Developing a deep learning forecasting system for short-term and high-resolution prediction of sea ice concentration

Kvanum, Are Frode; Palerme, Cyril; Müller, Malte; Rabault, Jean; Hughes, Nick

There has been a steady increase of marine activity throughout the Arctic Ocean during the last decades, and maritime end users are requesting skillful high-resolution sea ice forecasts to ensure operational safety. Different studies have demonstrated the effectiveness of utilizing computationally lightweight deep learning models to predict sea ice properties in the Arctic. In this study, we utilize operational atmospheric forecasts as well as ice charts and sea ice concentration passive microwave observations as predictors to train a deep learning model with ice charts as the ground truth. The developed deep learning forecasting system can predict regional sea ice concentration at one kilometer resolution for 1 to 3-day lead time. We validate the deep learning system performance by evaluating the position of forecasted sea ice concentration contours at different concentration thresholds. It is shown that the deep learning forecasting system achieves a lower error for several sea ice concentration contours when compared against baseline-forecasts (persistence-forecasts and a linear trend), as well as two state-of-the-art dynamical sea ice forecasting systems (neXtSIM and Barents-2.5) for all considered lead times and seasons.

Zitieren

Zitierform:

Kvanum, Are Frode / Palerme, Cyril / Müller, Malte / et al: Developing a deep learning forecasting system for short-term and high-resolution prediction of sea ice concentration. 2024. Copernicus Publications.

Zugriffsstatistik

Gesamt:
Volltextzugriffe:
Metadatenansicht:
12 Monate:
Volltextzugriffe:
Metadatenansicht:

Grafik öffnen

Rechte

Rechteinhaber: Are Frode Kvanum et al.

Nutzung und Vervielfältigung:

Export