Application of a Method for Fine-Tuning Differential Reflectivity in an X-Band Phased-Array Weather Radar to a Squall Line

Yang, Ling; Xu, Jinyan; Xu, Mingliang; Wu, Pengbo; Chen, Tianyi; Li, Jia; Wu, Chong; Chen, Chao

doi:10.5194/egusphere-2026-3965

Preprints

https://doi.org/10.5194/egusphere-2026-3965

Preprints

28 Aug 2026

| 28 Aug 2026

Application of a Method for Fine-Tuning Differential Reflectivity in an X-Band Phased-Array Weather Radar to a Squall Line

Ling Yang, Jinyan Xu, Mingliang Xu, Pengbo Wu, Tianyi Chen, Jia Li, Chong Wu, and Chao Chen

Abstract. Maintaining the calibration accuracy of differential reflectivity (Z_DR) within 0.2 dB for both X-band and S-band weather radars is a prevailing operational requirement. With the advancement of weather radar detection technologies, X-band phased-array weather radars (XPAR) offer superior spatiotemporal resolution and are particularly advantageous for low-altitude detection. However, they suffer from Z_DR bias caused by the deterioration of antenna isolation, which results from the variation of the normal direction along with changes in the beam pointing angle. To systematically investigate the primary factors affecting XPAR data quality, this study selects synchronized observations from XPAR and an S‑band polarimetric radar (SPOL) over multiple periods, and proposes a refined Z_DRcorrection algorithm (RC-Z_DR) that addresses four aspects: systematic bias correction, attenuation correction, cross‑polarization isolation degradation compensation specific to phased‑array systems, and correction for anomalous radial interference. For systematic bias, light rain below the zero-degree layer is employed as natural calibration targets, and a weighted correction method (Bias-WZ_DR) based on the mean Z_DR values at three elevation angles whose beam pointing angles are close to the array normal direction (0°) is proposed to mitigate the interference of cross‑polarization isolation on system calibration. Attenuation correction formulas are applied to moderate and heavy precipitation regions. In addition, a 9.4 GHz dual‑polarized microstrip antenna array is modeled using High Frequency Structure Simulator (HFSS) electromagnetic simulations to reveal the variations of antenna gain and 3 dB beamwidth with beam pointing angle. Based on this, a quadratic function correction method for cross‑polarization isolation (QCCPI) is proposed to quantitatively compensate for the degradation bias between different elevation angles. A composite threshold method is adopted to filter out radial interferences in low‑level XPAR observations, further improving the quality of basic data. During a moderate rain event at the ZG100 site, after QCCPI correction, the maximum difference in mean Z_DR among different beam pointing angles decreased from 0.45 dB to 0.27 dB. The proposed RC-Z_DR method is then applied to a squall line event observed at the ZG100 site on June 6, 2023. Spatiotemporal matching and comparison with SPOL data demonstrate that the corrected Z_DR data can more accurately capture the polarimetric signatures of the strong convective core of the squall line. The overall mean Z_DR of XPAR across all elevation angles is corrected from -0.591 dB to 0.226 dB, and the bias relative to SPOL observations is reduced from 0.619 dB to 0.198 dB. This work provides a scientific and efficient technical support for operational quality control and performance evaluation of next‑generation phased‑array weather radars.

Received: 13 Jul 2026 – Discussion started: 28 Aug 2026

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Ling Yang, Jinyan Xu, Mingliang Xu, Pengbo Wu, Tianyi Chen, Jia Li, Chong Wu, and Chao Chen

Status: final response (author comments only)

RC1:
'Comment on egusphere-2026-3965', Anonymous Referee #1, 20 Sep 2026
Review of: “Application of a Method for Fine-Tuning Differential Reflectivity in an X-Band Phased-Array Weather Radar to a Squall Line”, submitted to the Atmospheric Measurement Techniques Journal .
This manuscript presents a methodology for monitoring the calibration of differential reflectivity (ZDR) in X-band phased-array weather radars (XPAR) using a very limited dataset and compares the results with observations from an S-band polarimetric radar (SPOL). While the framework incorporates four correction stages to mitigate ZDR error sources, the validation against S-band observations is problematic. S-band data carries inherent limitations as a benchmark, and using it as ground truth does not sufficiently substantiate the accuracy or reliability of the resulting ZDR.
While the manuscript fits the journal’s scope, I have several concerns that need attention before publication is considered.
Major comments:
While the proposed methodology is promising, its validation relies on a minimal sample size, raising significant questions regarding its overall robustness. Why did the authors evaluate this framework on such a limited dataset? A single case study spanning only a few hours is insufficient to demonstrate the effectiveness, long-term stability, and applicability of the proposed framework. The authors must provide a clear, acceptable justification for this decision and validate their approach against a larger, more diverse dataset over an extended observation period.

The proposed framework outlines four primary stages to generate reliable ZDR values. However, each stage presents critical methodological gaps or validation weaknesses that need to be addressed:

System bias: in their literature review, the authors outline several established methodologies to address system bias, including (i) 90deg vertical (birdbath) scans, (ii) Quasi-Vertical Profiles (QVPs) collected during light rain, and (iii) observations within dry snow. However, the manuscript fails to justify why none of these standard approaches were adopted, nor does it provide a comparative analysis between the proposed method and these established techniques. The authors should clarify this choice and ideally benchmark their approach against at least one standard method.

The procedure for handling differential attenuation lacks clarity. Specifically, it is unclear how specific attenuation (AH) was computed (or if it was accounted for at all). At X-band frequencies, both attenuation and differential attenuation are non-negligible. A more rigorous, fully described correction procedure is essential for an X-band framework.

