My first hackathon. UofT Climate Hackathon 2024 tackled pressing climate issues with technology — drought mitigation, snowfall cleanup, global warming. Our team took on estimating optimal cooling-station locations for the City of Toronto, based on projected summertime hot days between 2071 and 2100, using downscaled CanESM5 data.
Results
We placed cooling centers where no existing solution overlapped with low-income areas — identifying City-owned locations in these high-risk areas that could be converted into makeshift cooling centers.
Something cool I learned
Climate models take a huge amount of data and can take weeks or months to run on supercomputers. To forecast Toronto specifically, we took an existing global climate model (GCM) and downscaled it to our region using statistical downscaling — combining the GCM with historical weather data to raise the resolution to fit our geogridded data.
Built with Mevan Solanga, Asli Bese, and Peter Angelinos.