PyEarthTools: Machine learning for Earth system science
Part of the Scientific Python specialist track
PyEarthTools is an open-source, Python software framework that supports the development of machine learning models, big and small, for Earth system science. See https://pyearthtools.readthedocs.io/ .
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I want a ticket!Models based on physical processes and equations of motion have been built, over decades, to model the physical structure and composition of the ocean, atmosphere, and land surface. Other scientific models have been built to process very complex, high-dimensional data from instruments such as satellites and radar into simpler data products such as images and surface field estimates. These models are often computationally intensive.
Prior to around 2022, the complexity of many of these tasks was seen as infeasible for machine learning systems. In the years since, deep neural networks have achieved state-of-the-art performance across the types of models described above.
PyEarthTools contains modules for:
- Loading and indexing Earth system data into Xarray;
- Processing and normalising Earth system data for machine learning;
- Defining machine learning (ML) models;
- Training ML models and managing experiments; and
- Evaluating ML models.
Come to this talk to learn how to:
- Get started with machine learning for Earth system science
- Learn about the PyEarthTools framework and how to build re-usable ML pipelines
- Catch the science bug and try out some simple projects
- Train your own weather models from scratch
This talk is suitable for beginners through to professional scientists and data scientists.
PyEarthTools was initially developed by the Bureau of Meteorology (Australia), and now also has developers from the National Institute of Water and Atmospheric Research (New Zealand), and the Met Office (United Kingdom).
Tennessee Leeuwenburg is a data scientist and software developer, at the Bureau of Meteorology, with over 20 years of experience. He has an interest in open source software, machine learning, and forecast verification. His current research work includes the development of scientific machine learning models for weather and environmental prediction. For an overview of his recent publications, please visit https://orcid.org/0009-0008-2024-1967 . He also maintains two open source software packages (https://github.com/nci/scores and https://github.com/ACCESS-Community-Hub/PyEarthTools).