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:

• (requires Google login)
• (no login but can be slow to start)

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

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 3.12.
   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=3.12
   conda activate my-env
   python --version
   

   Ensure the printed version is 3.12.
   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!

Why nb_mirrors/ dir?

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

Target audience

Students in their 3rd year or above in university.

Prerequisites: some experience with of numpy (Python) as well as 1st-year, university-level statistics and linear algebra.

Citing

Please cite as

@phdthesis{raanes2016thesis,
    author = {Raanes, Patrick N.},
    title = {Improvements to Ensemble Methods for Data Assimilation in the Geosciences},
    school = {University of Oxford},
    year = {2016},
    month = {January}
}