Simulating the Urban Heat Island during Extreme Heat Events Using Crowdsourcing: A Case Study for Bucharest (Romania)

Abstract Cities garner significant attention in discussions about climate change and its impacts, yet monitoring their urban climate encounters considerable challenges. Metropolitan areas have heterogeneity in their surfaces and functions, and monitoring their climate properties should address this condition as much as possible. Including observations from nontraditional sources (such as crowdsourcing) may be useful to represent the local nuances and heterogeneity in urban weather conditions. This paper is the first to explore improvements contributed by including crowdsourced data in characterizing the urban climate of Bucharest (Romania), using data retrieved by two major citizen weather station (CWS) networks: Netatmo and Wunderground. The analysis focuses on the summer months and two heatwave events occurring in 2023. Results demonstrate that integrating crowdsourced data with those from standard networks can significantly improve the spatial representation of temperature variations across Bucharest. The inclusion of CWS data enriches our understanding of urban temperature patterns, providing actionable insights for urban planning and adaptation strategies to mitigate heat stress. Expanding CWS networks in urban areas could enhance public awareness and promote community engagement in climate adaptation efforts. Significance Statement This study seeks to enhance urban temperature gridding by integrating crowdsourced and institutional weather monitoring networks, using Bucharest (Romania) as a case study. We present the temperature patterns and urban heat island effect during two 2023 heat waves. Results show that combining data from standard weather stations and citizen weather stations clearly improves temperature mapping, especially for minimum temperatures, which better capture the heat island effect. This improved dataset supports urban planning and climate adaptation initiatives and advocates for the development of citizen observations.