Today, more than half of the world’s population lives in urban areas and the proportion is projected to increase further in the near future (Grimm et al., 2008). Heatwaves worldwide lead to death and enormous economic and ecologic damage, especially in urban areas where the air temperatures are typically larger than in rural areas. In many parts of the world, heatwaves kill more people than any other natural hazard (Changnon, 2003).
Urban regions record higher air temperatures in comparison with their natural surroundings which is mainly caused by changes in the surface energy budget due to increased heat storage capacity of artificial surfaces (Oke, 1982; Roth, 2013). Further contributions to atmospheric heating in urban areas are the reduction of evaporative cooling, differences in convective and advective flows and increased anthropogenic heat release in cities.
Typically, urban areas have a lower albedo than rural areas. Buildings and streets interact with solar radiation in such a way, that the solar radiation is multiple reflected by buildings and roads before the solar radiation is reflected towards space. In urban areas the sky view factor is typically lower than in rural areas, which reduces the thermal emissions. Therefore, the aspect ratio of building height and road width is a crucial parameter to access the small-scale air temperature field in cities.
The phenomenon of higher air temperatures in urban areas is qualitatively accessed with the Urban Heat Island index (UHI). More than 400 cities worldwide are well documented (Basara et al., 2010; Tan et al., 2010; Li and Bou-Zeid, 2013; Li et al., 2015; Founda et al., 2015; Cheval et al., 2009), whose UHI varies between 2 and 8 K, depending on the analyzed city and the chosen method in the different studies. Many studies found a maximum UHI in the late evening, in summertime and in cities with a large population. Many empirical formulas calculate the maximum UHI based on e.g. the sky view factor, population, building properties, albedo functions, or surface and vegetation indices.
Chapman et al. 2017 analyzed in a review paper that largest UHI occur during low wind speeds and low cloud cover. Verentsov et al. 2018 calculated the small-scale air temperature field for Moscov for 10 summer seasons with the COSMO-CLM model and found UHIs during night of larger 3 K.
Chow and Roth (2006) accessed the peak UHI 3-4 h after sunset and an intensifying effect for low wind speeds and no cloud cover.
In literature many different approaches were used to access UHIs:
Satellite observations in the thermal infrared band (e.g. Parlow et al., 2014)
In-situ measurements of air temperatures in rural and urban regions (Meier et al., 2017)
Small-scale model approaches (e.g with COSMO-CLM; Varentsov et al., 2018)
The meteoblue approach combines in-situ measurements with satellite observations and small-scale model approaches to achieve a high accuracy in the estimation of UHIs.