Preprints
https://doi.org/10.5194/hess-2023-195
https://doi.org/10.5194/hess-2023-195
31 Aug 2023
 | 31 Aug 2023
Status: this preprint is currently under review for the journal HESS.

Technical note: Overview and comparison of three quality control algorithms for rainfall data from personal weather stations

Abbas El Hachem, Jochen Seidel, Tess O'Hara, Roberto Villalobos Herrera, Aart Overeem, Remko Uijlenhoet, András Bárdossy, and Lotte de Vos

Abstract. The number of rainfall observations from personal weather stations (PWSs) has increased significantly over the past years; however, there are persistent questions about data quality. In this paper, an examination and comparison of three quality control algorithms (PWSQC, PWS-pyQC, and GSDR-QC) designed for the quality control of rainfall data is presented. The focus was on a series of rainfall events occurring in the Amsterdam area between May 2017–May 2018. Quality issues observed include faulty zeros i.e., the underreporting of rainfall, significant gaps in the dataset, and systematic bias often caused by incorrect setup and installation of the PWS. The analysis shows that all three algorithms improve PWS data quality when cross-referenced against rain radar. The considered algorithms have different strengths and weaknesses depending on PWS and official data availability, making it inadvisable to recommend one over another without carefully considering the specific setting. The need for further objective quantitative benchmarking of QC algorithms requiring freely available test datasets representing a range of environments, gauge densities, and weather patterns is highlighted.

Abbas El Hachem, Jochen Seidel, Tess O'Hara, Roberto Villalobos Herrera, Aart Overeem, Remko Uijlenhoet, András Bárdossy, and Lotte de Vos

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2023-195', Anonymous Referee #1, 24 Sep 2023
    • AC1: 'Reply on RC1', Abbas El Hachem, 11 Dec 2023
  • RC2: 'Comment on hess-2023-195', Anonymous Referee #2, 13 Nov 2023
    • AC2: 'Reply on RC2', Abbas El Hachem, 11 Dec 2023
Abbas El Hachem, Jochen Seidel, Tess O'Hara, Roberto Villalobos Herrera, Aart Overeem, Remko Uijlenhoet, András Bárdossy, and Lotte de Vos
Abbas El Hachem, Jochen Seidel, Tess O'Hara, Roberto Villalobos Herrera, Aart Overeem, Remko Uijlenhoet, András Bárdossy, and Lotte de Vos

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Short summary
This study presents an overview of open-source quality control (QC) algorithms for rainfall data from personal weather stations (PWS). The methodology and useability for every QG algorithm are described. All QC algorithms were applied in a case study using a PWS data from the Amsterdam region in the Netherlands. The results highlight the necessity for adequate data filtering and show the advantages and disadvantages of each QC algorithm.