Publications

Articles (in revision)

Koh, J. (2024+). Statistics of extremes for natural hazards: wildfires. Book chapter in preparation, for Chapman and Hall/CRC Handbook on Statistics of Extremes.
Koh, J., Steinfeld, D., Martius, O. (2023+). Using spatial extreme-value theory with machine learning to model and understand spatially compounding extremes. ArXiv: 2401.12195.
Koh, J., Opitz, T. (2023+). Extreme-value modelling of migratory bird arrival dates: Insights from citizen-science data. ArXiv: 2312.01870.
Beucler, T., Koch, E., Kotlarski, S., Leutwyler, D., Michel, A., Koh, J. (2023+). Next-Generation Earth System Models: Towards Reliable Hybrid Models for Weather and Climate Applications. ArXiv: 2311.13691
Barton, Y., Aregger, M., Koh, J., Martius, O. (2023+). Serial clustering of convective storms and link to sub-hourly rainfall extremes in Switzerland - a large-scale process perspective.
Koh, J., Koch, E., Davison, A. C. (2022+). Space-time extremes of severe US thunderstorm environments. ArXiv: 2201.05102.

Poschlod, B., Koh, J. (2024). Convection-permitting climate models can support observations to generate rainfall return levels. Water Resources Research, 60(4):e2023WR035159
Koh, J. (2023). Gradient boosting with extreme-value theory for wildfire prediction. Extremes, 26(2):273–299.
Koh, J., Pimont, F., Dupuy, J.-L., Opitz, T. (2023). Spatiotemporal wildfire modelling through point processes with moderate and extreme marks. The Annals of Applied Statistics, 17(1):560-582. Video here. Correction here.
Barton, Y., Rivoire, P., Koh, J., Ali, S. M., Kopp, J., Martius, O. (2022). On the temporal clustering of European extreme precipitation events and its relationship to persistent and transient large-scale atmospheric drivers. Weather and Climate Extremes, 38:e100518.
Koch, E., Koh, J., Davison, A. C., Lepore, C., Tippett, M. K. (2021). Trends in the extremes of environments associated with severe US thunderstorms. Journal of Climate, 34(4):1259–1272.
Davison, A. C., Koch, E., Koh, J. (2019). Comment: Models are approximations! Statistical Science, 34(4):584–590.