Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting

• Published in: Remote Sensing
• Main Authors: Timofey Grigoryev, Polina Verezemskaya, Mikhail Krinitskiy, Nikita Anikin, Alexander Gavrikov, Ilya Trofimov, Nikita Balabin, Aleksei Shpilman, Andrei Eremchenko, Sergey Gulev, Evgeny Burnaev, Vladimir Vanovskiy
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2022
• Subjects: data-driven models; short-term sea ice forecasting; deep learning; computer vision; U-Net; remote sensing; Science; Q; Arctic; Barents Sea; Laptev Sea; Global warming; Labrador Sea; laptev; Sea ice
• Online Access: https://doi.org/10.3390/rs14225837; https://doaj.org/article/ff8d86b5fefd489d978a618f0e75a73b

Global warming has made the Arctic increasingly available for marine operations and created a demand for reliable operational sea ice forecasts to increase safety. Because ocean-ice numerical models are highly computationally intensive, relatively lightweight ML-based methods may be more efficient for sea ice forecasting. Many studies 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 needed for marine operations. In this article, 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 we can improve the model’s quality by using additional weather data and training on multiple regions to ensure 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.