Citizen weather stations (CWS) have emerged as valuable tools for urban climate research, providing hyperlocal observations that complement traditional meteorological networks. This study evaluates the spatio-temporal distribution and data quality of temperature measurements from two prominent CWS networks—Netatmo and Wunderground—using Paris as a case study. Paris offers a unique research setting due to its high density of CWS and an extensive network of professionally operated weather stations, enabling rigorous data validation.

Temperature data from both networks were analyzed before and after applying quality control techniques using the MetObs Toolkit. The analysis reveals that Netatmo, with a significantly larger number of stations, exhibits higher noise levels and variability, leading to challenges in data reliability. In contrast, Wunderground, despite its smaller network size, provides more consistent and stable temperature data. Key metrics such as data completeness, outlier and gap frequency, and alignment with official meteorological observations were assessed to quantify these differences.

Beyond the Paris case study, the global distributions and densities of Netatmo and Wunderground stations were examined, showcasing their heterogeneous spatial coverage. These insights help researchers identify the most suitable data sources based on specific study requirements, as well as understand their applicability and limitations.

This study underscores the trade-offs between data quantity, quality, and accessibility in CWS networks, providing valuable guidance for integrating these datasets into urban climate research and supporting data-driven climate adaptation strategies.

How to cite: agrawal, R., lizana, J., San-Nicolás Vargas, P., and Núñez-Peiró, M.: Spatio-Temporal Distribution and Data Quality of Citizen Weather Stations: A Comparative Study of Netatmo and Wunderground in Paris, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-59, https://doi.org/10.5194/icuc12-59, 2025.

Crowdsourced observations have significant potential to enhance our understanding of urban climates. Compared to existing baselines that underpin numerous climate services, gridded climatologies built from crowdsourced observations have the potential to show substantial increases in estimated heat hazard. Yet, the transient nature and variable quality of such observation networks offers challenges to building long-term gridded datasets of a comparable quality to those built from standard observing networks.

Leveraging the maturity of the Met Office’s Weather Observations Website (WOW), this project is building on a previous regional-scale pilot study to produce a twelve-year gridded crowdsourced temperature dataset at national scale. WOW observations from the period 2013-2024 are quality-controlled and interpolated to yield a set of daily grids of maximum, minimum, and mean temperature at 1km resolution for the UK.

The resulting urban-sensitive decadal record will offer a step-change in capability for urban climate services. The dataset has been co-designed with government partners to support the assessment of vulnerability posed by overheating to buildings, and research into mitigation/adaptation decisions across the public sector estate. Furthermore, the methodologies that have been developed will provide a springboard for further exploration into the potential impact of transient sensing - where the composition of a crowdsourced dataset evolves over time - on the resilience of crowdsourced climatological baselines.

How to cite: Fry, M., Mitchell, T., and Farrar, L.: Gridded Climatologies from Crowdsourced Data: A 12-Year Daily Dataset for Climate Services in the UK, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-173, https://doi.org/10.5194/icuc12-173, 2025.

Over the last decade, the use of Citizen Weather Stations (CWS) has gradually gained acceptance within the urban climate community. Among the reasons for that uptake are the increasing spatial and temporal coverage provided by these stations, together with sophisticated quality control and gap filling techniques. Although their potential is undeniable, challenges remain regarding uneven spatial coverage and the presence of sensor deserts, particularly in vulnerable areas. In addition, the growing volume of CWS data requires screening out the most representative points to reduce density and facilitate data processing. In this study, we use CWS data from Madrid, to investigate how CWS density and their spatial distribution might affect the accuracy of urban climate severity maps. A spatial optimisation algorithm is used to identify the most representative CWS. This is tested with six clustering techniques, which results in 120 different optimisation scenarios. Urban climate severity maps are then produced for each scenario using Empirical Bayesian Kriging (EBK) regression. The accuracy of each scenario is evaluated against a baseline scenario using all the available CWS. Results show that selecting 30-50% of the total number of stations yields  relatively small deviations from the baseline while preserving the overall urban heat distribution pattern. Furthermore, for the same number of CWS, accuracy among clustering techniques varies by up to 18% when compared to the baseline. These findings underscore the dual benefits of our approach: first, it facilitates the identification of optimal locations for new sensors in areas with insufficient coverage, addressing sensor deserts. Second, it provides a framework for scenarios with high density CWS data, enabling efficient resource allocation when not all data is necessary to produce reliable results. This highlights the potential of spatial optimisation in enhancing urban climate monitoring and decision-making, particularly in addressing vulnerabilities associated with uneven sensor distributions.

