Related publication of the modelling presented in the Jupyter notebook
Seasonal Arctic sea ice forecasting with probabilistic deep learning
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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Lock conda file for linux-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book
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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.
Sea ice forecasting using IceNet (Jupyter Notebook) forked from RO
Sea ice forecasting using IceNet (Jupyter Notebook) published in the Environmental Data Science book - fork
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.
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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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.
Modelling approach IceNet is a probabilistic, deep learning sea ice forecasting system.
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Sea ice forecasting using IceNet (Jupyter Notebook) forked from RO. The research object refers to the Sea ice forecasting using IceNet notebook published in the Environmental Data Science book.
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