Articles | Volume 16, issue 1
The Cryosphere, 16, 87–101, 2022
https://doi.org/10.5194/tc-16-87-2022
The Cryosphere, 16, 87–101, 2022
https://doi.org/10.5194/tc-16-87-2022

Research article 06 Jan 2022

Research article | 06 Jan 2022

Characterizing tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals

Julien Meloche et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2021-156', Matthew Sturm, 17 Jun 2021
    • AC1: 'Reply on RC1', Julien Meloche, 20 Aug 2021
  • RC2: 'Comment on tc-2021-156', Anonymous Referee #2, 10 Jul 2021
    • AC2: 'Reply on RC2', Julien Meloche, 21 Aug 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (07 Sep 2021) by Carrie Vuyovich
ED: Referee Nomination & Report Request started (27 Sep 2021) by Carrie Vuyovich
RR by Anonymous Referee #2 (28 Nov 2021)
AR by Anna Mirena Feist-Polner on behalf of the Authors (05 Nov 2021)  Author's response    Author's tracked changes
ED: Publish as is (29 Nov 2021) by Carrie Vuyovich
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Short summary
To estimate snow water equivalent from space, model predictions of the satellite measurement (brightness temperature in our case) have to be used. These models allow us to estimate snow properties from the brightness temperature by inverting the model. To improve SWE estimate, we proposed incorporating the variability of snow in these model as it has not been taken into account yet. A new parameter (coefficient of variation) is proposed because it improved simulation of brightness temperature.