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The research object refers to the Deep learning and variational inversion to quantify and attribute climate change (CIRC23) notebook published in the Environmental Data Science book.
Deep learning and variational inversion to quantify and attribute climate change (CIRC23) (Jupyter Notebook) published in the Environmental Data Science book
Viktor Domazetoski, Andrés Zúñiga-González, Owen Allemang, Meghna Asthana, Nick Homer, and Devanjan Bhattacharya. "Deep learning and variational inversion to quantify and attribute climate change (CIRC23) (Jupyter Notebook) published in the Environmental Data Science book." ROHub. Aug 30 ,2023. https://w3id.org/ro-id/91046403-e0b7-41d3-8d60-4b540219ffa7.
Image showing mean and standard deviation of the simulations of 12 climate models
Contains input Input dataset for paper used in the Jupyter notebook of Deep learning and variational inversion to quantify and attribute climate change (CIRC23)
Contains outputs, (figures, models and results), generated in the Jupyter notebook of Deep learning and variational inversion to quantify and attribute climate change (CIRC23)
Rendered version of the Jupyter Notebook hosted by the Environmental Data Science Book
Online rendered version of the Jupyter notebook
Conda environment when user want to have the same libraries installed without concerns of package versions
Jupyter Notebook hosted by the Environmental Data Science Book
Lock conda file of the Jupyter notebook hosted by the Environmental Data Science Book
Related publication of the modelling presented in the Jupyter notebook
Detection and attribution of climate change: A deep learning and variational approach
Deep learning and variational inversion to quantify and attribute climate change (CIRC23) (Jupyter Notebook) published in the Environmental Data Science book.
Environmental Data Science Book Community