Preprints
https://doi.org/10.5194/essd-2022-113
https://doi.org/10.5194/essd-2022-113
 
02 May 2022
02 May 2022
Status: this preprint is currently under review for the journal ESSD.

Quality control and correction method for air temperature data from a citizen science weather station network in Leuven, Belgium

Eva Beele1, Maarten Reyniers2, Raf Aerts1,3,4, and Ben Somers1,5 Eva Beele et al.
  • 1Division Forest, Nature and Landscape, University of Leuven (KU Leuven), Celestijnenlaan 200E-2411, BE-3001 Leuven, Belgium
  • 2Royal Meteorological Institute of Belgium, Ringlaan 3, BE-1180 Brussels, Belgium
  • 3Risk and Health Impact Assessment, Sciensano (Belgian Institute of Health), Juliette Wytsmanstraat 14, BE-1050 Brussels, Belgium
  • 4Division Ecology, Evolution and Biodiversity Conservation, University of Leuven (KU Leuven), Kasteelpark Arenberg 31 - 2435, BE-3001 Leuven, Belgium
  • 5KU Leuven Urban Studies Institute, University of Leuven (KU Leuven), Parkstraat 45-3609, BE-3000 Leuven, Belgium

Abstract. The growing urbanization trend and increasingly frequent extreme weather events urge further monitoring and understanding of weather in cities. In order to gain information on these intra urban weather patterns, dense high quality atmospheric measurements are needed. Crowdsourced weather stations (CSW) could be a promising solution to reach such monitoring networks in a cost-efficient way. Because of their non-traditional measuring equipment and installation settings, the quality of these datasets remains however an issue of concern. This paper presents crowdsourced data from the Leuven.cool network, a citizen science network of around 100 low-cost weather stations (Fine Offset WH2600) distributed across Leuven, Belgium.  The dataset is accompanied by a newly developed station specific temperature quality control (QC) and correction procedure. The procedure consists of three levels removing implausible measurements, while also correcting for inter (in between stations) and intra (station-specific) station temperature biases by means of a random-forest approach. The evaluation of the QC is performed using data from four WH2600 stations installed next to official weather stations belonging to the Royal Meteorological Institute of Belgium (RMIB). A positive temperature bias with strong relation to the incoming solar radiation was found between the CSW data and official data. The QC method is able to reduce this bias from 0.15 ± 0.56 °C to 0.00 ± 0.22 °C. After evaluation, the QC method is applied to the data of the Leuven.cool network, making it a very suitable data set to study in detail local weather phenomena such as the urban heat island (UHI) effect.

Eva Beele et al.

Status: open (until 27 Jun 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Eva Beele et al.

Data sets

Replication Data for: Quality control and correction method for air temperature data from a citizen science weather station network in Leuven, Belgium Beele Eva, Reyniers Maarten, Aerts Raf, Somers Ben https://rdr.kuleuven.be/dataset.xhtml?persistentId=doi:10.48804/SSRN3F

Eva Beele et al.

Viewed

Total article views: 213 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
167 39 7 213 2 3
  • HTML: 167
  • PDF: 39
  • XML: 7
  • Total: 213
  • BibTeX: 2
  • EndNote: 3
Views and downloads (calculated since 02 May 2022)
Cumulative views and downloads (calculated since 02 May 2022)

Viewed (geographical distribution)

Total article views: 190 (including HTML, PDF, and XML) Thereof 190 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 26 May 2022
Download
Short summary
This paper presents crowdsourced data from the Leuven.cool network, a citizen science network of around 100 low-cost weather stations distributed across Leuven, Belgium. The temperature data has undergone a quality control (QC) and correction procedure. The procedure consists of three levels removing implausible measurements, while also correcting for in-between-station and station-specific temperature biases.