| description abstract | ow much does minimum daily air temperature vary within neighborhoods exhibiting high land surface temperature (LST), and does this variability affect agreement with the nearest weather station? To answer these questions, a low-cost sensor network of 135 ?iButton? thermometers was deployed for summer 2015 in Baltimore, Maryland (a midsized American city with a temperate climate), focusing on an underserved area that exhibits high LST from satellite imagery. The sensors were evaluated against commercial and NOAA/NWS stations and showed good agreement for daily minimum temperatures. Variability within the study site was small: mean minimum daily temperatures have a spatial standard deviation of 0.9°C, much smaller than the same measure for satellite-derived LST. The sensor-measured temperatures agree well with the NWS weather station in downtown Baltimore, with a mean difference for all measurements in time and space of 0.00°C; this agreement with the station is not found to be correlated with any meteorological variables with the exception of radiation. Surface properties are found to be important in determining spatial variability: vegetated or green spaces are observed to be 0.5°C cooler than areas dominated by impervious surfaces, and the presence of green space is found to be a more significant predictor of temperature than surface properties such as elevation. Other surface properties?albedo, tree-canopy cover, and distance to the nearest park?are not found to correlate significantly with air temperatures. | |
