| Autor: |
Karin Mora, Gunnar Brandt, Vitus Benson, Carsten Brockmann, Gustau Camps-Valls, Miguel-Ángel Fernández-Torres, Tonio Fincke, Norman Fomferra, Fabian Gans, Maria Gonzalez, Chaonan Ji, Guido Kraemer, Eva Sevillano Marco, David Montero, Markus Reichstein, Christian Requena-Mesa, Oscar José Pellicer Valero, Mélanie Weynants, Sebastian Wieneke, Miguel D. Mahecha |
| Rok vydání: |
2023 |
| Popis: |
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 (https://rsc4earth.de/project/deepextremes/) 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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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