Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting
This work addresses the need for fast and accurate sea ice forecasts to support marine operations in warming Arctic regions, representing an incremental improvement by adapting existing methods to real-time operational constraints.
The paper tackles the problem of providing reliable daily operational sea ice forecasts for marine safety in the Arctic by developing a U-Net-based deep learning model that predicts sea ice for up to 10 days, showing it outperforms simple baselines and improves with additional weather data and multi-region training.
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.