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

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

doi:https://doi.org/10.5194/amt-16-911-2023

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

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Final revised paper (published on 21 Feb 2023)
Preprint (discussion started on 21 Jul 2022)

Interactive discussion

Status: closed

RC1:
'Comment on amt-2022-199', Stefan Kneifel, 26 Aug 2022

The comment was uploaded in the form of a supplement: https://amt.copernicus.org/preprints/amt-2022-199/amt-2022-199-RC1-supplement.pdf

Citation: https://doi.org/10.5194/amt-2022-199-RC1
- AC1: 'Reply on RC1', Anne-Claire Billault–Roux, 20 Dec 2022
  
  We thank Stefan Kneifel for his feedback on the manuscript. Our detailed response to his comments is attached in supplements, together with the response to the comments of Reviewer #2.
  
  Citation: https://doi.org/10.5194/amt-2022-199-AC1
RC2:
'Comment on amt-2022-199', Anonymous Referee #2, 06 Nov 2022

The comment was uploaded in the form of a supplement: https://amt.copernicus.org/preprints/amt-2022-199/amt-2022-199-RC2-supplement.pdf

Citation: https://doi.org/10.5194/amt-2022-199-RC2
- AC2: 'Reply on RC2', Anne-Claire Billault–Roux, 20 Dec 2022
  
  We thank Reviewer #2 for their feedback on the manuscript. Our detailed response to their comments is attached in supplements, together with the response to the comments of Reviewer #1 (Stefan Kneifel).
  
  Citation: https://doi.org/10.5194/amt-2022-199-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Anne-Claire Billault–Roux on behalf of the Authors (20 Dec 2022) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (22 Dec 2022) by Markus Rapp

RR by Anonymous Referee #2 (10 Jan 2023)

RR by Stefan Kneifel (31 Jan 2023)

ED: Publish subject to technical corrections (01 Feb 2023) by Markus Rapp

AR by Anne-Claire Billault–Roux on behalf of the Authors (02 Feb 2023) Author's response Manuscript

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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.