Articles | Volume 15, issue 8
The Cryosphere, 15, 3989–4004, 2021
https://doi.org/10.5194/tc-15-3989-2021
The Cryosphere, 15, 3989–4004, 2021
https://doi.org/10.5194/tc-15-3989-2021

Research article 23 Aug 2021

Research article | 23 Aug 2021

Calibration of sea ice drift forecasts using random forest algorithms

Cyril Palerme and Malte Müller

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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-2021-24', Anonymous Referee #1, 09 Mar 2021
  • RC2: 'Comment on tc-2021-24', Anonymous Referee #2, 10 Mar 2021
  • RC3: 'Comment on tc-2021-24', Bruno Tremblay, 07 Apr 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (review by editor) (04 Jul 2021) by Michel Tsamados
AR by Cyril Palerme on behalf of the Authors (07 Jul 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (26 Jul 2021) by Michel Tsamados
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Short summary
Methods have been developed for calibrating sea ice drift forecasts from an operational prediction system using machine learning algorithms. These algorithms use predictors from sea ice concentration observations during the initialization of the forecasts, sea ice and wind forecasts, and some geographical information. Depending on the calibration method, the mean absolute error is reduced between 3.3 % and 8.0 % for the direction and between 2.5 % and 7.1 % for the speed of sea ice drift.