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

Research output: Contribution to journalArticleAcademicpeer-review

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
LanguageEnglish
Pages8820-8829
Number of pages10
JournalGeophysical Research Letters
Volume46
Issue number15
Early online date22 Jul 2019
DOIs
Publication statusPublished - 16 Aug 2019

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weather stations
weather station
quality control
weather
rainfall
monitoring
rain gages
in situ measurement
temporal resolution
correlation coefficients
platforms
stations
modules
spatial resolution
methodology
targeting
gauge
high resolution
coefficients

Keywords

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

Cite this

@article{d1e3dc060e4546ecbf6c940558a160d1,
title = "Quality Control for Crowdsourced Personal Weather Stations to Enable Operational Rainfall Monitoring",
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",
keywords = "personal weather station, rainfall, quality control, filter, urban, rain gauge",
author = "{de Vos}, Lotte and Hidde Leijnse and Aart Overeem and Remko Uijlenhoet",
year = "2019",
month = "8",
day = "16",
doi = "10.1029/2019GL083731",
language = "English",
volume = "46",
pages = "8820--8829",
journal = "Geophysical Research Letters",
issn = "0094-8276",
publisher = "American Geophysical Union",
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}

Quality Control for Crowdsourced Personal Weather Stations to Enable Operational Rainfall Monitoring. / de Vos, Lotte; Leijnse, Hidde; Overeem, Aart; Uijlenhoet, Remko.

In: Geophysical Research Letters, Vol. 46, No. 15, 16.08.2019, p. 8820-8829.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Quality Control for Crowdsourced Personal Weather Stations to Enable Operational Rainfall Monitoring

AU - de Vos, Lotte

AU - Leijnse, Hidde

AU - Overeem, Aart

AU - Uijlenhoet, Remko

PY - 2019/8/16

Y1 - 2019/8/16

N2 - 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

AB - 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

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KW - quality control

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KW - rain gauge

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