EGU23-12657, updated on 27 Apr 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

DeepExtremes: Explainable Earth Surface Forecasting Under Extreme Climate Conditions 

Karin Mora1, Gunnar Brandt2, Vitus Benson3, Carsten Brockmann2, Gustau Camps-Valls4, Miguel-Ángel Fernández-Torres4, Tonio Fincke2, Norman Fomferra2, Fabian Gans3, Maria Gonzalez4, Chaonan Ji1, Guido Kraemer1, Eva Sevillano Marco4, David Montero1, Markus Reichstein3, Christian Requena-Mesa3, Oscar José Pellicer Valero4, Mélanie Weynants3, Sebastian Wieneke1, and Miguel D. Mahecha1
Karin Mora et al.
  • 1Leipzig University, Leipzig, Germany (
  • 2Brockmann Consult GmbH, Germany
  • 3Max-Planck-Institute for Biogeochemistry, Jena, Germany
  • 4University of Valencia, Valencia, Spain

Compound heat waves and drought events draw our particular attention as they become more frequent. Co-occurring extreme events often exacerbate impacts on ecosystems and can induce a cascade of detrimental consequences. However, the research to understand these events is still in its infancy. DeepExtremes is a project funded by the European Space Agency ( aiming at using deep learning to gain insight into Earth surface under extreme climate conditions. Specifically, the goal is to forecast and explain extreme, multi-hazard, and compound events. To this end, the project leverages the existing Earth observation archive to help us better understand and represent different types of hazards and their effects on society and vegetation. The project implementation involves a multi-stage process consisting of 1) global event detection; 2) intelligent subsampling and creation of mini-data-cubes; 3) forecasting methods development, interpretation, and testing; and 4) cloud deployment and upscaling. The data products will be made available to the community following the reproducibility and FAIR data principles. By effectively combining Earth system science with explainable AI, the project contributes knowledge to advancing the sustainable management of consequences of extreme events. This presentation will show the progress made so far and specifically introduce how to participate in the challenges about spatio-temporal extreme event prediction in DeepExtremes.

How to cite: Mora, K., Brandt, G., Benson, V., Brockmann, C., Camps-Valls, G., Fernández-Torres, M.-Á., Fincke, T., Fomferra, N., Gans, F., Gonzalez, M., Ji, C., Kraemer, G., Marco, E. S., Montero, D., Reichstein, M., Requena-Mesa, C., Valero, O. J. P., Weynants, M., Wieneke, S., and Mahecha, M. D.: DeepExtremes: Explainable Earth Surface Forecasting Under Extreme Climate Conditions , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-12657,, 2023.