Seminar: Melanie Weynants

Extreme climate events are on the rise. Heatwaves and droughts have uneven impacts on ecosystems that can be exacerbated in case of compound events. ESA AI4Science DeepExtremes project aims at studying those impacts with machine learning on high-resolution minicubes sampled from large-scale extreme events. In this context, we build a database of large-scale heat and drought extreme events based ERA5 reanalysis data. These were aggregated at daily scale to detect extremes on a daily basis. We computed drought indicators based on daily evapotranspiration and precipitation averaged over 30, 90 and 180 days. We detected extreme values over 1950-2021 and created a datacube of extremes and non-extremes for maximum temperature and the three drought indicators. For the years 2016 to 2021, we extracted labelled compound extreme heat and drought events. We verified that extreme events documented in the literature are captured in our database.

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