Resolving microscale surface temperature variability during a heatwave using a dense sensor network

Mark Ireland, Hector George Barnett , Abdullah Kahraman, Charles K Dunham

High-resolution observations of near-surface temperature variability are essential for understanding heat exposure during extreme weather events, yet are rarely available from dense, regularly spaced in situ measurement grids, with most existing observations derived from unevenly distributed sensor networks. Here, we analyse temperature measurements derived from a dense network of over 3,000 nodal seismometers deployed across a 6 km² area in North Yorkshire, UK, during the July 2022 heatwave, using data from embedded microcontroller sensors. Although the embedded microcontroller sensors provide incidental environmental measurements at unprecedented spatial and temporal resolution, with over 80 million logged temperatures recorded at 100 s intervals for 34 days.

Microcontroller derived temperature measurements were statistically corrected using co-located meteorological observations to account for systematic biases introduced by the thermal response of the sensor housing to closer approximate true near-surface temperatures across the network. The corrected dataset reveals strong spatial heterogeneity in surface temperatures, with mean differences of up to 3.6 °C at distances of 100–200 m and extreme contrasts exceeding 17 °C during peak heating conditions. Spatial variability is interpreted to be strongly modulated by meteorological forcing, with daytime heatwave conditions exhibiting the greatest heterogeneity, while nighttime periods show substantial homogenisation. Systematic but modest differences are observed between land-use classes (~0.1 °C), expressed primarily through variations in persistence and extremes rather than mean temperature alone.

The results highlight the importance of sub-kilometre variability in near surface temperature fields and demonstrate the potential of repurposing incidental environmental sensing to characterise microclimate environments at scales relevant to ecosystem dynamics and human exposure. More broadly, the findings open the potential, with improved calibration, that dense geophysical arrays could provide unique insights into fine-scale meteorological dynamics with implications for the interpretation of satellite-derived land surface temperature, the development of high-resolution modelling approaches, and the assessment of heat-related risks.