Researchers studying urban climates aim to understand phenomena like urban heat islands (UHIs), which describe temperature differences between urban and rural areas. However, studies often lack numerous measurement points and frequently overlook parameters like radiation and air velocity due to the high cost of precision instrumentation. This results in data with a low resolution, particularly in tropical cities where official weather stations are scarce. This research introduces a new, low-tech tool for district-level outdoor thermal comfort assessment and UHI characterization to address these challenges. The automated weather station employs sensors to measure temperature, humidity, wind speed, solar radiation, and globe temperature. The paper details these sensors’ rigorous selection and validation process, followed by a description of the sensor assembly, data acquisition chain, and network operation mechanisms. Calibration outcomes in laboratory and in situ environments highlight the station’s reliability, even in tropical conditions. In conclusion, this weather station offers a cost-effective solution to gathering high-resolution data in urban areas, enabling an improved understanding of the UHI phenomenon and the refinement of urban microclimate numerical models.

Dense tropical cities face increasing outdoor heat stress and require reliable microclimate modelling tools to support climate-responsive urban planning. However, the performance of these models remains insufficiently documented under tropical conditions. The objective of this study is to experimentally validate the performance of ENVI-met for outdoor thermal comfort assessment in a dense tropical neighbourhood on Reunion Island. Validation was performed using a network of 11 low-cost weather stations distributed across the study area, providing observations of air temperature, relative humidity, wind speed and globe temperature, from which mean radiant temperature (MRT) and the Universal Thermal Climate Index (UTCI) were derived. Model performance was evaluated through multipoint validation, error-propagation analysis and sensitivity experiments. Results show that ENVI-met reproduces air temperature and relative humidity with good accuracy (RMSE of 1.2 °C and 5%), while larger uncertainties are observed for wind speed (up to 4 m s⁻¹) and MRT (up to 8 °C), with strong spatial variability. Error-propagation analysis indicates that 62% of the UTCI error variance is explained by combined microclimate simulation errors, with radiative conditions emerging as the dominant contributor. Sensitivity analyses identify boundary wind forcing as the main source of modelling uncertainty. Overall, the proposed validation framework provides practical guidance for improving ENVI-met applications in tropical urban environments and supports more robust climate-responsive urban design.

This dataset provides a high-resolution spatial classification of urban morphology on Reunion Island, France, using a 100 m x 100 m grid based on the Local Climate Zone (LCZ) framework. The data were generated through a combination of satellite imagery analysis using the World Urban Database and Access Portal Tools (WUDAPT) method and Geographic Information System (GIS) data integration. The classification was refined using building height and surface fraction data from the BD TOPO 2018 database, improving the accuracy of urban form representation.