Global warming made the Arctic available for marine operations and created demand for reliable operational sea ice forecasts to make them safe. While ocean-ice numerical models are highly computationally intensive, relatively lightweight ML-based methods may be more efficient in this task. Many works have exploited different deep learning models alongside classical approaches for predicting sea ice concentration in the Arctic. However, only a few focus on daily operational forecasts and consider the real-time availability of data they need for operation. In this work, we aim to close this gap and investigate the performance of the U-Net model trained in two regimes for predicting sea ice for up to the next 10 days. We show that this deep learning model can outperform simple baselines by a significant margin and improve its quality by using additional weather data and training on multiple regions, ensuring its generalization abilities. As a practical outcome, we build a fast and flexible tool that produces operational sea ice forecasts in the Barents Sea, the Labrador Sea, and the Laptev Sea regions.

翻译：虽然海洋-冰数值模型在计算上高度密集,但相对轻轻的ML法方法可能在这方面更为有效。许多工作都利用了不同的深层次学习模型以及预测北极海冰浓度的典型方法。然而,只有少数工作侧重于日常作业预测,并审议了它们运作所需的实时数据。在这项工作中,我们的目标是缩小这一差距,并调查在预测海冰的两个制度下培训的U-Net模型的性能,以预测未来10天。我们表明,这一深层次学习模型通过使用更多的气象数据和在多个区域进行培训,可以大大超过简单的基线,提高质量,同时确保其普遍性能力。我们作为实际成果,在巴伦支海、拉布拉多海和拉普特夫海区域建立了一个快速灵活的工具,以产生实用的海洋冰预报。