Short-Term (Seven-Day) Beaufort Sea-Ice Extent Forecasting with Deep Learning

Abstract

Ships inside the Arctic basin require high-resolution (one to five kilometers), near-term (days to semi-monthly) forecasts for guidance on scales of interest to their operations where forecast model predictions are insufficient due to their coarse spatial and temporal resolutions. Deep learning techniques offer the capability of rapid assimilation and analysis of multiple sources of information for improved forecasting. Data from the National Oceanographic and Atmospheric Administration’s Global Forecast System, Multi-scale Ultra-high Resolution Sea Surface Temperature, and the National Snow and Ice Data Center’s Multisensor Analyzed Sea-Ice Extent (MASIE) were used to develop the sea-ice extent deep learning forecast model, over the freeze-up periods of 2016, 2018, 2019, and 2020 in the Beaufort Sea. Sea-ice extent forecasts were produced for one to seven days in the future. The approach was novel for sea-ice extent forecasting in using forecast data as model input to aid in the prediction of sea-ice extent. Model accuracy was assessed against a persistence model. While the average accuracy of the persistence model dropped from 97% to 90% for forecast days one to seven, the deep learning model accuracy dropped only to 93%. A k (four)-fold cross-validation study found that on all except the first day, the deep learning model, which includes a U-Net architecture with a Resnet-18 backbone, does better than the persistence model. Skill scores improve the farther out in time to 0.27. The model demonstrated success in predicting changes in ice extent of significance for navigation in the Amundsen Gulf. Extensions to other Arctic seas, seasons, and sea-ice parameters are under development.