CrowdQC+—A Quality-Control for Crowdsourced Air-Temperature Observations Enabling World-Wide Urban Climate Applications

Abstract

In recent years, the collection and utilisation of crowdsourced data has gained attention in atmospheric sciences and citizen weather stations (CWS), i.e., privately-owned weather stations whose owners share their data publicly via the internet, have become increasingly popular. This is particularly the case for cities, where traditional measurement networks are sparse. Rigorous quality control (QC) of CWS data is essential prior to any application. In this study, we present the QC package “CrowdQC+,” which identifies and removes faulty air-temperature (ta) data from crowdsourced CWS data sets, i.e., data from several tens to thousands of CWS. The package is a further development of the existing package “CrowdQC.” While QC levels and functionalities of the predecessor are kept, CrowdQC+ extends it to increase QC performance, enhance applicability, and increase user-friendliness. Firstly, two new QC levels are introduced. The first implements a spatial QC that mainly addresses radiation errors, the second a temporal correction of the data regarding sensor-response time. Secondly, new functionalities aim at making the package more flexible to apply to data sets of different lengths and sizes, enabling also near-real time application. Thirdly, additional helper functions increase user-friendliness of the package. As its predecessor, CrowdQC+ does not require reference meteorological data. The performance of the new package is tested with two 1-year data sets of CWS data from hundreds of “Netatmo” CWS in the cities of Amsterdam, Netherlands, and Toulouse, France. Quality-controlled data are compared with data from networks of professionally-operated weather stations (PRWS). Results show that the new package effectively removes faulty data from both data sets, leading to lower deviations between CWS and PRWS compared to its predecessor. It is further shown that CrowdQC+ leads to robust results for CWS networks of different sizes/densities. Further development of the package could include testing the suitability of CrowdQC+ for other variables than ta, such as air pressure or specific humidity, testing it on data sets from other background climates such as tropical or desert cities, and to incorporate added filter functionalities for further improvement. Overall, CrowdQC+ could lead the way to utilise CWS data in world-wide urban climate applications.

A global map of local climate zones to support earth system modelling and urban-scale environmental science

“…Since the LCZ typology was initially designed for urban temperature studies (Stewart and Oke, 2012), typical applications focus on the UHI, usually providing the context for designing and analysing observations from urban meteorological networks (Skarbit et al, 2017;Beck et al, 2018;Chieppa et al, 2018;Verdonck et al, 2018;Yang et al, 2018;Leconte et al, 2020;Milošević et al, 2021;Zong et al, 2021), from crowd-sourced data (Fenner et al, 2017;Varentsov et al, 2021;Fenner et al, 2021;Potgieter et al, 2021;Brousse et al, 2022), or from remote sensing (Wang and Ouyang, 2017;Bechtel et al, 2019b;Eldesoky et al, 2021;Stewart et al, 2021). However, the typology has been used for other purposes (see also Lehnert et al, 2021, for European applications), such as urban heat (risk) assessment studies (Verdonck et al, 2019b;Van de Walle et al, 2022), climate-sensitive design, land use/land cover change, urban planning (policies) (Perera and Emmanuel, 2018;Aminipouri et al, 2019;Vandamme et al, 2019;Maharoof et al, 2020;Chen et al, 2021b;Zhi et al, 2021), anthropogenic heat, building energy demand and consump-tion, carbon emissions (Wu et al, 2018;Santos et al, 2020;Yang et al, 2020;Benjamin et al, 2021;Kotharkar et al, 2022), quality of life (Sapena et al, 2021), urban ventilation (Z.…”

Transformational IoT sensing for air pollution and thermal exposures

“…Responding to this need, more holistic quality-control measures for crowdsourced air temperature observations are recently developed (Fenner et al, 2021), enabling a more consistent use of citizen weather station data in worldwide applications.…”

Evaluating the quality of daily rainfall observations from third- party stations based on multiple reference data

“…Bardossy et al (2021) proposed a two-fold approach to filter out suspicious rainfall measurements from third-party stations by checking whether they appear consistent with the spatial pattern of official weather stations. Several automated QC methods have been developed for third-party observations of other climate variables, such as air temperature (Beele et al, 2022;Chakraborty et al, 2020;Fenner et al, 2021;Meier et al, 2017;Napoly et al, 2018) and wind (J. Y. Droste et al, 2020) Almost all existing methods in the literature only considered the spatial consistency between third-party stations and primary gauges ( e.g.…”