Citizen data for global mapping of atmospheric urban heat islands

Abstract Urban heat islands are compounding the dangerous impacts of urban heat exposure globally. Cities across geographies require immediate and advanced urban climate information with enough resolution to detect risks and prioritise local climate adaptation interventions for their people. This research analyses the applicability of 550,000 citizen weather stations globally, a network 12 times larger than professionally operated weather stations, to measure urban climate at the highest spatio-temporal resolution and support climate adaptation policies globally. Cities with higher citizen data availability worldwide are identified, and a first open-source procedure in Python is proposed that cities around the world can use. The approach is validated in London , integrating temperature data from Netatmo and Wunderground platforms. The validation tests showed how pre-processing techniques improve citizen data accuracy, decreasing mean temperature deviation from 0.98ºC to 0.48ºC, inside the precision range of sensors . Data analytic methods identified significant implications of urban overheating in the case of London, increasing annual cooling degree hours by 60% in some urban areas, with different night and daytime hot spots. Aerodynamics and imperviousness affected urban overheating at night and daytime differently, requiring different localised interventions. Such large-scale application and scalability of open-access and readily available citizen data networks can efficiently support and monitor localised climate adaptation to mitigate the impacts of rising heat.