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Hybrid dry–hot Extremes prediction and AdapTation

CORDIS · observation · Publication date unknown

imited. Subseasonal timescales, between two weeks and two months, have traditionally been a blind spot: conventional weather forecast models are not tailored to these scales. However, the adoption of Artificial Intelligence (AI) may hold the key to fill this gap and reliably predict the upcoming occurrence of heat stress episodes weeks in advance. This would bring enormous societal benefits by enabling emergency planning. In this project, we will explore an innovative way to generate subseasonal forecasts of droughts and heatwaves, and their consequent human heat stress episodes. A 'hybrid' approach will be embraced, i.e., an approach based on physics-based models combined with AI algorithms. Building upon this approach, we will deepen our understanding of the climatic drivers of human heat stress, and explore the future benefits of land-based adaptation practices designed to attenuate these events, including afforestation, crop selection, and large-scale irrigation. Altogether, HEAT will foster our preparedness and resilience to future heat stress episodes – by improving their p

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recordType

award

status

SIGNED

region

EU

value

1983000

unit

EUR