the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 18 Sep 2024
Wet-Radome Attenuation in ARM Cloud Radars and Its Utilization in Radar Calibration Using Disdrometer Measurements
Abstract. A relative calibration technique is developed for the U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) user facility Ka-Band ARM Zenith Radars (KAZRs). The technique utilizes the signal attenuation due to water collected on the radome for estimates of the reflectivity factor (Ze) offset. The wet-radome attenuation (WRA) is assumed to follow a logarithmic relationship with rainfall rate in light and moderate rain conditions, measured by a collocated surface disdrometer. A practical advantage of this WRA approach to shorter-wavelength radar monitoring is that while it requires a reference disdrometer, it is shown viable for a wider range of collocated disdrometer measurements than traditional disdrometer direct comparisons in light rain. Adding such techniques may provide an additional, cost-effective monitoring tool for remote/longer-term deployments.
This technique has been applied during the ARM TRacking Aerosol Convection interactions ExpeRiment (TRACER) from October 2021 through September 2022. The estimated offsets in Ze are evaluated against traditional radar calibration and monitoring methods based on datasets available during this campaign. This WRA technique reports offsets that compare favorably with the mean offsets found between the cloud radars and a nearby disdrometer near the time of rain onset, while also demonstrates similar offset and campaign-long trends with respect to collocated and independently-calibrated reference radars. Overall, the KAZR Ze offsets estimated during TRACER remains stable and at a level 2 dBZ lower than the Ze estimated by disdrometer from the campaign start until the end of June 2022. Thereafter, the radar offsets increase to near 7 dBZ at the end of the campaign.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(3278 KB) - Metadata XML
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Supplement
(168 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-2615', Anonymous Referee #1, 14 Oct 2024
This manuscript describes an approach to identify absolute calibration trends of cloud radars. It is an important topic since calibration offsets cause biases in retrievals which use radar reflectivity measurements. I believe that revisions are necessary before the publication of the manuscript.
Main comments.
- The fitting calibration technique requires the use of Dze values, which are the differences between measured and disdrometer-based reflectivities with subsequent estimation of the intercept of Dze-log(R) fits. The authors need to clarify better why the calibration offset estimated directly by comparing radar measurements when R is small (e.g., at the onset of precipitation, so WRA is negligible) is not good enough.
- The KAZR radome is tilted by 4 deg (line 184) to remove the rain water and minimize wet-radome effects. Did this design prove to be not effective? Would your results hold for different tilted angles (e.g., reducing WRA magnitude with the tilt increase)?
- WRA are likely dependent on the radome type, because different radomes remove rain water with different efficiency. Given this, one would expect different slopes of Dze-log(R) relations for KaSACR and KAZR. You assume that these slopes are the same (~8.6 in Figs.5). Please clarify/explain.
- What is the reason of the large change in the KAZR calibration offset (up to ~7 dB or so) towards the end of the deployment (Fig. 8)?
- It appears that for R > 2 mm/h, your log-linear WRA- log(R) relation deviates significantly from the R^(1/3) behaivior suggested by previous studies (Fig. 6).
- Provide more information on frequency dependence of water absorption on a radome (line 142) as opposed to attenuation by cloud and precipitation drops, which is not scaled as the wavelength. The Bertie et al. 1996 reference (line 142) is not in the reference list.
- Please add some discussion on applicability of your approach to different radars, different radomes and different disdrometers (e.g., PARSIVEL, Joss…).
Minor comments
- Providing Ze – R relations for X and Ka-bands (using reflectivity as independent variable in such relations) in Fig. 3 would be informative. Using reflectivity as independent variable in such relations)
- I believe you are assuming that rain-rate and DSDs at the disdrometer level and at the radar resolution volume are the same. Please specify.
- In different parts of the manuscript you use “dB” units instead of “dBZ” units (e.g., lines 216-217, Y-axis in Figs, 2b, 4c, and other instances), and “dBZ” units instead of “dB” units (e.g., lines 253, 271 and other instances).
- The manuscript could benefit from additional editing.
- You use abbreviations “KaSACR” and “SACR” interchangeably. I suggest using KaSACR everywhere.
