the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 19 Feb 2026
A benchmark laboratory calibration dataset for tipping-bucket rain gauges: comparison of manual burette and automated methods
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.
- Preprint
(620 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 09 May 2026)
-
RC1: 'Comment on essd-2025-791', Anonymous Referee #1, 24 Mar 2026
reply
-
AC1: 'Reply on RC1', Bokjin Jang, 03 Apr 2026
reply
-
The method is focused on a benchmark making procedure, using a certain calibrating device (PRC-20AP), planned and installed by the author and his colleagues (https://doi.org/10.1016/j.flowmeasinst.2025.103063). The paper proposes this tool to be a widely accepted benchmark for other calibration techniques, so offering a quasy standard for the data quality, based on the development of Tippinng Bucket Gauge based rainfall intensity and rain depth measurement, the related WMO proposals etc.
Regarding the novel PRC-20AP device, the paper presents news, if this tool will be accepted and applied extendedly. Since there is a gap in the field of high accuracy calibration, the dataset of the calibration performance of the device can have potential in use, so on my opinion, the attached data can have potential as well.
The article is appropriate to support the publication of data set.
Response:
Thank you for your careful evaluation of our manuscript and dataset, and for your positive comments regarding their potential usefulness and suitability for data publication.
The primary objective of this study is not to introduce a new calibration device itself, but to provide a benchmark-oriented dataset and evaluation framework for assessing the calibration performance of tipping-bucket rain gauges under controlled rainfall intensity conditions. The PRC-20AP system serves as the experimental basis for generating the dataset.
As noted by the reviewer, the broader applicability of this approach depends on its future adoption. In this regard, we clarify that the dataset is not intended to serve as a formal standard, but rather as a benchmark reference to support comparison among different calibration methods. This clarification has been incorporated into the revised manuscript.
The dataset is constructed based on repeated experiments and uncertainty analysis, and is designed to support various applications, including comparative evaluation of calibration methods, validation of new calibration systems, and analysis of repeatability and uncertainty.
Thank you again for your valuable comments.
-
The dataset’s files were available at the given site, and opened correctly. The data set contains simple excel worksheets without cell interconnections, formulae. The worksheets seem to be adequate for the comparison of an actual measurement method’s statistics to the proposed benchmark values shown by the tables. Maybe a prepared worksheet with automatic calculation would be helpful for the users, referring to the potential applications, as it was mentioned in Chapter 6. In its actual form I find it a little bit poor.
There are error estimations and separation of sources of errors in the article. The error calculation follows the principles of the Guide to the Expression of Uncertainty in Measurement (GUM), what principle is the core of the several related national standards. The article takes into considerations of these standards.
The data set’s unique as a reference calibration-benchmark’s parameters, it is complete, clear, if the data set would be used as a comparison for other calibration processes or directly, in calibration, it can be useful.
Response:
Thank you for your detailed and constructive evaluation of the dataset. We appreciate your positive comments regarding its accessibility, completeness, and potential usefulness, as well as your recognition of the uncertainty analysis based on GUM principles.
We agree with the reviewer that the original dataset had limitations in terms of usability, particularly due to the absence of embedded calculations and automated analysis tools. In response, the dataset has been substantially revised to improve its usability and practical applicability, as summarized below:
(1) Introduction of an interactive analysis interface
An integrated Main Analysis worksheet has been introduced, allowing users to directly select rainfall intensity conditions and obtain key performance metrics, including total rainfall, rainfall intensity, relative error, repeatability, and uncertainty, as well as comparisons between manual and automated calibration methods.(2) Implementation of formula-based calculations
The summary worksheets (Summary_Manual and Summary_Automated) have been reconstructed using formula-based calculations. Mean values, standard deviation, and uncertainty components are now automatically derived from the raw data, ensuring consistency and full traceability.(3) Addition of structured comparison worksheets
Dedicated comparison worksheets (Total_Rainfall_Comparison and Rainfall_Intensity_Comparison) have been added to present side-by-side evaluation results and improvement metrics between calibration methods.(4) Integration into a unified dataset structure
Previously separated data files have been reorganized into a single integrated spreadsheet file, combining raw data, summary statistics, analysis tools, and documentation within a coherent structure.(5) Enhancement of usability and documentation
A comprehensive README worksheet and descriptive annotations have been added to each worksheet, providing clear explanations of data structure, variable definitions, calculation procedures, and usage instructions to support direct application of the dataset.These improvements have been reflected in the revised manuscript. In particular, the updated dataset structure and workflow are described in Section 2.4 (Data files and structure) and Section 2.5 (Variables and units).