How to cite: San-Nicolás Vargas, P., Núñez-Peiró, M., Lizana, J., Lado-Masson, S., Agrawal, R., and Sánchez-Guevara Sánchez, C.: Optimising crowsourced weather data point density for urban climate studies, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-278, https://doi.org/10.5194/icuc12-278, 2025.

Urban overheating is a critical challenge for cities, posing risks to human health and well-being. In response, some cities implement heat mitigation strategies in public spaces to alleviate this condition, but their effectiveness depends also on public awareness. This study investigates citizens’ perception of heat mitigation strategies, focusing on urban parks, by analyzing publicly available crowdsourced comments from a digital mapping platform. Using text mining, these reviews were compared with online descriptions of these places. New York City parks served as case studies as the government provides information on their facilities, number of mapped trees, park conditions, and other features on a dedicated website. A total of 8,558 comments from 11 urban parks were analyzed. About 10% of them mentioned the presence of trees and/or greenery at these parks, indicating a perception of their role in heat mitigation. Shading, usually associated with trees, was also frequently mentioned, suggesting that people recognize tree shading as a mitigation strategy. However, the number of mapped trees in a park was not associated with the number of mentions of “trees” or “shading” in its comments. Seven parks have spray showers and two have outdoor pools as additional mitigation amenities, but only 0.1% of the comments referred to the showers, and no one cited the pools. These findings demonstrate a need for improving the communication related to available mitigation actions. Public channels, such as webpages related to these places, should highlight these strategies to increase public awareness and motivate park attendance not only for the activities offered but also for alleviating heat stress. Additionally, encouraging citizens to share their environmental perceptions on review platforms can provide valuable feedback to the public administration, contributing to a better understanding of the actual environmental conditions and to the implementation of targeted measures to enhance urban livability.

How to cite: Jacoby Cureau, R., Fronzetti Colladon, A., Pigliautile, I., and Pisello, A. L.: Urban heat mitigation and public perception: insights from crowdsourced data, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-646, https://doi.org/10.5194/icuc12-646, 2025.

Urban areas with their high population density and urban heat islands (UHI), are increasingly vulnerable to heat waves under future climate scenarios. Urban climate models are essential for quantifying the potential benefits of strategies to mitigate UHI and support climate adaptation efforts. Buildings are pivotal in thermal exchanges within urban environments, making detailed building information crucial for the accuracy of urban climate models.

Frameworks for generating urban morphological parameters already exist, such as land cover classifications like Local Climate Zones (LCZ) or tools like Geoclimate, which computes detailed parameters from Open Street Map cadaster. But there is a critical lack of details on materials, insulation, and Heating, Ventilation, and Air Conditioning (HVAC) systems. The Global Data Set of Urban and Building Properties, the only global database on building materials, aggregates regions into 33 zones, limiting geographical precision.

To address this gap, we developed a crowdsourcing approach to collect building data worldwide at the country scale. A global survey was designed to describe buildings by their typology based on country, use (residential, commercial, office, and industrial), and form (low-rise, mid-rise, high-rise, and large low-rise). Despite existing variability, these typologies capture major architectural trends and construction practices within each area. Key data collected encompassed building envelope characteristics (walls, roof, windows and potential insulation layers) and the presence of HVAC systems.

As of February 2025, the survey, available in 11 languages, received 510 responses from 139 countries, offering significant global coverage. Residential building typologies were defined for each country, with gaps filled using neighboring data. For non-residential buildings, globally applicable typologies were derived from the responses. Further work could enhance regional specificity. This valuable resource for urban climate modeling will be freely available in GIS and CSV formats, enabling more accurate climate assessments and informed urban planning decisions worldwide.

How to cite: de Carvalho Araujo, L., Masson, V., Schoetter, R., and Wurtz, J.: Building a global database on architecture and construction materials for urban climate models: a crowdsourcing initiative, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-853, https://doi.org/10.5194/icuc12-853, 2025.

Data from non-traditional measurement networks, such as crowdsourced meteorological data, present unique and complex challenges in data structure, quality, consistency, lack of metadata, and analysis. Observational campaigns often encounter issues such as missing data due to technical failures, power outages, or communication problems. Furthermore, crowdsourced or low-cost sensor data require rigorous quality control to mitigate errors and biases, leading to additional gaps in the data. Additionally, inconsistent storage formats and temporal resolution complicate the use and combination of datasets across different networks. 

