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Oceans
Environmental Data Science Book

Variational data assimilation with deep prior (CIRC23)</h1>

Authors
Affiliations
University of Mumbai
University of Mumbai
https://doi.org/10.5281/zenodo.8339298

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/39d9c177-11da-41b2-9b64-63f4c1c834b3.git

  4. Move into the cloned repository

    cd 39d9c177-11da-41b2-9b64-63f4c1c834b3

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

    conda env create -f .binder/environment.yml
conda activate 39d9c177-11da-41b2-9b64-63f4c1c834b3

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

References
  1. Pahari, M., & Bhoir, R. (2025). Variational data assimilation with deep prior (Jupyter Notebook) published in the Environmental Data Science book. Zenodo. 10.5281/ZENODO.8339298
Variational assimilation with deep prior (CIRC23)
Variational data assimilation with deep prior (CIRC23)