Abstract
The availability of gridded, screen-level air temperature data at an effective spatial and temporal resolution is important for many fields such as climatology, ecology, urban planning and design. This study aims at providing such data in a data-scarce, arid city within the greater Cairo region (Egypt), namely the Sixth of October, where, to our knowledge, no such data are available. By using (i) air temperature data, collected from mobile measurements, (ii) multiple spectral indices, (iii) spatial analysis techniques and (iv) random forest regression modelling, we produced air temperature maps (for both daytime and nighttime) at 30-m spatial resolution for the entire city. The proposed method is systematic and relies on low-cost instrumentation and freely-available satellite data and hence it can be replicated in similar data-scarce, arid areas to allow for better spatial and temporal monitoring of air temperature.
Highlights
With heatwaves becoming more severe and frequent across many parts of the world [1], the interest in better understanding the urban micro- and local climate phenomena has been growing both in research and practice of urban planning and design
We explore the effectiveness of the random forest (RF) regression in modelling air temperature in an arid area, using sample air temperature data, collected from low-cost mobile measurement campaigns, and multiple spectral indices, derived from freely-available satellite imagery
Central urban areas with relatively higher building density are cooler than the surrounding desert, but warmer at night which is typical for arid cities. This can be returned to the shadows cast by tall and compact buildings, which reduce the amount of absorbed solar radiation by surfaces
Summary
With heatwaves becoming more severe and frequent across many parts of the world [1], the interest in better understanding the urban micro- and local climate phenomena has been growing both in research and practice of urban planning and design. Monitoring air temperature in the urban canopy layer (beneath the roof level) has been always limited by the availability and spatial coverage of air temperature data from fixed weather stations [4,5]. Mobile measurements, using instruments mounted on vehicles (e.g. cars, bicycles) or carried by humans, can be used to complement observations from fixed weather stations or to observe places that are rarely explored or with spatial heterogeneity of air temperature [6] and have been used in many studies [e.g. 7–15]. Air temperature data obtained either from fixed weather stations or using mobile measurements are collected as point samples and cannot continuously describe the spatial variability of air temperature. Providing gridded air temperature data at high spatial resolution has become of great importance and different modelling approaches have been used for this purpose such as interpolation, regression and simulation [3]
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