EGU22-10276, updated on 25 Oct 2023
https://doi.org/10.5194/egusphere-egu22-10276
EGU General Assembly 2022
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Weather and climate in the AI-supported early warning system DAKI-FWS

Elena Xoplaki1,2, Andrea Toreti3, Florian Ellsäßer2, Muralidhar Adakudlu2, Eva Hartmann1, Niklas Luther2, Johannes Damster1, Kim Giebenhain4, Andrej Ceglar2, and Jackie Ma5
Elena Xoplaki et al.
  • 1Department of Geography, Justus Liebig University Giessen, Giessen, Germany (elena.xoplaki@geogr.uni-giessen.de)
  • 2Center for international Development and Environmental Research, Justus Liebig University Giessen, Giessen, Germany
  • 3Joint Research Centre, European Commission, Ispra, Italy
  • 4Department of Physics , Justus Liebig University Giessen, Giessen, Germany
  • 5Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute HHI, Berlin, Germany

The project DAKI-FWS (BMWi joint-project “Data and AI-supported early warning system to stabilise the German economy”; German: “Daten- und KI-gestütztes Frühwarnsystem zur Stabilisierung der deutschen Wirtschaft”) develops an early warning system (EWS) to strengthen economic resilience in Germany. The EWS enables better characterization of the development and course of pandemics or hazardous climate extreme events and can thus protect and support lives, jobs, land and infrastructures.

The weather and climate modules of the DAKI-FWS use state-of-the-art seasonal forecasts for Germany and apply innovative AI-approaches to prepare very high spatial resolution simulations. These are used for the climate-related practical applications of the project, such as pandemics or subtropical/tropical diseases, and contribute to the estimation of the outbreak and evolution of health crises. Further, the weather modules of the EWS objectively identify weather and climate extremes, such as heat waves, storms and droughts, as well as compound extremes from a large pool of key data sets. The innovative project work is complemented by the development and AI-enhancement of the European Flood Awareness System model, LISFLOOD, and forecasting system for Germany at very high spatial resolution. The model combined with the high-end output of the seasonal forecast prepares high-resolution, accurate flood risk assessment. The final output of the EWS and hazard maps not only support adaptation, but they also increase preparedness providing a time horizon of several months ahead, thus increasing the resilience of economic sectors to impacts of the ongoing anthropogenic climate change. The weather and climate modules of the EWS provide economic, political, and administrative decision-makers and the general public with evidence on the probability of occurrence, intensity and spatial and temporal extent of extreme events as well as with critical information during a disaster.

How to cite: Xoplaki, E., Toreti, A., Ellsäßer, F., Adakudlu, M., Hartmann, E., Luther, N., Damster, J., Giebenhain, K., Ceglar, A., and Ma, J.: Weather and climate in the AI-supported early warning system DAKI-FWS, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10276, https://doi.org/10.5194/egusphere-egu22-10276, 2022.