A machine-learning (ML) tool developed by Stanford University researchers aims to more accurately predict extreme weather events.
Researchers trained a ML algorithm to detect large-scale atmospheric circulation patterns tied to extreme precipitation, defined as above the 95th percentile, with a focus on the upper Mississippi and eastern Missouri watersheds. The algorithm found multiple factors responsible for increased extreme precipitation in the region and accurately identified more than 90% of extreme precipitation days.
"While we focused on the Midwest initially, our approach can be applied to other regions and used to understand changes in extreme events more broadly," said Stanford's Frances Davenport. "This will help society better prepare for the impacts of climate change."
From Stanford University
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