Long-term Climatology of Vertical Profiles of Polarimetric Variables and Ice-microphysical Retrievals at X-band. Part I: Radar Calibration

Scharbach, Tobias; Trömel, Silke

doi:10.5194/egusphere-2026-493

Preprints

https://doi.org/10.5194/egusphere-2026-493

Preprints

02 Mar 2026

| 02 Mar 2026

Long-term Climatology of Vertical Profiles of Polarimetric Variables and Ice-microphysical Retrievals at X-band. Part I: Radar Calibration

Tobias Scharbach and Silke Trömel

Abstract. In a two-part series of papers, a climatology of quasi-vertical profiles (QVPs) of polarimetric variables and ice-microphysical retrievals such as ice water content, total number concentration and mean volume diameter is presented. QVPs are generated from plan position indicator scans at 18° elevation angle measured with the X-band radar located in the city of Bonn in western Germany between 2013 and 2023. They have been statistically analysed including error analysis with special emphasis on the characteristics of the melting layer and the dendritic growth layer. This long-term climatology improves the understanding of microphysical processes in stratiform cloud regimes and provides a reference for numerical weather prediction modellers to e.g. advance existing microphysical bulk paramerisation schemes.

While part two analyses and discusses the climatology, this first part of the series describes the prior thorough calibration of the radar reflectivity factor (Z_H) and the differential reflectivity (Z_DR). One method uses the relation between Z_H and Z_DR in light rain to calibrate Z_H. Best fits are determined from simulated Z_H and Z_DR values obtained with T-matrix calculations for various temperatures and values of the width of the canting angle distribution using a large disdrometer dataset of drop size distributions measured over Germany (mostly Bonn). Since this Z_H calibration technique strongly depends on the accuracy of Z_DR and encountered deficiencies in the birdbath scan, QVPs in light rain have been used to calibrate Z_DR. Obtained Z_H offset values are validated and compared using both satellite information and self-consistency relationships including specific differential phase (K_DP). The successful calibration of Z_H is confirmed by the root-mean-square error (0.70 dBZ), the mean- absolute error (0.60 dBZ), and the mean-bias (-0.50 dBZ) compared to the offsets obtained from satellite information. Offsets calculated by applying self-consistency relations show larger discrepancies, which favours the suitability of the novel method.

Received: 27 Jan 2026 – Discussion started: 02 Mar 2026

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Tobias Scharbach and Silke Trömel

Status: final response (author comments only)

RC1:
'Comment on egusphere-2026-493', Anonymous Referee #1, 24 Mar 2026
The paper provides a very comprehensive analysis of the Z_DR biases during a decade-long period of continuous observations with the X-band polarimetric radar at the University of Bonn and suggests a new “reverse Z – Z_DR method” for absolute calibration of Z based on the measurements of calibrated Z_DR. In the first part of the manuscript, the long-term statistics of the Z_DR biases estimated using the traditional “bird-bathing” and QVP-based calibration methodologies are presented illustrating temporal trends and differences between the outputs of the two techniques. This is a very valuable part of the study that demonstrates realistic spans of the Z_DR biases which seem to be quite sensitive to the changes of the radar hardware and software. The message is also that the bird-bathing calibration methodology is not a golden standard at all.
I have a few questions / concerns regarding the reverse Z – Z_DR method.
The Z – Z_DR dependence in rain is very nonlinear and it looks that the best results for Z(Z_DR) retrieval can be achieved only for Z_DR within the range between 0.5 – 2.0 dB where a slope of the Z_DR(Z) dependence is maximal. I doubt that any reliable estimate of Z is possible outside of this Z_DRrange.

Certain criteria should be satisfied to select appropriate values of Z_DRfor retrieval of Z and for utilizing the self-consistency relationship. One of the requirements is Φ_DP < 30°. The bias of Z_DR caused by differential attenuation in rain at X band is equal to the product β Φ_DP where the factor β strongly increases with Z_DR and often assumed to be 0.05 dB/deg. This means that ΔZ_DR can be as high as 1.5 dB for Φ_DP = 30° that is not acceptable if correction for differential attenuation is not performed. Should the Φ_DP requirement be stricter? Was the Z_DR correction for differential attenuation performed in the study?

The authors do not mention a situation with wet radome if it rains over the radar. Were these measurements excluded from the statistics? The problem is that Z bias associated with wet radome can be very high (up to 20 dB) and Z_DR can be heavily biased as well.
Citation: https://doi.org/10.5194/egusphere-2026-493-RC1
- AC1: 'Reply on RC1', Tobias Scharbach, 13 Apr 2026
  
  On behalf of my co-author, I would like to thank the reviewer for his time and valuable suggestions. A detailed response to your comments can be found in the attached file.
  
  Sincerely,
  
  Tobias Scharbach
  
  Citation: https://doi.org/10.5194/egusphere-2026-493-AC1
RC2: 'Comment on egusphere-2026-493', Anonymous Referee #2, 03 May 2026

Table of Contents

_________________
1. AMT 2026-493

.. 1. General comments

.. 2. Abstract

.. 3. Introduction

.. 4. Radar data

.. 5. Calibration of Zdr and Zh

..... 1. Zdr calibration

..... 2. Zh calibration

.. 6. Conclusion

1 AMT 2026-493

==============
1.1 General comments

~~~~~~~~~~~~~~~~~~~~
- The authors provide a rigouros calibration of Zdr and Zh of their

multi-year X-band radar data set

- They first calibrate Zdr with standard techniques (birdbath and

quasi-vertical profiles in light rain)

- They introduce the reverse Zdr-Zh technique to calibrate Zh based on

the calibrated Zh

- They compare their results with GPM satellite measurements and

self-consistency Zh-Kdp methods
This is a valuable data set which is an important basis for polarimetric radar research spanning multiple years. I'm not too convinced about their reverse Zdr - Zh technique for various reasons, i.e., the radome attenuation and ambiguity They authors might address these concerns to further strengthen the applicability of the proposed method. I also do not understand the differences that occur in birdbath scanning and QVP profiles in light rain in view of Zdr offsets. It might be necessary to provide some further insight into this.
1.3 Introduction

