

Gergely, Mathias, Steven J. Cooper, and Timothy J. Garrett. 2017. “Using snowflake surface-area-to-volume ratio to model and interpret snowfall triple-frequency radar signatures.” Atmospheric chemistry and physics 17 (19): 12011–30. https://doi.org/10.5194/acp-17-12011-2017.

Harvard

Gergely, M., Cooper, S.J. and Garrett, T.J. (2017) ‘Using snowflake surface-area-to-volume ratio to model and interpret snowfall triple-frequency radar signatures’, Atmospheric chemistry and physics, 17(19), pp. 12011–12030. Available at: https://doi.org/10.5194/acp-17-12011-2017.

BibTeX
@article{gergely2017using,
  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.},
  journal = {Atmospheric chemistry and physics},
  volume = {17},
  number = {19},
  pages = {12011--12030},
  year = {2017},
  doi = {10.5194/acp-17-12011-2017}
}

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