An iceberg forecast approach based on a statistical ocean current model
Journal article, Peer reviewed
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Original versionCold Regions Science and Technology. 2019, 158 128-142. 10.1016/j.coldregions.2018.11.016
This article proposes a statistical model for short-term iceberg drift forecasts by transforming the problem of forecasting the iceberg velocity into a problem of forecasting the ocean current velocity. A Vector-autoregression model is identified using historical ocean current data as a training set. The proposed forecast scheme is tested and analysed on four real iceberg drift trajectories. Based on these results, recommendations about the forecast horizon, the filter horizon and model order are given. Moreover, it is shown that the statistical forecast approach presented in this article offers superior performance to a conventional dynamic iceberg forecast model for short-term drift forecasts.