An ensemble forecast model of iceberg drift

• An ensemble forecast model was developed to predict the drift of icebergs,
• A Monte Carlo approach was used to add random variability to key model parameters,
• The ensemble mean did not improve the skill of the forecast,
• The model is consistent and provides information on the forecast uncertainty.

A study of the application of ensemble techniques to iceberg forecasting has been performed using a numerical iceberg drift model. A simple ‘Monte Carlo’ approach was used in which variations for each key environmental parameter and iceberg property were sampled randomly to generate 250 ensemble members. The range of variations was estimated to represent the 95% confidence level in the parameter's value. A set of 216 iceberg tracks from the northern Grand Banks region, collected between 2002 and 2007, was used to assess the ensemble performance. While the ensemble mean drift forecast did not improve over the deterministic forecast, the ensemble model is shown to be consistent and the statistical properties of the ensemble provide useful information on the uncertainty inherent in the forecasts.