To address these challenges, we developed the MetObs-toolkit, an open-source Python package designed to streamline the processing of observational meteorological data. The MetObs-toolkit facilitates the entire workflow, from raw sensor data to a comprehensive analysis. Common use cases are resampling and synchronization of time series, automated and spatial quality control, Google Earth Engine interaction for additional metadata, gap-filling techniques, and analysis tools. Designed for both students and experienced scientists, MetObs offers accessible tutorials and examples, enabling users to gain insights into their observational data. By providing an open-source standardized approach, MetObs improves data usability, ultimately enhancing the impact of non-traditional observational data efforts.

This presentation will demonstrate how the MetObs-toolkit facilitates the analysis of crowdsourced and non-traditional urban climate data.

How to cite: Vergauwen, T., Vieijra, M., Covaci, A., Jacobs, A., Top, S., Dewettinck, W., Vandelanotte, K., Hellebosch, I., and Caluwaerts, S.: MetObs, to streamline your crowdsourced data processing., 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-361, https://doi.org/10.5194/icuc12-361, 2025.

The interaction between walkability and outdoor thermal comfort (OTC) assessments is key in the context of climate change, since optimizations of outdoor areas are relevant to ensure overall cardiovascular health of the population (overheated urban environments discourage outdoor activities, resulting in increasingly sedentary, indoor lifestyles). Motivation of the study was to combine walkability and OTC assessments with guided walks using wearable equipment. 6 walks on different days were conducted in a subtropical location. Research participants completed the approximately 1km circuit in 40min, with five stops, during which they filled out a questionnaire that included items related to the accessibility of street segments, crossings and at a given stop, and OTC-related items. Concurrently, an Arduino-ESP32 platform built on a backpack registered air temperature and humidity, wind speed and globe temperature. Physiological data were also recorded (skin temperature and heart rate). A pairwise comparison of perceptual evaluation and physiological response tested the impact of metabolic activity rate of an active walker against a sedentary person, which remained seated on a wheelchair, both with equivalent BMI. Altogether 10 students comprise the sample. As per research protocol, at a pre-conditioning stage, participants remained 30min indoors in an air-conditioned thermal environment, leaving that space fitted with Thermochron iButtons and heart rate straps, accompanied by two researchers, one of which wore the portable monitoring unit and the other pushed the wheelchair. Exposure conditions during campaigns showed mild ambient temperatures, mostly under cloudy conditions. Sedentary subjects felt slightly cooler at the stops and less satisfied with the street segments than walkers in terms of OTC, with noticeable point-specific changes in perceptual responses. Accessibility of the segments was rated worse by sedentary subjects, and again differences in accessibility evaluation were point specific. Heart rate was higher for the walker whereas skin temperature showed minute effects from point exposure.

How to cite: Kruger, E., Alcantara Rosa, L., Cerutti, S., de Dear, R., and Solange, L.: Walkability and outdoor thermal comfort evaluation with a portable low-cost environmental monitoring platform, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-776, https://doi.org/10.5194/icuc12-776, 2025.

As smart cities increasingly incorporate Information and Communication Technologies (ICTs) into urban infrastructure, microclimate information displays integrated into smart lampposts present a novel strategy for enhancing climate adaptation and optimising the usability of public spaces. This study investigates their impact on user behaviour by developing a microclimate information-user behaviour model, drawing on Environmental Behaviour Theory (EBT) and the Theory of Planned Behaviour (TPB). A survey-based study was conducted in Shenzhen, employing Partial Least Squares Structural Equation Modelling (PLS-SEM) for data analysis. The study investigates the influence of environmental perception attitudes, behavioural attitude, subjective norms, and perceived behavioural control on behavioural intentions and actual behavioral choices. The findings indicate that a positive environmental perception enhances users’ confidence in adjusting their activities based on displayed microclimate information. At the same time, strong subjective norms significantly influence behavioural intention, thereby affecting actual behavioural choice, while perceived behavioural control has a direct and significant impact on actual behaviour choice. The results underscore the potential of microclimate information displays in promoting climate-responsive behaviours and improving public space engagement. Optimising microclimate display systems can improve user experience and urban management. Future research and urban planning efforts should focus on integrating real-time data analytics, enhancing user interaction mechanisms, and refining display interfaces to facilitate accessibility and usability. By adopting these advancements, smart cities can foster more adaptive, livable, and resilient urban environments, contributing to the sustainable transformation of public spaces in the face of climate challenges.

