Understanding Sea-Ice and the importance of accurate seasonal forecasts
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Applied sciences
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Seasonal Arctic sea ice forecasting with probabilistic deep learning
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Anne Fouilloux
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Input Forecasts, neural networks, and results from the paper: 'Seasonal Arctic sea ice forecasting with probabilistic deep learning'
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polar-modelling-icenet.ipynb
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description: Jupyter Notebook hosted by the Environmental Data Science Book and exemplifying the use of IceNet, a probabilistic deep learning algorithm to compute seasonal sea-ice forecasts over the Arctic.
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Bibliography on sea-ice forecasting and navigability in the Arctic
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Understanding Sea-Ice and the importance of accurate seasonal forecasts
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description: The research object refers to the Sea ice forecasting using IceNet notebook published in the Environmental Data Science book. Modelling approach IceNet is a probabilistic, deep learning sea ice forecasting system. The model, an ensemble of U-Net networks, learns how sea ice changes from climate simulations and observational data to forecast up to 6 months of monthly-averaged sea ice concentration maps at 25 km resolution. IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. IceNet was implemented in Python 3.7 using TensorFlow v2.2.0. Further details can be found in the Nature Communications paper Seasonal Arctic sea ice forecasting with probabilistic deep learning.
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