In addition, Section 5 (Data availability and access) has been revised to clearly state that the improved dataset has been updated in the Mendeley Data repository. The updated dataset is provided as a single integrated spreadsheet file that enables users to directly access, analyze, and compare calibration results without additional preprocessing.
Regarding uncertainty estimation, the dataset consistently follows the Guide to the Expression of Uncertainty in Measurement (GUM), including the separation of Type A and Type B components and the calculation of combined and expanded uncertainty.
With these revisions, we believe that the dataset has been significantly improved in terms of usability, transparency, and practical applicability for calibration comparison and benchmarking purposes.
Thank you again for your valuable comments.
-
Investigating the text, I could not find traces of inconsistencies, implausible assertions or any other issues. The data set in this context is so simple and so short, that the test of the data was basically the repetition of the calculation. I found no issues in the table.
Response:
Thank you for your careful evaluation of the manuscript and dataset, and for confirming that no inconsistencies or issues were identified.
As noted by the reviewer, the dataset has a relatively simple structure. This simplicity is intentional, as the dataset is designed to ensure transparency and reproducibility of the calculation process. All statistical results are derived from repeated experimental data, allowing users to directly verify the results through straightforward calculations.
In addition, the dataset is structured to maintain clear traceability between raw data and derived statistics, enabling users to fully understand and validate the data processing workflow.
Thank you again for your valuable comments.
-
The data set is usable but I do not find it user-friend, because for the use of the table the cells should be re-formulated, and for this, the user must work a lot. A proposal should be mention, the rounding of the statistical parameter would be better for four decimal digits. On my opinion, if one decides to make a benchmark for the TBR devices, finding the 15 repetition and the selected rainfall intensities enough (it’s OK). I think that the completion of the excel files with an input worksheet, and some hidden calculation sheets, and an output one where the results are compared automatically to the benchmark values would be useful. The metadata could have been a little bit more informative.
The length of the article is appropriate, well structured, and clear enough for understanding. I found the language of the text consistent and precise, but I am not a native speaker, so I am not the best person to judge this question. The authors show the minimum of formulas, the variables are listed correctly, generally, the representation of quantities and their units is clear and correct. The table’s outfit, arrangement is clear too. Regarding the figures, for figures 1b and 2b, 3a, 3b, wider column would be more easily readable.
Finally: By reading the article and downloading the data set, would you be able to understand and (re-)use the data set in the future?
I think, I could.
Response:
Thank you for your detailed and constructive evaluation of the dataset, including its usability, metadata, and overall presentation quality. We also appreciate your positive comments regarding the structure, clarity, and readability of the manuscript.
In response to the reviewer’s comments, the following revisions have been made.
(1) Improvement of dataset usability
To address the usability limitations of the original dataset, an interactive Main Analysis worksheet has been introduced, together with formula-based summary worksheets and dedicated comparison worksheets. In addition, the dataset has been reorganized into a single integrated spreadsheet file, enabling direct use without additional preprocessing.(2) Adjustment of numerical precision
The numerical representation of statistical variables has been revised to avoid over-precision while maintaining practical relevance. Most statistical values (e.g., rainfall and rainfall intensity) are presented with two decimal places, while time-related variables are reported at higher precision (up to four decimal places) to maintain calculation accuracy.(3) Enhancement of metadata and documentation
The README worksheet has been expanded, and descriptive annotations have been added to each worksheet to clearly explain data structure, variable definitions, calculation procedures, and usage instructions from a user-oriented perspective.(4) Improvement of figure readability
The readability of Figs. 1, 2, and 3 has been improved by adjusting their layout and size for clearer visualization.These revisions have been reflected in both the dataset and the revised manuscript. In particular, improvements related to dataset structure and usability are described in Section 2.4 (Data files and structure) and Section 5 (Data availability and access), while variable definitions and numerical representation are clarified in Section 2.5 (Variables and units). In addition, the improvements in figure readability have been applied to Figs. 1–3.
