Nanopublications LDF server

Matches in Nanopublications for { <https://w3id.org/ro-id/b0a8864e-415d-42e3-972f-bb66c6d6a4d9/> ?p ?o ?g. }

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• b0a8864e-415d-42e3-972f-bb66c6d6a4d9 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." assertion.
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• b0a8864e-415d-42e3-972f-bb66c6d6a4d9 cite-as "Alejandro Coca-Castro, Anne Foilloux, Tom Andersson, Nick Barlow, and Jean Iaquinta. "Learn about IceNet, a probabilistic Deep learning for seasonal sea-ice forecasts." ROHub. Apr 03 ,2022. https://w3id.org/ro-id/b0a8864e-415d-42e3-972f-bb66c6d6a4d9." assertion.
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