Comparison of statistical iceberg forecast models
Journal article, Peer reviewed
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Original versionCold Regions Science and Technology. 2018, 155 69-89. 10.1016/j.coldregions.2018.07.003
Short-term iceberg drift prediction is challenging. Large uncertainties in the driving forces – current, wind and waves – usually prevent accurate forecasts. Recently several statistical iceberg forecast models have been proposed by the authors, which use iceberg position measurements to improve the short-term drift forecast. In this article these statistical forecast methods and models are briefly reviewed. An extensive comparison between the statistical models, in addition to a dynamic iceberg forecast model, is performed on several iceberg drift trajectories. Based on this comparison a new statistical forecast scheme is proposed that combines some of the advantages of the other methods.