How to cite: Yang, W. and Qi, G.: Advancing Urban Climate Adaptation: The Influence of Microclimate Information Displays in Public Spaces of Smart Cities, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-1077, https://doi.org/10.5194/icuc12-1077, 2025.

The scorching Urban Heat Island (UHI) impacts have aroused more public concerns. More frequent extreme hot days were recorded under the synergy of climate change and urban development in Hong Kong. To quantify thermal comfort and health threats of high school students who are vulnerable to heat-related illnesses, we applied Geography, Science, Technology, Engineering, Arts and Mathematics (STEAM)-education initiatives in conducting a measurement campaign across the high schools in Hong Kong to extend the microclimate measurement capability. With the experience of hosting Community Weather Information Network (Co-WIN), the University-Government-School partnership network in Hong Kong, we invited students to participate in school-based fieldwork and collected spatiotemporal data that is accessible to the community with our self-developed low-cost IoT sensors. The dataset was further QAQC checked and post-processed with computational fluid dynamics model - ENVI-met to model the spatiotemporal UHI risks in the community. Our results indicated that spatiotemporal variations largely exist in the school building, with the west-facing side (i.e. corridor at different floors) experiencing up to 6^oC higher temperature than on the basketball court, and the shape of school campus might aggravate the stagnant condition by wind blockage risking heat stroke in the afternoon time. To cope with the threats of heat stroke, we further designed a heat alert system linked to our low-cost sensors, which is found to be feasible and effective in ensuring thermal comfort of students. At the same time, hands-on experience in assembling sensors during the campaign has raised students’ interest in STEAM education and awareness of preventing heat stroke. As such, this study offers alternative solution for policymakers and education practitioners to protect their students from upsurging urban temperatures in the future.

How to cite: Chang, M. H. J. and Lam, Y. F.: Micro Campus, Marco Future: Integrating STEAM-based fieldwork and numerical modeling for urban microclimate analytics in Hong Kong, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-565, https://doi.org/10.5194/icuc12-565, 2025.

On 29 October 2024 torrential rainfall exceeding locally 300 mm within less than 24 h, triggered devastating flash floods in the province of Valencia in Spain. Rainfall sums equivalent to more than half a year’s total precipitation occurred within just a few hours.  In this region, more than 150 low-cost weather observation devices, referred to as personal weather stations (PWSs), are located. The network density of PWSs in this region is seven times higher than that of the Spanish Meteorological Agency (AEMET), being able to provide more detailed insights in the rainfall dynamics. Another advantage is that rainfall observations from PWSs are available near real-time for everyone.

In this study we used rainfall observations from PWSs to get local insights into the rainfall event of October 29. Several PWSs measured already more than 180 mm of rainfall in parts of the Magro catchment (1661 km²) in the morning, consequently generating a flash flood in the upstream parts of this rapidly responding catchment. Areal rainfall maps, based on interpolating the PWS data, indicated daily catchment averaged rainfall sums exceeding 150 mm d^-1 across an area of more than 2500 km². Daily rainfall sums recorded by the PWSs showed a slight underestimation of the rainfall with a bias of 4% and a high correlation (r = 0.96) when compared to reported rainfall from AEMET.

This presentation shows the relevance of utilizing PWSs for near real-time rainfall monitoring and potentially flood early warning systems.

How to cite: Rombeek, N., Hrachowitz, M., Wüthrich, D., and Uijlenhoet, R.: Insights from personal weather stations in the torrential rainfall preceding and during the 29 October 2024 Valencia floods, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-1080, https://doi.org/10.5194/icuc12-1080, 2025.

How to cite: Tötzer, T., Bügelmayer-Blaschek, M., Hochebner, A., Kozlowska, A., Schneider, M., Chatzichristaki, C., Molina Lopez, P., Murano, I., Xekalakis, G., and Havlik, D.: Empowering local authorities through collaborative climate resilience research: findings from five South-European regions  , 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-306, https://doi.org/10.5194/icuc12-306, 2025.

The Citizen-Science programme ‘Delft Measures’ has been running for several years in the city of Delft, the Netherlands. Within this programme, interested citizens can apply to receive a low- cost Alecto WS5500 weather station, to measure local meteorological parameters in their own garden. Currently there are over 45 of these citizen-science weather stations spread across neighbourhoods in Delft, capturing the variability of different urban microclimates, with a specific focus on rainfall observations.

