the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 19 Jun 2025
Design and Application of Two Consistency Verification Models for Weather Radar Networks in the South China Region
Abstract. The observational consistency between ground-based weather radars significantly impacts the quality of mosaic products and severe convection identification products. The real-time monitoring of observational biases between radars can provide a basis for calibration and adjustment. This study designed a consistency verification model for weather radar networks based on the FY-3G precipitation radar (SGRCM) and a ground-based weather radar network consistency verification model (AWRCM). From January to October 2024, observational experiments were conducted in the South China region involving 19 S-band weather radars and 13 X-band phased-array weather radars. The aim was to analyze the influencing factors of the consistency verification models and the observational biases of reflectivity factors for radars with different bands and systems. For the S-band weather radars, the difference in the mean bias between the two models ranged from -1.5 dB to 1.4 dB, and the difference in the mean standard deviation ranged from -1.2 dB to 1.2 dB. For the X-band phased-array weather radars, the difference in the mean bias between the two models ranged from -6.67 dB to 0.84 dB, and the difference in the mean standard deviation ranged from -0.38 dB to 1.51 dB. The evaluation results of the two models show good consistency for weather radars with different bands.
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Status: open (until 26 Jul 2025)
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RC1: 'Comment on egusphere-2025-2313', Anonymous Referee #1, 02 Jul 2025
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- On pages 85 to 90 of the article, the VCP21 scanning mode is mainly used for stratiform precipitation, while VCP31 is primarily used for clear-sky conditions.It is recommended to make the changes in the text after confirmation.
- Section 3.1.2 mentions the removal of shielding caused by terrain. However, in recent years, shielding caused by buildings at low elevation angles has become increasingly common. How is this factor considered in the algorithm to ensure the reliability of the final results?
- How can the stability and accuracy of satellite data be ensured, and how can the results of satellite-ground comparisons be used to calibrate the radar?
Citation: https://doi.org/10.5194/egusphere-2025-2313-RC1 -
AC1: 'Reply on RC1', Heng Hu, 07 Jul 2025
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Thank you for your valuable comments. Our detailed responses are as follows:
Comment 1: Regarding the volume scanning modes currently operated in the radar network, we will verify the operational configurations and make appropriate revisions in the updated version of the manuscript.
Comment 2: Presently, we have incorporated configurable parameters within the algorithm. For stations subject to significant obstruction—including those caused by buildings—the lowest elevation angle (0.5°) is excluded from comparative analyses. Additionally, we are developing an algorithm to systematically assess the actual shielding conditions at each station. Once refined, this will be integrated into the model to enhance overall reliability.
Comment 3: FY-3G will be cross-compared with other satellites such as GPM to ensure a robust and consistent calibration mechanism. Within this framework, we propose using FY-3G as a reference benchmark to evaluate the consistency among ground-based radar networks, rather than directly calibrating discrepancies between radar and satellite observations. This approach is adopted due to the inherent difficulty in establishing which observation—satellite or radar—more accurately represents the true characteristics of the observed targets.
Citation: https://doi.org/10.5194/egusphere-2025-2313-AC1
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RC2: 'Comment on egusphere-2025-2313', Anonymous Referee #2, 13 Jul 2025
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This paper needs a major revision. I think the material is there, but a more thorough analysis is needed. A lot of information on the radars are missing (hardware signal processing e.gI) so that the results are hard to interpret. Figures are not properly discussed (e.g Figure 17) where two modes are visible. In principle you would expect one linear dependence (ideally 1:1).
Some more specific comments;
You don’t list other options to verify the calibration and consistency of data in the network, most importantly the sun as a reference. Please include and discuss!
A technical description of the radars you investigate is missing. This is important to interpret the results:
e.g. antenna gain, beam width, transmit power, tx type, dualpol or not, signal processing, clutterfilter and so on.
you missed https://amt.copernicus.org/preprints/amt-2021-257/amt-2021-257.pdf
check out this paper
l 48: „satellite used as a reference standard”: I would disagree here. No weather radar network is using satellite radar data as a reference operationally
l.100: is an attenuation correction performed with the Ku, Ka-Band data? or do you avoid situations with attenuation?
- 135: the specific mathematical detail to get the coordinates right, should be moved to an appendix, unless there is something new here.
- 232: is this a X-Band phased array? You don’t use an attenuation correction?
this section needs be reworked in order to really provide a meaningful comparison between the two bands
- 244: the 15-35 dBZ: do you do an attenuation correction? or do you avoid any precipitation > 35 dBZ? but then 35 dBZ is probably too large for the X-Band; you will have attenuation. Please clarify.
- 12: I couldn’t find a reference to figure 9. it is not clear which radar is the Radar1 or 2. Clearly state what radar is meant! what kind of correction is shown?
l 265, fig 10: no dualpol system? no sqi, Doppler filter implemented?
- 280: describe the fuzzy logic interference removal I think you mean the left figure as the quality controlled picture?
l 310: figure 15: I don’t understand this figure. How does the ground based consistence analysis looks like? Take radar 1: what is the reference radar here? How do you come up with the bias?
Fig 16: font cannot be read. Rework the figures. X-Axis is a time axis. What time period? why not showing the times? Larger biases can be attributed to specific weather events? Are there any snow cases?
l319: reflectivity is not “strong” it is large, small I would say
Fig 17: clearly two modes are visible in each plot, they are not discussed and explained! (two linear fits with different slopes could be fitted). Two modes suggest that there is something fundamentally wrong, or?
l. 328: without discussing the quality control of the reflectivity factor from the X-Band the results are difficult to interpret: are you really sure that you can rule out attenuation effects e.g.?
l 335: so Fig 19: really doesn’t say anything about the biases. Comparing Fig 19 and 15 one would assume similar performance of the S and X-Band. Why do you show standard deviations? Doesn’t make sense to me. Please explain!
l 361: what is a SC model weather radar?
Fig 23: the result suggests that the satellite / radar has further systematic problems errors in my view. The calibration does not provide a more consistent result.
Citation: https://doi.org/10.5194/egusphere-2025-2313-RC2
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