Articles | Volume 18, issue 2
https://doi.org/10.5194/tc-18-747-2024
https://doi.org/10.5194/tc-18-747-2024
Research article
 | 
20 Feb 2024
Research article |  | 20 Feb 2024

Bayesian physical–statistical retrieval of snow water equivalent and snow depth from X- and Ku-band synthetic aperture radar – demonstration using airborne SnowSAr in SnowEx'17

Siddharth Singh, Michael Durand, Edward Kim, and Ana P. Barros

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • AC1: 'Comment on egusphere-2023-1987', Siddharth Singh, 28 Sep 2023
  • RC1: 'Comment on egusphere-2023-1987', Anonymous Referee #1, 11 Oct 2023
    • AC2: 'Reply on RC1', Siddharth Singh, 02 Dec 2023
  • RC2: 'Comment on egusphere-2023-1987', Anonymous Referee #2, 17 Nov 2023
    • AC3: 'Reply on RC2', Siddharth Singh, 02 Dec 2023
    • AC4: 'Reply on RC2', Siddharth Singh, 02 Dec 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to minor revisions (review by editor) (14 Dec 2023) by Alexandre Langlois
AR by Siddharth Singh on behalf of the Authors (22 Dec 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (04 Jan 2024) by Alexandre Langlois
AR by Siddharth Singh on behalf of the Authors (06 Jan 2024)  Manuscript 
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Short summary
Seasonal snowfall accumulation plays a critical role in climate. The water stored in it is measured by the snow water equivalent (SWE), the amount of water released after completely melting. We demonstrate a Bayesian physical–statistical framework to estimate SWE from airborne X- and Ku-band synthetic aperture radar backscatter measurements constrained by physical snow hydrology and radar models. We explored spatial resolutions and vertical structures that agree well with ground observations.