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Seasonal Arctic sea ice forecasting with probabilistic deep learning
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Input Dataset for IceNet's demo notebook
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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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description: 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
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Sea ice forecasting using IceNet (Jupyter Notebook) forked from RO
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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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cite-as: Alejandro Coca-Castro, Tom Andersson, and Nick Barlow. "Sea ice forecasting using IceNet (Jupyter Notebook) forked from RO." ROHub. Apr 03 ,2022. https://w3id.org/ro-id/911b0247-5b28-4993-894e-aff28828e643.

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Image showing interactive plot of IceNet seasonal forecasts of Artic sea ice according to four lead times and months in 2020
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Sea ice forecasting using IceNet (Jupyter Notebook) forked from RO
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