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extrAIM

National Technical University of Athens (GR)

Summary

extrAIM (AI-enhanced uncertainty quantification of satellite-derived hydroclimatic extremes) is part of the AI4SCIENCE activity. The first AI4SCIENCE ITT was launched in 2021 and had a focus on Extreme Events, Multi-Hazards and Compound Events, and contributes to the ESA Extremes and Natural Disasters Science Cluster. 

The AI4SCIENCE ITT had 2 main objectives:

  1. Advancing Earth System Science: advancing our capacity to combine EO and AI to address a major scientific challenge: The observation, understanding and characterisation of multi-hazards, compound and cascade events and its impacts on society and ecosystems.
  2. Advancing Artificial Intelligence for EO: unlocking the full potential of Artificial Intelligence for Earth System Science with focus on two main AI challenges: physics-driven Artificial Intelligence and explainable AI.

extrAIM will develop a first-of-its-kind, satellite-based, low-latency, uncertainty-aware precipitation dataset for the Mediterranean region, adjusted to account for the extremes’ probabilistic behavior.

extrAIM will combine statistical learning and Bayesian modelling methods (for uncertainty quantification) with an AI (Artificial Intelligence)-enhanced dataset integration approach, suitable for combining multiple precipitation products (e.g., satellite-data, estimates based on soil moisture), with an eye on model’s explainability.

Finally, and with improving understanding and awareness in mind, extrAIM will develop a user-friendly data-management and visualization platform able to provide easy access to the UA Mediterranean dataset, as well as communicate risks arising from individual and compound extreme events. In more detail, extrAIM project’s specific objectives are: 

1.  The development of an AI-enhanced, yet explainable and operational approach capable of optimally combining multiple SPPs into a single, and improved integrated SPP. 

2.   The development of a general probabilistic framework for the uncertainty modelling and quantification of the quantitative precipitation estimates obtained by SPPs (with a focus on extremes). 

3.     The creation of a first-of-its-kind UA satellite-based precipitation dataset for the Mediterranean region.

4.    The development of a user-friendly data analysis and visualization platform, which will enable easy data retrieval and visualization, aiming to increase understanding and awareness against hydroclimatic risks arising from individual and compound extreme events.


Information

Domain
Science
Prime contractor
National Technical University of Athens (GR)
Subcontractors
  • CNR-RESEARCH INSTITUTE FOR GEO-HYDROLOGICAL PROTECTION – IRPI (IT)
  • Tethys Consulting (GR)