  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.
