the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 30 Jun 2025
Have you ever seen the rain? Observing a record convective rainfall with national and local monitoring networks and opportunistic sensors
Abstract. Short-duration extreme rainfall can cause severe impacts in built environments and flood mitigation measures require high-resolution rainfall data to be effective. It is a particular challenge to observe convective storms which are expected to intensify with climate change. However, rainfall monitoring networks operated by national meteorological and hydrological services generally have limited ability to observe rainfall at sub-hourly and sub-kilometre scale. This paper investigates the capability of second- and third-party rainfall sensors to observe a highly localized convective storm that hit southwestern Sweden in August 2022. Specifically, we compared the observations from professional weather stations, C-band radar, X-band radar, Commercial Microwave Links and Personal Weather Stations to get a full impression of the sensors’ strengths and weaknesses in the context of convective storms. The results suggest that second- and third-party networks can contribute with important information on short-duration extreme rainfall to national weather services. The second-party network assisted in quantifying the magnitude and spatial variability of the event with high precision. The third-party network could contribute to the understanding of the duration and spatial distribution of the storm, but underestimated the magnitude compared with the reference sensors.
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RC1: 'Comment on egusphere-2025-2820', Anonymous Referee #1, 22 Jul 2025
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AC1: 'Reply on RC1', Louise Petersson Wårdh, 21 Aug 2025
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We thank the reviewer for the helpful comments that will support clarifications and further enhancement of the manuscript. Please find answers to the comments below.
Line 161: Thank you for pointing this out. The text is referring to the C-band radar located 6 km South of the study area, marked with a white dot in Map 1 in Figure 1. The radar location is unfortunately missing from Map 2 in Figure 1 and will be added.
Line 194: Overshooting in this context means that the radar beam shoots above the precipitation cloud and hence does not record it. It is a common source of error in radar data. We will add some standard literature as reference for further reading on the topic. The purpose of this section is to see if there is risk of overshooting by the X-band radar in the area of interest. The same analysis is done in lines 168 – 171 for the C-band radar. As the radar beam we are using travels on 200-300 m height at the area of interest for CWR and 300-1200 m for XWR, and convective precipitation in the summer months in Sweden typically originate from much higher altitudes, the risk of overshooting is very small. We will add sources to support these numbers.
Line 227-229: The intention of this sentence is to add clarity, but if it only confuses things, it is maybe better removed. For sub-hourly rainfall data, the correlation between time series recorded by nearby sensors can be low even if they record similar total rainfall depths (for example Spearman correlation 0.18 between XWR and the municipal gauge, see Results). As convective rainfall is highly variable in space and time the observations per time step can be very different at nearby locations. If you use correlation as metric this will indicate poor performance, when, in fact, the sensors simply may experience different rainfall intensities even if they are closely located. By shifting the time series in time, you can account for the fact that a certain rainfall intensity may be observed by the radar before it reaches a rain gauge on the ground, for example.
Line 272 and 274:
Equation 6: Reflectivity data from radars are stored and distributed as integers between 0 and 255 to enable smaller storage size, following European standards (Michelson et al., 2014). To convert these integers back to reflectivity (dBZ) you apply the coefficients G (gain) and offset.
Equation 7: a and b are well-established empirical constants that Marshall and Palmer (1948) found when establishing the relationship between size distribution of raindrops (DSD) in a radar pulse volume to the rainfall rate.
Line 392: This is not a very general feature for Sweden, but the statement referred to the specific study area. The pattern can be seen in maps of 30-year mean annual precipitation here: https://www.smhi.se/klimat/klimatet-da-och-nu/normalkartor/normal/arsnederbord-normal. We will clarify this part of the text in the revised manuscript.
Figure 4: Thank you for pointing this out. The line types will be better differentiated in the next version.
Line 494: Given the sudden constant records of rainfall rate observed in the CML time series, which is unexpected and clearly not representative for the actual rainfall rate, it was excluded from the analysis.
Figure 10: Thank you for pointing this out. It is correct that, for example, panel a is 17:22-17:23 and panel b is 17:23-17:24. This will be clarified in the next version.
Figure 11a: Thank you for pointing this out. A linear trend is expected because, ideally, the two sensors (CML and XWR at the CML location) should record the same or very similar rainfall intensities. R2 will be reported in the next version.
Figure 12: Thank you for this valid and helpful comment, it will be added in the next version.
About the Discussion section: We were indeed discussing whether to keep the results and discussion sections together or apart before arriving at this solution. It is understood that the discussion, if kept separate, will need more cross-references to the text. We will await the second review to decide which approach to choose.
Citation: https://doi.org/10.5194/egusphere-2025-2820-AC1
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AC1: 'Reply on RC1', Louise Petersson Wårdh, 21 Aug 2025
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- 1
This manuscript aims to assess the capacity for second and third-party sensors to observe short duration extreme rainfall via comparisons to the national rainfall monitoring network in Sweden. While this study found that even the national rainfall monitoring network was unable to fully capture the results of a strong convective storm, it was shown that second and third party sensors can provide accurate representations of that data. This is an important addition to accurate weather monitoring as these additional sensors can assist with creating detailed representations of significant storms within a given area. The authors highlight several limitations to the integration of second and third party sensors into conventional weather monitoring systems and encourage the need for more research in this area. I support the publication of this work following minor edits described below. This work highlights crucial elements for establishing robust weather monitoring protocols and should provide the authors and other researchers will a strong starting point for future research and improvements in this area.
There are, however, a few areas where this manuscript could be improved. Most of the comments before focus on clarity of terminology and how the discussion relates back to the results presented.
Line 161: I was unsure what area this portion of the text was referring to in Figure 1. Is this just a composite of SMHI stations? Clarity could be improved here so readers understand the monitoring locations.
Line 194: Can you define overshooting in the text? What does it mean in this context?
Line 227-229: This sentence would benefit from more clarity and further explanation.
Line 272 and 274: I appreciate the clear explanation of all equations and understand that several of the parameters were obtained from other sources. Could you briefly elaborate what the G and offset variables represent for Equation 6 as well as a and b for Equation 7?
Line 392: Is it typical to see a trend with higher accumulated depth at inland regions like Baramossa? A reference here may be appropriate to establish that your results are typical and expected.
Figure 4: It’s difficult to tell the difference between SMHI Hov and SMHI Laholm D. The line types are very similar. This figure would be more clear with a different line choice for one of those data sets.
Line 494: Could you elaborate in the text on why the plateau period was considering unsuitable for comparison?
Figure 10: Is each panel a-e correspond to one minute? For example: panel a is 17:22-17:23 and panel b is 17:23-17:24. If so, this is not clear based on the information provided in the figure caption.
Figure 11a: Are you expecting a linear trend with this comparison? If so, reporting an R2 value would be useful.
Figure 12: The figure caption, please specify that the rs is relating the data to XWR data.
The discussion overall is thorough and touches on many good points. However, is there a reason the results and discussion sections are separate? At times this made assessing the claims made in the discussion challenging due to needing to locate the appropriate data in the Results section. If you choose to leave the Discussion as a separate section, please refer the reader to relevant tables and figures when emphasizing trends observed in the data. I have noted a few specific lines below, but I encourage you to go back through this section for any crucial information that would benefit from being redirected back to a table or figure.
Lines 589-591, Lines 598-599, Line 604, Line 612-616, Lines 628-630, Lines 644-645, Lines 660-662.