the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 16 Sep 2025
Path-CVP (pCVP) – Polarimetric radar data snapshot along the predefined path based on Columnar Vertical Profiles
Abstract. Recently introduced Columnar Vertical Profiles (CVPs) arrange polarimetric radar data collected via plan position indicator (PPI) scans in height vs. time format at a single location. A novel method for polarimetric radar data processing and visualization, path-CVP (pCVP), is introduced. It represents radar data in height vs. location format at the time of a completed radar volume scan. pCVP, an offspring of CVP, is a single-radar-volume time snapshot of the polarimetric radar data along an arbitrary or predefined path with high spatial resolution. Multiple examples from S-band WSR-88D radars in the NEXRAD network demonstrate the potential usage and advantages of the technique. Monitoring and quantifying instantaneous weather conditions with polarimetric radar along motorways, mountain overpasses, and aircraft paths during descent and ascent from the runway, as well as tornado location diagnostics, are potential benefits of the novel technique. However, the increasing distance from the radar and the size of the area used for CVP spatial averaging may need to be adjusted based on user needs.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-3980', Anonymous Referee #1, 23 Sep 2025
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RC2: 'Comment on egusphere-2025-3980', Anonymous Referee #2, 15 Oct 2025
This manuscript presents a novel radar data processing and visualization technique referred to as Path-CVP. The method enables the depiction of vertical distributions of radar-observed parameters along arbitrary paths. It holds considerable potential for comparison with aircraft and radiosonde observations, as well as for operational applications in transportation systems that follow fixed routes, such as aviation, highways, and railways.
The authors effectively demonstrate the utility of the proposed method through multiple well-chosen and accessible case studies. The manuscript is clearly structured, and the content is highly valuable. There are only minor comments.
Specific Comments
- L93: Does the Cressman interpolation utilize distance-weighted coefficients, or is it implemented in a linear fashion? Clarification would be appreciated.
- L129–131: Are there additional dependencies on parameters such as Doppler spectrum width, pulse repetition time (PRT), or ρhv? As the authors note in the Discussion, selecting an appropriate CVP radius is crucial for balancing accuracy and the spatial scale of the observed phenomena. Therefore, I suggest that the manuscript more clearly describe the factors that influence the accuracy of pCVP-derived variables—even if these are already discussed in previous literature. Including a concise summary within this manuscript would improve clarity and completeness. Additionally, in Section 3.1 and onward, CVP radii of 2, 5, and 10 km are used. Were these values selected through trial and error, considering the trade-off between the scale of the phenomena and the visibility of their signatures?
- Figure 4: Is the value “−99.00” used to indicate undefined or missing data? If so, this should be explicitly stated in the figure caption or legend. The same clarification applies to other ZDR figures.
- Figure 7: Does “Heading 0 deg.” correspond to true north? This may not be immediately clear to all readers and could benefit from clarification.
- L263–264: I agree that negative ZDR values are indicative of conical graupel in LES (lake-effect snow) environments, as discussed by the authors. However, are snowfall cases with positive ZDR not present in this region, or are they considered less relevant? From an operational perspective, should road maintenance authorities focus only on cases exhibiting negative ZDR?
- L308: The phrase “a minimum in…” seems imprecise in this context. Could the authors clarify what specific range or domain this minimum refers to? Alternatively, would it be more appropriate to describe the feature using terms such as “low values” or “a decrease in the variable” ?
- L313: Is “BEWR” a typographical error for “BWER” (Bounded Weak Echo Region)?
- L381–383: I did not fully understand why RD-QVP is also effective at capturing higher altitudes, as shown in Figures 10b and 10c. My understanding is that RD-QVP differs from QVP in that it incorporates lower elevation angles, allowing vertical profiles to include near-surface layers even close to the radar. Could the authors please provide additional explanation on how RD-QVP enables observation of higher altitudes in these examples? Is this related to the inclusion of data from farther horizontal distances from the radar?
- While the manuscript employs Cressman interpolation, would the use of a median filter also be effective in reducing noise when applying smaller CVP radii?
Citation: https://doi.org/10.5194/egusphere-2025-3980-RC2
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Review of the manuscript “Path-CVP (pCVP) – Polarimetric radar data snapshot along the predefined path based on Columnar Vertical Profiles” submitted to AMT by Bukovcic and Krause.
This manuscript presents a novel display technique for radar data called path-CVP (pCVP). This is a way to observe the current status of radar polarimetric variables along a specified path which enable one to see microphysical processes and precipitation characteristics at desired locations. The authors describe the how the method is applied and present a number of examples that show the application and describe the benefits of the novel approach. The advantages of such a technique are clearly discussed, as well as the limitations.
The document is logically structured, well written, and the content is relevant and useful for practical purposes. I have only a few minor comments and suggestions.
Comments:
Lines 35-36: “range-height indicator (RHI) scan based QVP (R-QVPs, Allabakash et al. 2019, RSVP Blanke et al. 2023)
Blanke, A., A. J. Heymsfield, M. Moser and S. Trömel, 2023: Evaluation of polarimetric ice microphysical retrievals with OLYMPEX campaign data, Atmos. Meas. Tech., 16(8), 2089–2106, https://doi.org/10.5194/amt-16-2089-2023.
Line 43 (and others): The authors mention several times the accuracy being proportional to N0.5 but there is no explanation for this. Please provide a brief explanation.
Lines 129-130: “where the standard deviation of all polarimetric variables is directly proportional to λ1/2 (λ is the radar wavelength), and inversely proportional to N1/2”
I think here a reference is necessary (concerning the wavelength), since this is not a trivial concept.
Line 155: suggest the change “...in the lower 0.5 km AGL, the reduction in ρhv throughout the column up to 3 km AGL…”
Figures 2b, 3b and 4b have the colorbar reversed (from high to low values). Better to present this with increasing values.
Line 198: “… from the WSR-88D KTLX”
line 208: “...layer of increased values of Z…” (remove “the”)
Lines 244-245: “An example of using pCVP for this purpose is in Fig. 8, …” This seems a little misleading because Fig. 8 shows a road map.
Line 257: “...is an artefact…”
Line 295: “...very few direct wind speed measurements on the ground along the tornado path.”
Lines 355-356: “The local microphysical processes are more present in the 2 km version, ” The formulation “more present” is a bit odd…
I suggest “The local microphysical processes are clearly visible…”
In all the examples the radar moments shown are Z, Zdr and ρhv. Could the authors comment on the possibility of using pCVP for displaying Kdp?
What about quantitative retrievals, can these be applied to pCVPs and what is the expected outcome of doing it?