Evaluation and application of a low-cost measurement network to study intra-urban temperature differences during record summer 2018

Abstract

The understanding of intra-urban temperature variations is crucial for cities’ adaptation to impacts of present and future anthropogenic climate change. Depending on extensive measurement networks, high-resolution air temperature measurements in urban environments are challenging due to high instrumentation and maintenance costs. Here, we present a low-cost measurement device (LCD) consisting of a temperature logger and a custom made, naturally ventilated radiation shield. Besides inter-comparisons with automated weather stations (AWS) at three reference sites during record-dry summer 2018, we tested the potential using a network of 76 LCD to map the intra-urban variability of urban heat island (UHI) patterns throughout an intense heat wave based in the city of Bern, Switzerland. We found positive mean measurement biases between LCD and AWS of 0.61 to 0.93 K (RMSE: 0.78 to 1.17 K) during daytime, of which up to 82.8% could be explained by radiative heating of solar irradiance and insufficient ventilation by prevailing winds. During night, average measurement biases were markedly lower and eventually negative with -0.12 to 0.23 K (RMSE: 0.19 to 0.34 K). Our results further highlight the importance of sensor inter-comparisons being conducted at multiple locations with differing urban forms given that biases varied considerably between the reference sites. Data retrieved by the city-wide measurement network showed that the LCD approach is well suited for the analysis of spatiotemporal UHI-patterns during night with maximum UHI intensities of up to 5.5 K. In conclusion, the here presented LCD measurement approach represents a valuable option for cost-effective analyses of urban air temperature variability across multiple scales, which may be of particular value for the development, appliance, and monitoring of adaptation strategies to climate change in cities with restricted financial resources.