CLIM4cities: from Citizen Science, Machine Learning and Earth Observation towards Urban Climate Services

As climate change prospects point towards the pressing need for local adaptation strategies, exposure to extreme weather events becomes one of the most important aspects in determining our society’s resilience in the future. At the local level, these conditions are strongly influenced by the energy exchanges between the lower atmosphere and our strongly modified urban surfaces. To address these challenges, CLIM4cities - a European Space Agency (ESA)-funded project under the call for Artificial Intelligence (AI) Trustworthy Applications for Climate - aims to pioneer the development of Machine Learning (ML) and Artificial Intelligence (AI) models designed to downscale air and land surface temperature predictions in urban areas. This initiative serves as a preliminary step towards the implementation of cost-effective Integrated Urban Climate and Weather components into local Digital Twin Systems. By leveraging crowdsourced data obtained from citizens-owned weather stations, Earth Observation and weather forecasting models, we offer spatio-temporal data fusion models that can solve the unmet need for a low-cost, efficient and scalable Urban Climate prediction system. To achieve this, CLIM4cities has tailored its solution to the requirements of local early adopters, who state the need for tools that offer both early warning weather forecast capabilities, as well as scenario-making capabilities to evaluate climate adaptation measures, namely the impact of blue-green infrastructures on the Urban Heat Island effect. The first version of its coupled ML-based near-surface Air Temperature (T2m) and Land Surface Temperature (LST) downscaling models, targeting four metropolitan areas in Denmark, proving the concept’s reliability and scalability to other urban regions.