Articles | Volume 16, issue 9
The Cryosphere, 16, 3801–3814, 2022
https://doi.org/10.5194/tc-16-3801-2022
The Cryosphere, 16, 3801–3814, 2022
https://doi.org/10.5194/tc-16-3801-2022
Research article
22 Sep 2022
Research article | 22 Sep 2022

Sensitivity of modeled snow grain size retrievals to solar geometry, snow particle asphericity, and snowpack impurities

Zachary Fair 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 egusphere-2022-266', Anonymous Referee #1, 18 May 2022
    • AC1: 'Reply on RC1', Zachary Fair, 11 Aug 2022
  • RC2: 'Comment on egusphere-2022-266', Anonymous Referee #2, 15 Jul 2022
    • AC2: 'Reply on RC2', Zachary Fair, 11 Aug 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (review by editor) (12 Aug 2022) by Florent Dominé
AR by Zachary Fair on behalf of the Authors (18 Aug 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to technical corrections (30 Aug 2022) by Florent Dominé
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Short summary
Snow grain size is important to determine the age and structure of snow, but it is difficult to measure. Snow grain size can be found from airborne and spaceborne observations by measuring near-infrared energy reflected from snow. In this study, we use the SNICAR radiative transfer model and a Monte Carlo model to examine how snow grain size measurements change with snow structure and solar zenith angle. We show that improved understanding of these variables improves snow grain size precision.