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Atmosphere
Environmental Data Science Book

Deep learning and variational inversion to quantify and attribute climate change (CIRC23)</h1>

Authors
Affiliations
University of Göttingen
University of Cambridge
University of Cambridge
https://doi.org/10.5281/zenodo.8301002

Continuous integration badge Binder doi notebook review

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How to run

Running locally

You may also download the notebook from GitHub to run it locally:

  1. Open your terminal

  2. Check your conda install with conda --version. If you don’t have conda, install it by following these instructions (see here)

  3. Clone the repository

    git clone https://github.com/eds-book-gallery/93463cac-471a-469d-ad52-0514fd9b67f2.git

  4. Move into the cloned repository

    cd 93463cac-471a-469d-ad52-0514fd9b67f2

  5. Create and activate your environment from the .binder/environment.yml file

    conda env create -f .binder/environment.yml
conda activate 93463cac-471a-469d-ad52-0514fd9b67f2

  6. Launch the jupyter interface of your preference, notebook, jupyter notebook or lab jupyter lab

References
  1. Domazetoski, V., Zúñiga-González, A., Allemang, O., & This EDS book notebook contributors. (2024). Deep learning and variational inversion to quantify and attribute climate change (Jupyter Notebook) published in the Environmental Data Science book. Zenodo. 10.5281/ZENODO.8301002
Deep learning and variational inversion for climate science (CIRC23)
Deep learning and variational inversion to quantify and attribute climate change (CIRC23)