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the Creative Commons Attribution 4.0 License.
| 24 Jan 2025
Adaptation of RainGaugeQC algorithms for quality control of rain gauge data from professional and non-professional measurement networks
Abstract. Rain gauge measurements are one of the primary techniques used to estimate a precipitation field, but they require careful quality control. This paper describes a modified RainGaugeQC system, which is applied to real-time quality control of rain gauge measurements made every 10-min. This system works operationally at the national meteorological and hydrological service in Poland. The RainGaugeQC algorithms, which have been significantly modified, are described in detail. The modifications were made primarily to control data from non-professional measurement networks, which may be of lower quality than professional data, especially in the case of private stations. Accordingly, the modifications went in the direction of performing more sophisticated data control, applying weather radar data and taking into account various aspects of data quality, such as consistency analysis of data time series, bias detection, etc. The effectiveness of the modified system was verified based on independent measurement data from manual rain gauges, which are considered one of the most accurate measurement instruments, although they mostly provide daily totals. In addition, an analysis of two case studies is presented. This highlights various issues involved in using non-professional data to generate multi-source estimates of the precipitation field.
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RC1: 'Comment on amt-2024-204', Anonymous Referee #1, 24 Feb 2025
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Katarzyna Osródka, Jan Szturc, Anna Jurczyk, Agnieszka Kurcz
Adaptation of RainGaugeQC algorithms for quality control of rain gauge data from professional and non-professional measurement networks
https://doi.org/10.5194/amt-2024-204Summary
The article describes a number of performant quality control functions in order to assess the quality of precipitation measured by ground based stations. The overall system is detailed and provides a good understanding of different observation problems. The final outcome of the RainGaugeQC system is a quality index for each station.General remarks
lines 67 and 73: There are numerous stations which are operated by former meteorologists or even active meteorologists on their private ground which provide data of a high quality. Often, operators of such stations are organised in amateur meteorological clubs. These clubs often publish data on the internet and togther with the conditions of the measurement locations, so that they are documented. Therefore, private stations do not guarantee a good data quality, but a considerable number of them is regularly monitored and their data of high quality. This needs to be verified for each private station, though.
line 82: internationally, dual-sensor rain gauges are not the rule, even in national weather services. The WMO classifies station location (e.g. GIMO Guide of WMO (2021)) and has performed several gauge intercomparison exercises (Lanza / Vuerich, 2009).
Specific remarks
line 33: "... highly distorted" - depending on the operator, usually radar measurements are quantitatively less accurate, but not highly distorted (see e.g. WMO, 2024)
line 33: "Rain gauge measurements are still considered ..." - maybe you could be more specific, like: "In hydrology, rain gauge measurements are considered ..."
line 42: it is correct that the authors have published relevant papers in this context. However, the reader would also appreciate more general publications, such as WMO BPG on radar data quality and Lanza / Vuerich (2009) on the WMO rain gauge intercomparison.
lines 110 and 115: radar data should only be incorporated after their quality control - else this is not state of the art (WMO, 2024).
line 117: If you do not set a minimum threshold, your correlation risks to give you random results - this should be pointed out more clearly here.
line 131: the adjustment to rain gauges modifies the radar time series temporally and thus bears the risk that a comparison to a rain gauge time series becomes more difficult. A time series with corrected radar data and without gauge adjustment might give better results, in particular if the gauge data to be analysed have been integrated into the adjustment.
lines 132 and 133: please give an indication of the length of the required time interval, e.g. one year.
table 1: the column "type of rain gauges" would merit additional information: what is the minimum volume of the tipping bucket gauges? Are they unheated for the two first rows? Which measurement type is the third row? "heated" does not tell much ...
line 181: please comment on the accuracy of the daily gauges if there is rainfall at the time of the day change. How accurate can the daily precipitation amount be under such conditions? Manually operated gauges often have a time interval for the operator to read and check the rainfall amount which may be in the order of 15 minutes. Such information should be communicated, additionally to the formulation that their data "are believed to be more accurate".
line 189: Please comment on the range of 250 km. Please note that for hydrological quantitative use, distances beyond 120 km range are subject to higher uncertainties in the radar measurement due to the measurement height and measurement geometry. Which range is practically used for your applications?
Figure 3: Why 215 km ranges? Can you please elaborate on this?
line 210: please give more details on the satellite data used in the system! Which is the data source, how is it quantitatively transformed to precipitation? Which is the original resolution in time and space and how is it mapped to fit to the ground measured data?
Table 2: What do you understand with "gross errors"? Please explain more in detail or refer to the correspondent section in this paper
Table 2: TCC - over which time interval does the comparison take place?
Table 2: SCC - please provide more details on the definition of outliers in this context!
line 255: what would be a typical "specific time interval"? Please provide a range in minutes!
line 256: do you request a minimum amount of rainfall in radar and gauge?
line 256: When do you consider a correlation coefficient to be "good"? Please give more details in the assessment of the correlation quality!
line 263: how do you carry out the unbiasing procedure? Please provide more details!
line 272: when do you consider a time series to be long? What is the minimum duration for this?
line 275: the amount of 0.025 mm is per which time period?
line 336: the formula implies that the QI value is reduced even for perfect data. Is this intended?
Formulas (9) and (11): it is unusual to work with a bias in this way - more often, a multiplicative approach is used (see chapter 3.3.5 of WMO, 2024). Your approach penalises a deviation of 5 mm equally for a rain gauge sum of 10 mm and of 50 mm where in the first case, this represents 50%, in the second one 10%.
Figure 4: (a) please indicate the number of dry time spells for each of the months - for some statistics this plays an important role
(b) how did you take into account the systematic bias of extreme values due to the interpolation of the gauges? Does the distance of each gauge play a role? How would the result have looked when comparing to radar data?
line 422: how can you state that the reliability of both data sources is comparable, if your reference is biased? This is, also in the light of the important and illustrative discussion in this paragraph, a statement without foundation.
line 458: Your finding that gauge data in Junauary are the least reliable is at least surprising since point rainfall data in summer are less representative in space. It would therefore be beneficial to read a discussion on these findings, in particular considering the predominant rainfall types and their spatial variability. Is this influenced by low temperatures in winter?
line 521: is this a finding for this day only or are the non-professional gauges always biased towards higher values?
line 540: do you consider this high value to be an outlier or a true value? If a true value, please discuss the discrepancies that you can see in figure 7.
line 556: do you have an explanation for the underestimation? Was there snowfall?
Technical details
line 11: replace "10-min" by "10 min".
line 49: delete "often"
lines 76, 133: replace "np" by "e.g."
line 79: delete "very"
line 122: I suggest to replace "underestimated" by "underestimating the true rainfall"
line 141: replace "were" by "are"
lines 147 - 148: please provide the number of stations here or omit them from the two following bullet points in order to give a uniform information
line 292: please add "r" after "correlation coefficient" for clarity purposes!
References:
WMO (2024) Guide to Operational Weather Radar Best Practices (WMO-No. 1257). Volume VI: Weather Radar Data Processing. Provisional version at https://community.wmo.int/en/activity-areas/weather-radar-observations/best-practices-guidance
Lanza, L., Vuerich, E. (2009) The WMO Field Intercomparison of Rain Intensity Gauges. Atmospheric Research, Volume 94, Issue 4, December 2009, Pages 534-543.
WMO (2021) Guide to Instruments and Methods of Observation (WMO No. 8). https://community.wmo.int/en/activity-areas/imop/wmo-no_8.Citation: https://doi.org/10.5194/amt-2024-204-RC1
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