Articles | Volume 15, issue 8
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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Cited articles

Ahijevych, D., Pinto, J. O., Williams, J. K., and Steiner, M.: Probabilistic Forecasts of Mesoscale Convective System Initiation Using the Random Forest Data Mining Technique, Weather Forecast., 31, 581–599,, 2016. a
Berkman, P. A., Fiske, G., Røyset, J.-A., Brigham, L. W., and Lorenzini, D.: Next-Generation Arctic Marine Shipping Assessments, Springer International Publishing, 241–268,, 2020. a
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Cavalieri, D. J. and Parkinson, C. L.: Arctic sea ice variability and trends, 1979–2010, The Cryosphere, 6, 881–889,, 2012. a
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.