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<p>In situ measured Arctic weather data access has traditionally been complicated by harsh conditions in the region and publicly available data being scattered in different databases and formats. To address this, we collected publicly available in situ measurements of 36 ground and surface climatic variables from 13 different data sources, focusing on the period 1990-2023. The dataset, which consists of 719 unique locations in total with varying data coverage in time and variables, is available in two versions: In the first ’raw’ version, data was restructured and reformatted from each original source into a common format, but was not tested for quality. In the second, quality checked version, the dataset has additionally been run through a five-module quality check involving 1) removing common error values, 2) evaluating physically impossible values, 3) outlier-detection and evaluation, 4) unit conversions and 5) evaluation of likely instrument and/or calibration artifacts. The code for import, normalization and quality check with optional modules is made available in addition to the data.</p>

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A pan-Arctic terrestrial in situ weather dataset from 1990-2023 collected from publicly available data sources

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月16日

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  • Laura Helene Rasmussen,
  • Bo Markussen,
  • Susanne Ditlevsen

摘要

In situ measured Arctic weather data access has traditionally been complicated by harsh conditions in the region and publicly available data being scattered in different databases and formats. To address this, we collected publicly available in situ measurements of 36 ground and surface climatic variables from 13 different data sources, focusing on the period 1990-2023. The dataset, which consists of 719 unique locations in total with varying data coverage in time and variables, is available in two versions: In the first ’raw’ version, data was restructured and reformatted from each original source into a common format, but was not tested for quality. In the second, quality checked version, the dataset has additionally been run through a five-module quality check involving 1) removing common error values, 2) evaluating physically impossible values, 3) outlier-detection and evaluation, 4) unit conversions and 5) evaluation of likely instrument and/or calibration artifacts. The code for import, normalization and quality check with optional modules is made available in addition to the data.