CryoSwath is a python package containing processing pipelines, a tool library, and some pre-assembled data to retrieve and study CryoSat-2 data.

Adaptability lies at its core. The user can access many options simply by passing arguments to functions; everything else can be customized changing the concerned function or adding a new one.

CryoSwath is being developed. Branch main is the release branch, scripts contains tutorials, and data contains auxiliary data and the required directory structure. You can have everything setup automatically (see "getting started"). Other branches are for development.

• find all CryoSat-2 tracks passing over your region of interest
• download L1b data from ESA
• retrieve swath elevation estimates
• aggregate point data to gridded data
• fill data gaps using tested methods
• calculate change rates

There is a number of ways you can start off, including installing from "source", pypi, conda-forge, or docker. Please find more details in the docs (prerequisites). I show two approaches, installing from conda-forge and a mixture of methods.

advantage: simple and most stable dependency resolution

First, choose an environment name and either define $env_name, e.g., env_name=cryoswath, or adapt the create and activate commands accordingly.

mamba create -n $env_name conda-forge::cryoswath

Continue below at "init project".

advantage: allows modifications and easy updates

Like the above, first, choose an environment name and either define $env_name, e.g., env_name=cryoswath, or adapt the create and activate commands accordingly. You will also need the path to your environment. That will be something ending in .../envs/env_name. If you are not sure, find it viewing mamba env list. Further, I assume you'll clone into a directory named cryoswath.

git clone https://github.com/j-haacker/cryoswath.git cryoswath
mamba env create -n $env_name -f cryoswath/environment.yml
mamba activate $env_name
mamba install pip
pip install --editable cryoswath

CryoSwath will deal with data that is not meant to reside in the installation directory. The command cryoswath-init will set up a directory structure and download some auxiliary files. Please choose a project name of you liking and replace proj_dir in the following.

mkdir proj_dir
cd proj_dir
cryoswath-init

This, among others, creates a file scripts/config.ini that contains the base path of your project. This allows CryoSwath to find the data - if you wish to run scripts from different directories, copy this file there.

advantage: will almost always work

note: the first time running the docker image requires to download ~ 1 Gb

1. docker run -it -p 8888:8888 -v <proj_dir>:/home/jovyan/project_dir cryoswath/jupyterlab:v0.2.3
2. You will receive an address including a token with which you can connect to the jupyterlab using your browser
3. Start doing your things or get started with the installed tutorial notebooks!

cryoswath.readthedocs.io

• requirements.txt
• reference elevation model
• glacier outlines

Always install xarray and Zarr at the same time. Otherwise xarray might not support the install Zarr version.

cryoswath will point you to the required resources.

• ESA's data server is not available from all internet service providers

• projected RGI basins sometimes "invalid" -> add .make_valid() if it is missing somewhere

• it has mostly been tested for the Arctic

  Further: see open issues.

You can cite this package using bibtex:

@software{cryoswath,
  author       = {Haacker, Jan},
  title        = {CryoSwath: v0.2.3},
  month        = jul,
  year         = 2025,
  publisher    = {Zenodo},
  version      = {v0.2.3},
  doi          = {10.5281/zenodo.15809596}
}

Please mind that you likely used other resources on the way.

• ESA provides the L1b data under these Terms and Conditions
• RGI data is distributed under CC-BY-4.0 license
• if you (likely) used DEMs of the PGC, see their Acknowledgement Policy
• the many python packages and libraries this package depends on; some of which are indispensable.

MIT. See LICENSE.txt.