However, the scientific quality of the specific citizen weather stations (Alecto) has never been tested, and from previous work we know that rigorous quality assurance is necessary in order to get meaningful weather data. Thus we have installed 8 Alecto stations in The Green Village outdoors urban climate field lab at the TU Delft. Stations have been explicitly installed in ways that a citizen might do: slightly tilted; next to a wall (simulating the limited open garden space of a Dutch residence); on top of a shed; as well as free-standing. These different measurement setups, combined with a row of reference stations, allow us to investigate the bias in rainfall observations caused by less-than-ideal station installation, as well as systematic errors related to the tipping bucket mechanism and sensor drifts. Results show a general overestimation of the Alecto compared to reference stations and radar observations, and a discernible negative bias caused by sheltering effects of plants and, to a lesser extent by walls.

The value of the Citizen Science approach is in the regular contact with the Citizen Scientists, providing valuable feedback and local knowledge that a crowdsourcing-focused approach would not yield, which was of great use in correcting faulty data. Such a two-way process with a long-term group of dedicated Citizen Scientists has shown great potential to improve the usability of crowdsourced data for urban hydrometeorological applications.

How to cite: Droste, A., Boonstra, M., Bogert, M., and De Vries, S.: Delft Measures: Citizen Science for Urban Climate Research, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-791, https://doi.org/10.5194/icuc12-791, 2025.

Monitoring neighborhood-scale variability of weather within cities is critical for understanding the causes of and developing equitable solutions for urban heat, flooding, and air pollution. This is particularly true as conditions are generally not evenly distributed across cities, with the highest temperatures and air pollution and most frequent flooding often in low-income neighborhoods. There is, however, a lack of surface weather measurements within cities that prevents needed analysis of causes of spatial variability and of the efficacy of active or proposed interventions. Here we will discuss the development of a community-based network of weather stations to fill this measurement desert in Baltimore City. The Baltimore Social-Environmental Collaborative (BSEC), a partnership between universities, state agencies, and the Baltimore community, has created a community-centered urban climate observatory that uses low-cost personal weather stations. These weather stations have been installed in a range of location with neighborhoods, including faith-based centers, recreation and community centers, schools, outdoor learning centers, and urban gardens and parks. Examples will be presented that show the use the observatory to quantify the intra-urban variation of heat and the evaluation of mesoscale climate model simulations. In addition, the use of the weather stations for community engagement, education, and empowerment will be discussed.

How to cite: Waugh, D. and the BSEC: The Baltimore community-based weather station network: Filling the urban measurement desert, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-192, https://doi.org/10.5194/icuc12-192, 2025.

Despite ongoing efforts to collect high-resolution datasets that capture the spatial distribution of urban heat, there remains a gap in human-centric monitoring that focuses on the immediate environment of individuals experiencing heat exposure. We aimed to develop a reliable prediction model for air temperature in dynamic outdoor settings using wrist-mounted wearable sensors. Data was collected for 22 days between 2020 and 2024 in Sydney, Australia. Each experiment involved 6 to 15 participants walking through different built environments. When air temperature and relative humidity measured by wrist-mounted sensors were compared to reference sensors, we found that wrist-mounted wearables cannot directly measure air temperature due to the influence of skin temperature. However, we can use their data to train a prediction model for air temperature. We explored three prediction methods: a steady-state heat transfer model of human skin, multi-linear regression, and random forest machine learning (ML). Results showed that the heat transfer model relied heavily on climatic parameters which could be measured by wrist-mounted sensors, limiting the applicability of this method. The linear regression model developed solely based on wrist-mounted data neglected the correlation between its inputs, such as wrist air temperature and wrist skin temperature. In comparison, the ML approach was capable of capturing non-linear, multi-dimensional relationships and demonstrated the best predictive performance. ML tested on out-of-sample data achieved a correlation coefficient (R²) of 0.97 (in contrast with 0.61 and 0.88 for heat transfer and linear regression) between predicted and observed air temperature, with a mean absolute error of <1℃(4.64℃ and 1.81℃). This performance is equivalent to the accuracy of many common air temperature sensors. This prediction model can be an effective method for providing high-resolution air temperature data in cities in moderate climates such as Sydney, while informing future work in other climate backgrounds. 

How to cite: Parchami, M., Nazarian, N., Hart, M. A., Liu, S., and Martilli, A.: Can your Smartwatch Measure Ambient Air Temperature? , 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-244, https://doi.org/10.5194/icuc12-244, 2025.