Preprints
https://doi.org/10.5194/essd-2025-791
https://doi.org/10.5194/essd-2025-791
19 Feb 2026
 | 19 Feb 2026
Status: this preprint is currently under review for the journal ESSD.

A benchmark laboratory calibration dataset for tipping-bucket rain gauges: comparison of manual burette and automated methods

Bokjin Jang

Abstract. Reliable calibration data are essential for ensuring the accuracy and traceability of precipitation measurements obtained from tipping-bucket rain gauges (TBRGs), which are widely used in hydrological and meteorological monitoring networks. Although manual burette-based calibration remains the most commonly applied approach, its reproducibility is often limited by operator dependency and changes in discharge conditions during experiments. Automated calibration devices have been developed to address these limitations, yet publicly available benchmark datasets that allow transparent comparison between manual and automated calibration methods remain scarce.

This paper presents a benchmark laboratory calibration dataset for tipping-bucket rain gauges generated under controlled conditions using two calibration approaches: a conventional manual burette method and an automated calibration device (PRC-20AP). Calibration experiments were conducted at five target rainfall intensities (10, 20, 30, 50, and 100 mm h⁻¹), with a target total rainfall of 20 mm and 15 repeated trials for each intensity. For every trial, the dataset reports elapsed time, measured total rainfall, measured rainfall intensity, and corresponding relative errors.

In addition to raw measurements, the dataset includes intensity-wise summary statistics and a comprehensive uncertainty evaluation following the Guide to the Expression of Uncertainty in Measurement (GUM). Type A, Type B, combined, and expanded uncertainties at 95 % coverage are provided to support quantitative assessment of measurement repeatability and reliability. All data are released in machine-readable spreadsheet formats with detailed documentation of variables, units, and calculation conventions to facilitate reuse.

The dataset is publicly available through a persistent DOI and is intended to serve as a reference benchmark for laboratory calibration of tipping-bucket rain gauges. Potential applications include calibration protocol validation, uncertainty budgeting, intercomparison of calibration methods, and the development and evaluation of automated calibration technologies for precipitation measurement.

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Bokjin Jang

Status: open (until 28 Mar 2026)

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Bokjin Jang

Data sets

Laboratory calibration dataset for tipping-bucket rain gauges: manual burette vs automated device Bokjin Jang https://doi.org/10.17632/czzzth6z26.3

Bokjin Jang
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Short summary
Accurate rainfall measurement is important for weather monitoring and water resource management, but biased results can occur if sensors are not properly calibrated. This study provides a laboratory dataset describing the performance of tipping-bucket rain gauges using manual and automated calibration methods. The openly available data support comparison of calibration approaches and improvement of rainfall observations.
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