4 citations · 9 across the 6 of their papers we have counts for
6 papers
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…
Next-Generation Earth System Models: Towards Reliable Hybrid Models for Weather and Climate Applications
Tom Beucler, Erwan Koch, Sven Kotlarski +3
We review how machine learning has transformed our ability to model the Earth system, and how we expect recent breakthroughs to benefit end-users in Switzerland in the near future.…
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…
Spatiotemporal wildfire modeling through point processes with moderate and extreme marks
Jonathan Koh, François Pimont, Jean-Luc Dupuy +1
Accurate spatiotemporal modeling of conditions leading to moderate and large wildfires provides better understanding of mechanisms driving fire-prone ecosystems and improves risk m…
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…