A New Method for Correcting Urbanization-Induced Bias in Surface Air Temperature Observations: Insights From Comparative Site-Relocation Data

Abstract

The effect of urbanization on surface air temperature (SAT) is one of the most important systematic biases in SAT series of urban stations. Correcting this so-called urbanization bias has the potential to provide accurate basic data for long-term climate change monitoring and research. In the western region of the Yangtze River Delta, 42 meteorological stations with site-relocation history from 2009 to 2018 were selected to analyze the statistical characteristics of the differences in comparative site-relocation daily average SAT. The annual average differences in comparative site-relocation SAT series between the old and the new stations (SATDON) were used to characterize the impact of urbanization bias on the air temperature observation series. Using remote sensing technology, spatial datasets of land-use, landscape, and geometric parameters of the underlying surface in the 5-km buffer zone around the station were established as the observed environmental factors of the site, and the differences in these observed environmental factors (DOEFs) between the old and the new stations were calculated to indicate the change induced by urbanization. Next, multiple linear regression models of SATDON and DOEFs were constructed, showing that the error range of the model for simulated SATDON was 3.66–18.21%, and the average error was 10.09%. Finally, this new correction method (NCM) and conventional correction method (CCM) were applied to the correction of the urbanization bias of SAT series at Hefei station. After comparison, it is found that the NCM could reveal clear contributions of the rapid and slow stages of the urbanization process and resultant environmental changes around the stations to the observed SAT. In summary, the NCM based on remote sensing technology can more reasonably and effectively correct the urbanization bias caused by local human activities, as well as reduce the error caused by the selection of reference stations via the conventional correction method.

Synergistic Influence of Local Climate Zones and Wind Speeds on the Urban Heat Island and Heat Waves in the Megacity of Beijing, China

“…Therefore, an important selection criterion was applied in that, during the study period (2014)(2015)(2016)(2017)(2018)(2019)(2020), urban stations had to be within the urban center (considered here as within or near the Fifth Ring Road), and rural stations had to be far away from major construction areas (Figure 1; Table 1). The selection of urban and rural stations refers to the method of previous studies (Yang et al, 2013;Shi et al, 2015;Shi et al, 2021). The method described by Xu et al (2013) was used for quality control and homogenization of the daily meteorological data.…”

Dual Effects of Synoptic Weather Patterns and Urbanization on Summer Diurnal Temperature Range in an Urban Agglomeration of East China

“…Since the measurement of surface air temperature is taken at the height of 2m, according to previous studies (Cai, 2008;Shi et al, 2015;Shi et al, 2021;Yang et al, 2020), the maximum impact of anthropogenic heat on meteorological observations around the station usually does not exceed 5 km under advection and turbulence transport conditions. Thus, we chose 5 km as the radius of the buffer zone to capture the effects of urbanization on air temperature.…”

Influence of Changes in Meteorological Observational Environment on Urbanization Bias in Surface Air Temperature: A Review

“…Further, we take the Hefei station in eastern China as an example to discuss the changes of MOE from 1979 to 2018. Figure 2 gives the distribution of land use within the 20-km buffer zone around the center of Hefei (Shi et al, 2021). In the last 40 years, the built-up area within 20 km of Hefei in 2018 is about 20 times larger than that in 1979, leading to the continuous deterioration of MOE.…”

“…However, these methods are not suitable for large-scale use as a result of their high impact on natural conditions and social activities and the relatively high cost of construction and maintenance. The use of satellite data to study the representation of the MOE has become more common with the rapid development of remote sensing technology (Ren et al, 2010b;Ren and Ren, 2011;Yang et al, 2013;Li Y. et al, 2015;Shi et al, 2015Shi et al, , 2021. Satellite remote sensing data cover a wide area and are periodic; they can therefore provide multi-temporal and large-scale information for the study of dynamic changes in the MOE.…”

“…The impact of changes in the local MOE related to urbanization bias on the SAT should therefore be quantified in large-scale studies of climate change (Brohan et al, 2006;Zhang, 2009;Soon et al, 2018). The spatial morphological characteristics of the MOE around a weather station are important in driving urbanization bias in the SAT (Erell et al, 2011;Shi et al, 2021). Previous studies have attempted to quantify the contribution of this urbanization bias to regional warming by using the results from surface stations (Jones et al, 2007;Menne et al, 2010;Ren et al, 2010a;Fall et al, 2011;Stewart and Oke, 2012;Estoque et al, 2017;Scarano and Mancini, 2017), although this is still controversial as a result of uncertainties in quantifying the impact of the MOE on urbanization bias.…”