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Intro to data assimilation (DA) and the EnKF

An interactive (Jupyter notebook) tutorial. Jump right in (no installation!) by clicking the button of one of these cloud computing providers:

  • Open In Colab (requires Google login)
  • Binder (no login but can be slow to start)

Prerequisites: basics of calculus, matrices (e.g. inverses), random variables, Python (numpy).

ToC

Instructions for working locally

If you prefer, you can also run these notebooks on your own (Linux/Windows/Mac) computer. This is a bit snappier than running them online.

  1. Prerequisite: Python 3.9.
    If you're an expert, setup a python environment however you like. Otherwise: Install Anaconda, then open the Anaconda terminal and run the following commands:

    conda create --yes --name my-env python=3.9
    conda activate my-env
    python --version

    Ensure the printed version is 3.9.
    Keep using the same terminal for the commands below.

  2. Install:

    • Download and unzip (or git clone) this repository (see the green button up top)
    • Move the resulting folder wherever you like
    • cd into the folder
    • Install requirements:
      pip install -r path/to/requirements.txt
  3. Launch the Jupyter notebooks:

    • Launch the "notebook server" by executing:
      jupyter-notebook
      This will open up a page in your web browser that is a file navigator.
    • Enter the folder DA-tutorials/notebooks, and click on a tutorial (T1... .ipynb).

Developer notes

Please don't hesitate to submit issues or pull requests!

GitHub CI

Why scripts/ dir?

  • Easier to read git diffs
  • Enable importing from notebook (script mirrors)