Articles | Volume 17, issue 19
https://doi.org/10.5194/acp-17-12011-2017
https://doi.org/10.5194/acp-17-12011-2017
Research article
 | 
10 Oct 2017
Research article |  | 10 Oct 2017

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

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

Viewed

Total article views: 3,299 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,925 1,212 162 3,299 324 169 174
  • HTML: 1,925
  • PDF: 1,212
  • XML: 162
  • Total: 3,299
  • Supplement: 324
  • BibTeX: 169
  • EndNote: 174
Views and downloads (calculated since 16 May 2017)
Cumulative views and downloads (calculated since 16 May 2017)

Viewed (geographical distribution)

Total article views: 3,299 (including HTML, PDF, and XML) Thereof 3,280 with geography defined and 19 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 17 Jun 2026
Download
Short summary
This study investigates the importance of snowflake surface-area-to-volume ratio (SAV) for the interpretation of snowfall triple-frequency radar signatures. The results indicate that snowflake SAV has a strong impact on modeled snowfall radar signatures and therefore may be used to further constrain (the large variety and high natural variability of) snowflake shape for snowfall remote sensing, e.g., to distinguish graupel snow from snowfall characterized by large aggregate snowflakes.
Share
Altmetrics
Final-revised paper
Preprint