Quality Control for Crowdsourced Personal Weather Stations to Enable Operational Rainfall Monitoring

Lotte de Vos*, Hidde Leijnse, Aart Overeem, Remko Uijlenhoet

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)

Abstract

Automatic personal weather stations owned and maintained by weather enthusiasts provide spatially dense in situ measurements that are often collected and visualized in real time on online weather platforms. While the spatial and temporal resolution of this data source is high, its rainfall observations are prone to typical errors, currently preventing its large‐scale, real‐time application. This study proposes a quality control methodology consisting of four modules targeting these errors, applicable in real time without requiring auxiliary measurements. The quality control improves the overall accuracy of a year of hourly rainfall depths in Amsterdam to a bias of −11.3% (0.2% when a proxy for overall rainfall underestimation by personal weather stations is used), a Pearson correlation coefficient of 0.82, and a coefficient of variation of 2.70, while maintaining 88% of the original data set. Application on a national scale (average 1 station per ∼10 km2) yields high‐resolution nationwide rainfall maps, hence showing the great potential of personal weather stations for complementing existing often sparse traditional rain gauge networks
Original languageEnglish
Pages (from-to)8820-8829
Number of pages10
JournalGeophysical Research Letters
Volume46
Issue number15
Early online date22 Jul 2019
DOIs
Publication statusPublished - 16 Aug 2019

Keywords

  • personal weather station
  • rainfall
  • quality control
  • filter
  • urban
  • rain gauge

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