Related publication of the modelling presented in the Jupyter notebook
Seasonal Arctic sea ice forecasting with probabilistic deep learning
Environmental Data Science Book Community
Contains input Dataset for IceNet's demo notebook used in the Jupyter notebook of Sea ice forecasting using IceNet
Input Dataset for IceNet's demo notebook
Contains outputs, (table and figures), generated in the Jupyter notebook of Sea ice forecasting using IceNet
Contains input Forecasts, neural networks, and results from the paper: 'Seasonal Arctic sea ice forecasting with probabilistic deep learning' used in the Jupyter notebook of Sea ice forecasting using IceNet
Input Forecasts, neural networks, and results from the paper: 'Seasonal Arctic sea ice forecasting with probabilistic deep learning'
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Jupyter Notebook hosted by the Environmental Data Science Book
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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.
Learn about IceNet, a probabilistic Deep learning for seasonal sea-ice forecasts
Sea ice forecasting using IceNet (Jupyter Notebook) published in the Environmental Data Science book - fork
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.
Nordic e-Infrastructure Collaboration (NeIC)
Learn about IceNet, a probabilistic Deep learning for seasonal sea-ice forecasts.
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The research object refers to the Sea ice forecasting using IceNet notebook published in the Environmental Data Science book.
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.
physical geography and environmental geoscience
Derivative work created from forked Research Object. The Jupyter Notebook has been updated
Figure showing the 2 meter temperature from ECMWF ERA5 (monthly mean September to November 2019)
Image showing interactive plot of IceNet seasonal forecasts of Artic sea ice according to four lead times and months in 2020