Articles | Volume 17, issue 19
Atmos. Chem. Phys., 17, 12011–12030, 2017
https://doi.org/10.5194/acp-17-12011-2017
Atmos. Chem. Phys., 17, 12011–12030, 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 et al.

Viewed

Total article views: 1,330 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
759 509 62 1,330 145 32 53
  • HTML: 759
  • PDF: 509
  • XML: 62
  • Total: 1,330
  • Supplement: 145
  • BibTeX: 32
  • EndNote: 53
Views and downloads (calculated since 16 May 2017)
Cumulative views and downloads (calculated since 16 May 2017)

Viewed (geographical distribution)

Total article views: 1,323 (including HTML, PDF, and XML) Thereof 1,318 with geography defined and 5 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 19 Oct 2021
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.
Altmetrics
Final-revised paper
Preprint