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'
Conda environment when user want to have the same libraries installed without concerns of package versions
Lock conda file for linux-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book
Lock conda file for osx-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book
Jupyter Notebook hosted by the Environmental Data Science Book and exemplifying the use of IceNet, a probabilistic deep learning algorithm to compute seasonal sea-ice forecasts over the Arctic.
Rendered version for the original notebook. It has been used as input for this Research Object.
Online rendered version of the Jupyter notebook
Bibliographic Research Object created by Jean Iaquinta on sea-ice forecasting and navigability in the Arctic.
Bibliography on sea-ice forecasting and navigability in the Arctic
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.
Understanding Sea-Ice and the importance of accurate seasonal forecasts
Alejandro Coca-Castro, Anne Fouilloux, Jean Iaquinta, Tom Andersson, and Nick Barlow. "Understanding Sea-Ice and the importance of accurate seasonal forecasts." ROHub. Dec 02 ,2022. https://w3id.org/ro-id/df6591e6-c326-4d28-92fb-cb9d59786ac7.
Image showing interactive plot of IceNet seasonal forecasts of Artic sea ice according to four lead times and months in 2020
Seasonal sea ice forecast for September 2020 (leadtime=3)
This Jupyter notebook is the one used for developing while the GitHub repository may contain a slightly older version (but working version e.g. fully tested). This notebook is shared while working on it and may contain errors.
polar-modelling-icenet.ipynb (shared while on B2DROP)
Arts, culture and entertainment/Arts and entertainment/Literature
Modelling approach IceNet is a probabilistic, deep learning sea ice forecasting system.
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.
Economy, business and finance/Economic sector/Media/Book industry
Science and technology/Social sciences/Philosophy
The research object refers to the Sea ice forecasting using IceNet notebook published in the Environmental Data Science book.