Articles | Volume 18, issue 11
https://doi.org/10.5194/tc-18-5139-2024
https://doi.org/10.5194/tc-18-5139-2024
Research article
 | 
14 Nov 2024
Research article |  | 14 Nov 2024

Characterization of non-Gaussianity in the snow distributions of various landscapes

Noriaki Ohara, Andrew D. Parsekian, Benjamin M. Jones, Rodrigo C. Rangel, Kenneth M. Hinkel, and Rui A. P. Perdigão

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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-2024-395', Anonymous Referee #1, 12 Jun 2024
    • AC1: 'Reply on RC1', Noriaki Ohara, 19 Aug 2024
  • RC2: 'Comment on egusphere-2024-395', Anonymous Referee #2, 22 Jul 2024
    • AC2: 'Reply on RC2', Noriaki Ohara, 19 Aug 2024
    • AC3: 'Reply on RC2', Noriaki Ohara, 19 Aug 2024

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) (28 Aug 2024) by Xavier Fettweis
AR by Noriaki Ohara on behalf of the Authors (05 Sep 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (20 Sep 2024) by Xavier Fettweis
AR by Noriaki Ohara on behalf of the Authors (26 Sep 2024)  Manuscript 
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Short summary
Snow distribution characterization is essential for accurate snow water estimation for water resource prediction from existing in situ observations and remote-sensing data at a finite spatial resolution. Four different observed snow distribution datasets were analyzed for Gaussianity. We found that non-Gaussianity of snow distribution is a signature of the wind redistribution effect. Generally, seasonal snowpack can be approximated well by a Gaussian distribution for a fully snow-covered area.