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On the Importance of Data Quality Assessment of Crowdsourced Meteorological Data

Author

Listed:
  • Milena Vuckovic

    (VRVis Zentrum für Virtual Reality und Visualisierung Forschungs-GmbH, 1220 Vienna, Austria)

  • Johanna Schmidt

    (VRVis Zentrum für Virtual Reality und Visualisierung Forschungs-GmbH, 1220 Vienna, Austria)

Abstract

This study reflects on the quality aspects of urban meteorological time series obtained by crowdsourcing, specifically the air temperature and humidity data originating from personal weather stations (PWS) and the related implications for empirical and numerical research. A number of year-long hourly-based PWS data were obtained and compared to the data from the authoritative weather stations for selected areas in the city of Vienna, Austria. The results revealed a substantial amount of erroneous occurrences, ranging from singular and sequential data gaps to prevalent faulty signals in the recorded PWS data. These erroneous signals were more prominent in humidity time series data. If not treated correctly, such datasets may be a source of substantial errors that may drive inaccurate inferences from the modelling results and could further critically misinform future mitigation measures aimed at alleviating pressures related to climate change and urbanization.

Suggested Citation

  • Milena Vuckovic & Johanna Schmidt, 2023. "On the Importance of Data Quality Assessment of Crowdsourced Meteorological Data," Sustainability, MDPI, vol. 15(8), pages 1-18, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6941-:d:1128330
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    References listed on IDEAS

    as
    1. Milena Vuckovic & Kristina Kiesel & Ardeshir Mahdavi, 2017. "The Extent and Implications of the Microclimatic Conditions in the Urban Environment: A Vienna Case Study," Sustainability, MDPI, vol. 9(2), pages 1-16, January.
    2. Milena Vuckovic & Johanna Schmidt, 2021. "Visual Analytics for Climate Change Detection in Meteorological Time-Series," Forecasting, MDPI, vol. 3(2), pages 1-14, April.
    3. Milena Vuckovic & Johanna Schmidt, 2020. "Visual Analytics Approach to Comprehensive Meteorological Time-Series Analysis," Data, MDPI, vol. 5(4), pages 1-16, September.
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