Cross‑polarisation isolation degradation compensation specific to phased‑array systems. For this reviewer, this stage represents the most novel component of the proposed framework, particularly regarding its application to phased-array systems. Unfortunately, because the evaluation is restricted to a brief dataset covering only a few hours, it is impossible to properly verify the effectiveness, stability, or long-term reliability of this compensation technique.

Correction for anomalous radial interference. The filtering solution presented for radial interference appears ad hoc and lacks technical robustness. The approach feels like a simplistic shortcut to remove problematic data rather than a well-supported, objective filtering algorithm. The authors need to provide theoretical or empirical justification for this step.
In addition to the methodological limitations, the overall presentation of the manuscript requires substantial revision. The submission is unrefined, and critical formatting guidelines have not been followed. Specifically, the reference list is unordered and non-compliant with the journal's guidelines for authors, while the figures and tables are inconsistent, improperly labeled, and poorly organised. A thorough re-formatting and structural cleanup are necessary prior to any further consideration. Kindly review the technical comments for several observations.

Technical comments:

Figure and tables organisation. Figure captions lack structural consistency across the manuscript. For example, Figure 17 embeds timeframes and sub-figure labels directly into the main title, whereas Figure 18 adopts a clearer structure by separating the main title from the sub-figure descriptions. All figure captions must follow a uniform, standardised format throughout the manuscript. Such problems appear throughout the manuscript.
Table Titles: Table titles throughout the paper need refinement for clarity (e.g., the title for Table 3 is overly vague). On the other hand, the table on page 20 lacks a title and numbering, and the values are clearly incorrect (a bias of 19967 dB is unrealistic). Such problems appear throughout the manuscript.
Similarly, some figures are repetitive and not easy to follow.
Mathematical expressions across the manuscript should strictly adhere to standard academic formatting and journal guidelines. The introducing text following equations (e.g., the use and formatting of "where") is inconsistent. For instance Lines 280, 286 or 294 uses different styles.
Variable Definitions: Please carefully audit the text to ensure every variable appearing in an equation is explicitly defined upon its first appearance. For instance, in equations 2-3 non-mathematical symbols are used, and \lambda is left undefined.
Ensure all timestamps and dates follow a standard, consistent format throughout the paper (e.g., ISO format or standard UTC presentation).
Reference List: The reference section must be reordered alphabetically and formatted strictly according to the journal’s author guidelines.
Line 89. Please add a proper reference for the method based on scans taken at 90 degree.
Lines 136. This paragraph disrupts the flow of the section and should be rephrased to improve logical continuity and readability
Line 145. This paragraph presents concluding remarks, which are inappropriate for the Introduction/Literature Review section and should be relocated.
Line 209. This sentence is incorrect. ZDR system bias detection is not limited to the highest elevation angle scan.
Line 223-235. The procedural sequence appears out of order: shouldn't the process begin with the determination of the melting layer (ML) height, given that light-rain data selection is restricted to regions below the ML bottom? Furthermore, please consistently use the term "melting layer" instead of "zero-degree layer."
Figure 4. There are no axes labels (latitude and longitude).
Figure 5. Axis labels are incorrect; please use standard labels such as "Range [units]" and "Height [units]"
Eq. 7. Please use a more adequate citation.
Line 267. The term ‘La’ is duplicated.
Figures 8-12 may look better in one combined plot, using different line styles and markers.

Citation: https://doi.org/10.5194/egusphere-2026-3965-RC1

CC1: 'Comment on egusphere-2026-3965', yuxiang wen, 22 Sep 2026

AC1: 'Comment on egusphere-2026-3965', Jinyan Xu, 01 Oct 2026

Author's track-changes file and response

Citation: https://doi.org/10.5194/egusphere-2026-3965-AC1

AC1: 'Comment on egusphere-2026-3965', Jinyan Xu, 01 Oct 2026

RC2: 'Comment on egusphere-2026-3965', Anonymous Referee #2, 01 Oct 2026

Ling Yang, Jinyan Xu, Mingliang Xu, Pengbo Wu, Tianyi Chen, Jia Li, Chong Wu, and Chao Chen

Data sets

XPAR-DATA Ling Yang, Jinyan Xu, Mingliang Xu https://github.com/Xuyanyan88/XPAR-DATA

Model code and software

Xuyanyan88/RC-ZDR Ling Yang, Jinyan Xu, Mingliang Xu https://github.com/Xuyanyan88/RC-ZDR

Interactive computing environment

jypyer-noterbook-RC-ZDR Ling Yang, Jinyan Xu, Mingliang Xu https://github.com/Xuyanyan88/jypyer-noterbook-RC-ZDR

Video supplement

Abstract Brief: Fine‑Tuning ZDR in an X‑Band Phased‑Array Weather Radar ‑ Application of RC‑ZDR to a squall line case observed by XPAR and SPOL Ling Yang, Jinyan Xu, Mingliang Xu https://doi.org/10.5446/73736

Video abstract

Fine‑Tuning Differential Reflectivity and Its Application to a Squall Line ‑ Application of RC‑ZDR to an X‑band phased‑array weather radar Ling Yang, Jinyan Xu, Mingliang Xu https://doi.org/10.5446/73737

Ling Yang, Jinyan Xu, Mingliang Xu, Pengbo Wu, Tianyi Chen, Jia Li, Chong Wu, and Chao Chen

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Short summary

X-band phased-array weather radar (XPAR) suffers differential reflectivity biases from changing beam directions and poor antenna isolation. This study develops a refined differential reflectivity correction algorithm (RC-ZDR). Combining S-band polarimetric radar (SPOL) data and electromagnetic simulation, the algorithm eliminates radar errors. Squall line tests validate its capability to improve XPAR data quality and support phased-array radar operational quality control.


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