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https://ror.org/01xtthb56 https://w3id.org/ro-id/b0a8864e-415d-42e3-972f-bb66c6d6a4d9/ http://schema.org/copyrightHolder mailto:environmental.ds.book@gmail.com https://w3id.org/ro-id/b0a8864e-415d-42e3-972f-bb66c6d6a4d9/ http://schema.org/creator mailto:environmental.ds.book@gmail.com https://w3id.org/ro-id/b0a8864e-415d-42e3-972f-bb66c6d6a4d9/ http://schema.org/dateCreated 2022-04-03 22:37:45.977506+00:00 https://w3id.org/ro-id/b0a8864e-415d-42e3-972f-bb66c6d6a4d9/ http://schema.org/dateModified 2025-10-18 10:49:31.245781+00:00 https://w3id.org/ro-id/b0a8864e-415d-42e3-972f-bb66c6d6a4d9/ http://schema.org/datePublished 2022-04-03 22:37:45.977506+00:00 https://w3id.org/ro-id/b0a8864e-415d-42e3-972f-bb66c6d6a4d9/ http://schema.org/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. 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