Thank you again for your valuable comments.
- The revised manuscript and updated dataset will be provided in the revision stage.
Citation: https://doi.org/10.5194/essd-2025-791-AC1 -
-
AC1: 'Reply on RC1', Bokjin Jang, 03 Apr 2026
reply
-
AC2: 'Comment on essd-2025-791 (Suggested reviewers)', Bokjin Jang, 03 Apr 2026
reply
In response to the editor’s request, we would like to suggest the following potential reviewers for our manuscript:
1. Prof. Dongsu Kim (dongsu-kim@dankook.ac.kr)
Dankook University, Republic of Korea
Expertise: hydrology, hydraulic measurements, and uncertainty analysis2. Prof. Jooheon Lee (leejh@joongbu.ac.kr)
Joongbu University, Republic of Korea
Expertise: hydrology and rainfall-related studiesI confirm that there are no conflicts of interest with the suggested reviewers.
Thank you for your consideration.
Citation: https://doi.org/10.5194/essd-2025-791-AC2
Data sets
Laboratory calibration dataset for tipping-bucket rain gauges: manual burette vs automated device Bokjin Jang https://doi.org/10.17632/czzzth6z26.3
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 145 | 49 | 20 | 214 | 21 | 27 |
- HTML: 145
- PDF: 49
- XML: 20
- Total: 214
- BibTeX: 21
- EndNote: 27
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
The method is focused on a benchmark making procedure, using a certain calibrating device (PRC-20AP), planned and installed by the author and his colleagues (https://doi.org/10.1016/j.flowmeasinst.2025.103063). The paper proposes this tool to be a widely accepted benchmark for other calibration techniques, so offering a quasy standard for the data quality, based on the development of Tippinng Bucket Gauge based rainfall intensity and rain depth measurement, the related WMO proposals etc.
Regarding the novel PRC-20AP device, the paper presents news, if this tool will be accepted and applied extendedly. Since there is a gap in the field of high accuracy calibration, the dataset of the calibration performance of the device can have potential in use, so on my opinion, the attached data can have potential as well.
The article is appropriate to support the publication of data set.
The dataset’s files were available at the given site, and opened correctly. The data set contains simple excel worksheets without cell interconnections, formulae. The worksheets seem to be adequate for the comparison of an actual measurement method’s statistics to the proposed benchmark values shown by the tables. Maybe a prepared worksheet with automatic calculation would be helpful for the users, referring to the potential applications, as it was mentioned in Chapter 6. In its actual form I find it a little bit poor.
There are error estimations and separation of sources of errors in the article. The error calculation follows the principles of the Guide to the Expression of Uncertainty in Measurement (GUM), what principle is the core of the several related national standards. The article takes into considerations of these standards.
The data set’s unique as a reference calibration-benchmark’s parameters, it is complete, clear, if the data set would be used as a comparison for other calibration processes or directly, in calibration, it can be useful.
Investigating the text, I could not find traces of inconsistencies, implausible assertions or any other issues. The data set in this context is so simple and so short, that the test of the data was basically the repetition of the calculation. I found no issues in the table.
The data set is usable but I do not find it user-friend, because for the use of the table the cells should be re-formulated, and for this, the user must work a lot. A proposal should be mention, the rounding of the statistical parameter would be better for four decimal digits. On my opinion, if one decides to make a benchmark for the TBR devices, finding the 15 repetition and the selected rainfall intensities enough (it’s OK). I think that the completion of the excel files with an input worksheet, and some hidden calculation sheets, and an output one where the results are compared automatically to the benchmark values would be useful. The metadata could have been a little bit more informative.
The length of the article is appropriate, well structured, and clear enough for understanding. I found the language of the text consistent and precise, but I am not a native speaker, so I am not the best person to judge this question. The authors show the minimum of formulas, the variables are listed correctly, generally, the representation of quantities and their units is clear and correct. The table’s outfit, arrangement is clear too. Regarding the figures, for figures 1b and 2b, 3a, 3b, wider column would be more easily readable.
Finally: By reading the article and downloading the data set, would you be able to understand and (re-)use the data set in the future?
I think, I could.