Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 16, issue 4
Articles | Volume 16, issue 4
https://doi.org/10.5194/amt-16-911-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-911-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 4
Research article
 | 
21 Feb 2023
Research article |  | 21 Feb 2023

Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach

Anne-Claire Billault-Roux, Gionata Ghiggi, Louis Jaffeux, Audrey Martini, Nicolas Viltard, and Alexis Berne

Viewed

Total article views: 4,549 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,769 707 73 4,549 108 115
  • HTML: 3,769
  • PDF: 707
  • XML: 73
  • Total: 4,549
  • BibTeX: 108
  • EndNote: 115
Views and downloads (calculated since 21 Jul 2022)
Cumulative views and downloads (calculated since 21 Jul 2022)

Viewed (geographical distribution)

Total article views: 4,549 (including HTML, PDF, and XML) Thereof 4,530 with geography defined and 19 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 25 Nov 2025
Download

The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.

Short summary
Better understanding and modeling snowfall properties and processes is relevant to many fields, ranging from weather forecasting to aircraft safety. Meteorological radars can be used to gain insights into the microphysics of snowfall. In this work, we propose a new method to retrieve snowfall properties from measurements of radars with different frequencies. It relies on an original deep-learning framework, which incorporates knowledge of the underlying physics, i.e., electromagnetic scattering.
Read more
Share