4 citations · 9 across the 6 of their papers we have counts for
4 papers · 1 filter
Using spatial extreme-value theory with machine learning to model and understand spatially compounding weather extremes
Jonathan Koh, Daniel Steinfeld, Olivia Martius
When extreme weather events affect large areas, their regional to sub-continental spatial scale is important for their impacts. We propose a novel machine learning (ML) framework t…
Space-time extremes of severe US thunderstorm environments
Jonathan Koh, Erwan Koch, Anthony C. Davison
Severe thunderstorms cause substantial economic and human losses in the United States. Simultaneous high values of convective available potential energy (CAPE) and storm relative h…
Gradient boosting with extreme-value theory for wildfire prediction
Jonathan Koh
This paper details the approach of the team in the 2021 Extreme Value Analysis data challenge, dealing with the prediction of wildfire counts and sizes over…
Trends in the extremes of environments associated with severe US thunderstorms
Erwan Koch, Jonathan Koh, Anthony C. Davison +2
Severe thunderstorms can have devastating impacts. Concurrently high values of convective available potential energy (CAPE) and storm relative helicity (SRH) are known to be conduc…