2026-08-30 –, 700
Accessing and visualising climate data can be challenging due to high volume of available datasets and the significant memory requirements that come with processing them. This workshop guides participants accessing CMIP6 climate model data, processing it with Python, computing different climatic indicators and develop interactive web maps using MapLibre GL.
This hands-on workshop would walk participants through a complete open-source Python workflow for exploring and communicating future climate projections using CMIP6 data. The first half of the workshop will cover assessing CMIP6 data directly from Google Cloud’s public storage using the Pangeo intake catalog. Participants will learn to work with NetCDF and Zarr formats using xarray, subset data to a region of interest, and compute climate indicators including days above heat thresholds and rainfall anomalies.
In the second half of the workshop, the focus will be on visualisation, i.e. creating publication-quality static maps with Cartopy and Matplotlib, converting processed data to GeoJSON, and building a simple interactive climate explorer web map using MapLibre GL. All exercises will run in Jupyter notebooks. Basic Python familiarity is assumed, but no climate science background is required for this workshop, only a passion to learn how the rainfall patterns would look like in the next 50 years.
Attendees will only need a laptop.
What skills do participants require to have?:Participants should be comfortable reading and running Python code in a Jupyter notebook
I am a Senior Data Scientist and Climate Scientist with expertise in machine learning, statistical modelling, and geospatial analytics for complex environmental and climate risk challenges. At EP Risk, I lead the development of prototype models assessing multiple climate perils, including flood, bushfire, wind, and coastal erosion. My work bridges data science and climate science to deliver actionable insights for sustainable risk management and ESG initiatives. I hold a PhD in Climate Modelling and Forecasting from the University of Sydney and have experience in academia and industry across environmental modelling and applied data science.
Saba is a geospatial and climate risk analyst with 15+ years' experience across different sectors. She is PhD candidate at the University of Sydney; she specialises in flood risk modelling, satellite data analysis, and GIS systems.