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Meteorological measurements require significant time, human effort, and resources, including instrumental and operational costs. The resulting meteorological database must have a reliable consistency, which must be designed according to the user’s needs. The whole measurement process, from planning and operational phases to the development of the complete dataset, must be accompanied by continuous quality controlQuality control (QC) and quality assuranceQuality assurance (QA) activities. In this chapter, we present the necessary steps and best practices for the QA/QC of micrometeorological data. These practices are vital to correctly identifying, addressing, and correcting errors that may occur during data collection, processing, and analysis.

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Quality Control and Recovery of Meteorological Data

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Mina Petrić,
  • Branislava Lalic,
  • Peter Domonkos,
  • Tamás Weidinger,
  • Thomas Vergauwen,
  • Ivan Koči,
  • Thomas Foken

摘要

Meteorological measurements require significant time, human effort, and resources, including instrumental and operational costs. The resulting meteorological database must have a reliable consistency, which must be designed according to the user’s needs. The whole measurement process, from planning and operational phases to the development of the complete dataset, must be accompanied by continuous quality controlQuality control (QC) and quality assuranceQuality assurance (QA) activities. In this chapter, we present the necessary steps and best practices for the QA/QC of micrometeorological data. These practices are vital to correctly identifying, addressing, and correcting errors that may occur during data collection, processing, and analysis.