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<p>Accurate measurement of atmospheric pressure is fundamental to reliable meteorological data acquisition with Automatic Weather Stations (AWS) for assessment of Indian Himalayan weather. Despite advancements in sensor technology, pressure measurements remain susceptible to uncertainties stemming from instrumental limitations, calibration procedures, and environmental factors. This study conducts a rigorous uncertainty analysis of a pressure sensor widely deployed in AWS Networks of Indian Himalayas, utilizing data from eight independent calibration trials performed in snow and meteorological secondary calibration laboratory. The analysis adheres to the guidelines outlined in the Guide to the Expression of Uncertainty in Measurement (GUM), encompassing both Type A (statistical) and Type B (non-statistical) uncertainty components. Combined and expanded uncertainties were subsequently computed for each calibration trial. Results demonstrate that the sensor exhibits high repeatability, with most expanded uncertainty values falling within the ± 1.0&#xa0;hPa limit recommended by the World Meteorological Organization (WMO). These findings validate the sensor’s reliability for field-level meteorological applications and highlight the critical role of systematic calibration and uncertainty quantification in enhancing the credibility of AWS network of Indian Himalaya.</p>

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Uncertainty for Metrological Pressure Sensors of Automatic Weather Stations of Indian Himalayas

  • 期刊论文

  • 发表日期：

    2025年11月28日

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  • Neeraj Sharma,
  • Ashish Dubey,
  • Ashok Kumar,
  • Rajesh Kumar Garg,
  • Prachi Mishra

摘要

Accurate measurement of atmospheric pressure is fundamental to reliable meteorological data acquisition with Automatic Weather Stations (AWS) for assessment of Indian Himalayan weather. Despite advancements in sensor technology, pressure measurements remain susceptible to uncertainties stemming from instrumental limitations, calibration procedures, and environmental factors. This study conducts a rigorous uncertainty analysis of a pressure sensor widely deployed in AWS Networks of Indian Himalayas, utilizing data from eight independent calibration trials performed in snow and meteorological secondary calibration laboratory. The analysis adheres to the guidelines outlined in the Guide to the Expression of Uncertainty in Measurement (GUM), encompassing both Type A (statistical) and Type B (non-statistical) uncertainty components. Combined and expanded uncertainties were subsequently computed for each calibration trial. Results demonstrate that the sensor exhibits high repeatability, with most expanded uncertainty values falling within the ± 1.0 hPa limit recommended by the World Meteorological Organization (WMO). These findings validate the sensor’s reliability for field-level meteorological applications and highlight the critical role of systematic calibration and uncertainty quantification in enhancing the credibility of AWS network of Indian Himalaya.