the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Calibrating low-cost rain gauge sensors for their applications in IoT infrastructures to densify environmental monitoring networks
Robert Krüger
Pierre Karrasch
Anette Eltner
Abstract. Environmental observations are crucial for understanding the state of the environment. However, current observation networks are limited in spatial and temporal resolution due to high costs. For many applications, data acquisition with a higher resolution would be desirable. Recently, Internet of Things (IoT) -enabled low-cost sensor systems offer a solution to this problem. While low-cost sensors may have lower quality than sensors in official measuring networks, they can still provide valuable data. This study describes the requirements for such a low-cost sensor system, presents two implementations, and evaluates the quality of the factory calibration for a widely used low-cost precipitation sensor. Here, twenty sensors have been tested for an 8-month period against three reference instruments at the meteorological site of the TU Dresden. Further, the factory calibration of 66 rain gauges has been evaluated in the lab. Results show that the used sensor falls short for the desired out-of-the-box use case. Nevertheless, it could be shown that the accuracy could be improved by further calibration.
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Robert Krüger et al.
Status: open (until 30 Sep 2023)
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RC1: 'Comment on gi-2023-7', Rolf Hut, 15 Jun 2023
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Review of “Calibrating low-cost rain gauge sensors for their applications in IoT infrastructure to densify environmental monitoring network” by Krüger et al
Review by Rolf Hut
The authors calibrated a collection of off the shelf low-cost rain gauges to test if they are usable in scientific applications with the factory calibration. Given the amount of projects that aim to use Personal Weather Stations (PWS) to supplement professional networks, this is a valuable addition to the literature. I do have, however, some suggestions to in my opinion improve the paper (and its usability by the scientific community) before publication.
Overall comments
- The manuscript as written hinges on two thoughts: on the one side a lab and field calibration of low-cost rain gauges and on the other side an overview of IoT hardware and cost needed to use low cost sensors in general and rain gauges in particular. This last part (IoT hardware) is worked out in far less detail compared to the first part (rain gauge calibration). In literature a large collection of articles reviewing state of the art development boards (including Arduino and Raspberry Pi) for use in environmental sensing in general and weather stations, is available. I would suggest that the authors focus on the rain gauge calibration and remove, or move ta an appendix, paragraphs 2.1 until and including 2.2.4. In the main text the authors can cite relevant literature on IoT hardware reviews. (a quick search on google scholar already resulted in these DOIs, there is much more: https://doi.org/10.3390/ijerph17113995, https://doi.org/10.1016/j.cosrev.2021.100364, https://doi.org/10.1016/j.procs.2014.07.059, 9734/AJRCOS/2021/v9i130215)
- In calibrations of rain gauges the crucial question is always: “what do we use as ‘the truth’ and the authors have three reference devices available. They choose to use the Hellman gauge as reference without further justification. I would ask the authors to substantiate why the Hellman, compared to the other devices, should be considered “reference” (or “thruth”).
- Continuing on point 2: the authors only report on the difference between the different gauges, both within the groups of low-cost gauges and between the low cost gauges and the reference gauges. However, they do not quantify if these are significant in the light of uncertainties, either inherent in their way of measuring, or inherent in the nature of rainfall. Given the large amount of low-cost rain gauges they use, it should be straightforward to indicate if the values of the the reference gauges are significantly outside the distribution of low-cost gauges. If the authors have access to a long time series from the reference devices (which I assume they have), they could use triple co-location to estimate the uncertainties in the three reference devices. This would than allow for a two-way comparison between the reference devices and the low-cost gauges. There is a wealth of literature on (how to do) comparisons between raingauges, including the statistics involved. I suggest starting at Lanza 2009 (https://doi.org/10.1016/j.atmosres.2009.06.012)
- In the lab calibration it is extremely important that the rain gauges are placed perfectly horizontal. I assume the authors made sure of this. I would suggest to add a few sentences on how this was done. Furthermore, it is important to know if all raingauges were oriented exactly the same direction on the table. If the table was even slightly tilting, having all rain gauges in the same orientation would result in a bias towards a certain direction and could explain the left-right difference observed?
- The analyses done within the discussion is, in my point, central to the manuscript. I would suggest to move the results of the comparison of the field and lab experiment to the result, explain in the methods which (statistical) methods are used to compare the two datasets and in the discussion only reflect on the result, not present new ones.
Specific comments
- All figures need more detail in their captions to understand what is shown.
- In figure 1 I would add a vertical (red?) line at 0.20 mm to indicate where the factory calibration is.
- Figure 6 could use the Hellman data as crosses or points. Especially on the one hourly data it is interesting to look at the uncertainties of the three reference devices (see above).
Overall I think this is a highly relevant paper given the focus on citizen science projects to use Personal Weather Stations to supplement professional networks. With the above suggestions implemented I would be happy to recommend publication in GI.
Rolf Hut
Citation: https://doi.org/10.5194/gi-2023-7-RC1
Robert Krüger et al.
Robert Krüger et al.
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