Preprints
https://doi.org/10.5194/egusphere-2023-3107
https://doi.org/10.5194/egusphere-2023-3107
16 Feb 2024
 | 16 Feb 2024
Status: this preprint is open for discussion.

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

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

Abstract. 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.

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

Status: open (until 13 Apr 2024)

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

Model code and software

Project repository Are Frode Kvanum https://github.com/AreFrode/Developing_ice_chart_deep_learning_predictions

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

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Short summary
Recent studies have shown that machine learning models are effective at predicting sea ice concentration, yet few have explored the development of such models in an operational context. In this study, we present the development of a machine learning forecasting system which can predict sea ice concentration at 1 km resolution, up to 3 days ahead using real time operational data. The developed forecasts predict the sea ice edge position with a better accuracy than physical and baseline forecasts.