~~~~~~~~~~~~~~~~
- In your introduction, you also need to consider and discuss the

following article: Figueras y ventura, 2012:

<https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/qj.1934>

which contains relevant information similar to what you are doing

- Sphere calibration: Joshil, 2022:

<https://www.mdpi.com/2072-4292/14/15/3534>

- line 51 / 52: virtually generated radar targets: wang 2025:

<https://ieeexplore.ieee.org/document/11143545>; schneebeli, 2025:

<https://amt.copernicus.org/articles/18/5157/2025/>

- line 59: birdbath scan: Gorgucci 1999:

<https://ieeexplore.ieee.org/document/739161>

- line 64: "One major benefit and motivation using the QVP..." -> but

this is also true for the birdbath scan

- line 70: "the results were not reliable for the whole period..." ->

Why?

1.4 Radar data

~~~~~~~~~~~~~~
- line 103: rho_hv of 0.7 is terribly low. Why do you use such a low

threshold? Rain is usually above 0.995 for well calibrated radars.

- line 105: Phase offset processing is not really necessary for Kdp

estimation.

Table 1:

- Radar description: a 0.2us transmit pulse leads to a range

resolution o 30 m. So the range resolution you are indicating are

averages? Or you are sampling at at 25m resolution?

- STAR is not really a transmit mode

- Staggering mode is a transmit mode from my point of view

- What kind of transmitter (Magnetron I guess, but what's the power)?

- Antenna gain = ?

- What do you mean with high PRF? What's the low PRF then?

1.5 Calibration of Zdr and Zh

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
1.5.1 Zdr calibration

---------------------
- line 118: You are using the Probert-Jones Gaussian approximation for

the antenna pattern, which can introduce errors of up to 1 dB

compared to the usage of the full antenna pattern. Can this be

justified?

- line 120: You are using theta_h^H and theta_V^H but only G^H

(similar for both directions). Why?

- line 123: If your reference plane is directly behind the antenna,

why do you introduce A_t an A_r?

- line 136: I don't think that Zdr needs to be calibrated to 0.2 dB

for QPE applications.

- line 141: If you look from below, raindrops appear spherical for Zh

and Zv not only in light rain

- line 150 onwards: I don't understand why the birdbath scan should be

influenced by the radar hardware, since it is supposed to

out-calibrate such effects.

- Also: Consists your hardware of rotary joints or similar equipment

or is your transmitter / receiver attached to the antenna

directly?

- Could rotary joints be a source of elevation dependence?

- In general: I don't fully understand the reasoning here
- line 166: 10 valid values in the height dimension depends on how you

interpolate your range data to a vertical grid

- line 175: What is the standard deviation of the canting angle? 8 deg

as stated in line 177?

- Not sure if sufficient information is provided on how you performed

the T-matrix calculations.

- Are you using the code of Mishchenko?

- Can more information on the processing be found in Chen et

al. (2021)?

- What model of the dielectric constant of water are you using?

- Line 181: You should also take into account the findings of Zeyong

2019: <https://ieeexplore.ieee.org/document/9025914>

- I also recall that Figueras y Ventura made experiments with Zdr

calibration in light rain, but I'm not sure if this is published

somewhere.

- Line 194: I'm not sure if this argumentation is valid, since (as

mentioned) the birdbath scan is supposed to calibrate such hardware

changes.

- Figure 4: This figure is using a misleading color scale, since

negative values range in an interval between [-0.2 and -1]

1.5.2 Zh calibration

--------------------
- You ignore the effect of radome attenuation. What is the

consequence?

- From your curve in Figure 3, a Zh value between 34 and 36

corresponds to a Zdr value between 1 and 1.75, which is a huge range

for Zdr but a small one for Zh.

- How can you make an accurate calibration from such a curve?

- line 224 and Figure 6: This is not really a too convincing fit

- line 241: For a comparison

- line 276: 0.25 -0.5 dBZ means 0.25 to 0.5 dBZ?

- Table 3: gap-filled means that the reverse Zdr-Zh algorithm has been

applied?

- I.e., upper row is reverse Zdr-Zh, lower row Kdp-Zh self

consistency

- line 295: I'm not convinced that the reverse Zdr-Zh algorithm

outperforms the Kdp self consistency.

- Kdp is independent of radome attenuation, which is not true for

Zdr.

- In addition the Zdr - Zh relation is ambiguous (as mentioned

before)

- Just because it fits better to the satellite is not a strong

argument

- If you compare to the satellite anyway, why not taking the offsets

obtained from satellite comparisons directly?

1.6 Conclusion

~~~~~~~~~~~~~~
- See comment above

Citation: https://doi.org/10.5194/egusphere-2026-493-RC2

Tobias Scharbach and Silke Trömel

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Short summary

For the X-band radar in Bonn, the birdbath method has proven to be insufficient for calibrating differential reflectivity. This study raises awareness of the need for critically applying the birdbath method. A novel technique for calibrating the reflectivity factor is presented and compared with satellite measurements, serving as ground truth, and a known standard calibration method. The offset values agree well with the satellite and even exceed the accuracy of the standard calibration method.


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