Articles | Volume 15, issue 8
https://doi.org/10.5194/tc-15-3989-2021
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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Latest update: 21 May 2024
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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.