Citation: https://doi.org/10.5194/egusphere-2024-2615-RC1 -
AC1: 'Reply on RC1', Min Deng, 12 Nov 2024
Thank you for your feedback and for emphasizing the importance of addressing calibration trends in cloud radars to reduce biases in radar-based retrievals. We appreciate your suggestion for revisions and are committed to enhancing the clarity and impact of the manuscript. The item-by-item response to your comments and suggestion are provided in the attached file highlighted in blue.
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RC2: 'Comment on egusphere-2024-2615', Anonymous Referee #2, 05 Nov 2024
The manuscript by Deng et al. describes a method for monitoring trends in calibration of ARM cloud radars and the effect of wet radome attenuation (WRA). Both aspects are important because the number of cloud radars working at Ka band is increasing also because there are (at some extent), affordable radars commercially available that are used mainly for research purposes. Their performance should be monitored and the dependency of the impact on measured reflectivity factor of the WRA, which seems predominant over precipitation and gas attenuation must be predicted. The proposed method is based on a linear relation between the difference (in dB) between the Ze predicted from disdrometer measurements at ground and the one radar measured by radar at 500 meters. With proper averaging, intercept and slope of the relation express the system bias and the rainfall-dependent WRA, respectively.
The current manuscript focuses on one ARM setup. It would be beneficial to generalize the method for different radars and disdrometers. Extending the method to laser disdrometers should not be too difficult, although laser disdrometer measurements are generally considered less accurate than video disdrometer measurements. Laser disdrometers should be available in the TRACER campaign. Extending the method to other types of radomes could be challenging, as radome attenuation depends on many factors, including the hydrophobicity of the radome material, its aging, and the geometry of the radome. The Gibbs formula and the works by Gorgucci et al., Frasier et al., and Schneebeli et al. (doi:10.5194/amt-5-2183-2012) refer to an X-band radar with a hemispherical radome, which might explain the different behavior observed in Figure 6.
Minor Suggestions:
- Please replace “K” or “K” with “k” for specific attenuation to allow a better identification (e.g., in Figures 2a and 4a).
- In Figure 5, please remove the “corr” within the panels or improve the panels.
- The different uses of Ze, which can be predicted or measured with different radars, can be confusing. Consider using superscripts to differentiate them, although this is just a suggestion.
Citation: https://doi.org/10.5194/egusphere-2024-2615-RC2 -
AC2: 'Reply on RC2', Min Deng, 12 Nov 2024
Thank you for your thoughtful comments and suggestions. We appreciate your input on generalizing our method to different disdrometer measures and fitting functions. The item-by-item response to your comments and suggestions are provided in the attached file highlighted in blue.
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-2615', Anonymous Referee #1, 14 Oct 2024
This manuscript describes an approach to identify absolute calibration trends of cloud radars. It is an important topic since calibration offsets cause biases in retrievals which use radar reflectivity measurements. I believe that revisions are necessary before the publication of the manuscript.
Main comments.
- The fitting calibration technique requires the use of Dze values, which are the differences between measured and disdrometer-based reflectivities with subsequent estimation of the intercept of Dze-log(R) fits. The authors need to clarify better why the calibration offset estimated directly by comparing radar measurements when R is small (e.g., at the onset of precipitation, so WRA is negligible) is not good enough.
- The KAZR radome is tilted by 4 deg (line 184) to remove the rain water and minimize wet-radome effects. Did this design prove to be not effective? Would your results hold for different tilted angles (e.g., reducing WRA magnitude with the tilt increase)?
- WRA are likely dependent on the radome type, because different radomes remove rain water with different efficiency. Given this, one would expect different slopes of Dze-log(R) relations for KaSACR and KAZR. You assume that these slopes are the same (~8.6 in Figs.5). Please clarify/explain.
- What is the reason of the large change in the KAZR calibration offset (up to ~7 dB or so) towards the end of the deployment (Fig. 8)?
- It appears that for R > 2 mm/h, your log-linear WRA- log(R) relation deviates significantly from the R^(1/3) behaivior suggested by previous studies (Fig. 6).
- Provide more information on frequency dependence of water absorption on a radome (line 142) as opposed to attenuation by cloud and precipitation drops, which is not scaled as the wavelength. The Bertie et al. 1996 reference (line 142) is not in the reference list.
- Please add some discussion on applicability of your approach to different radars, different radomes and different disdrometers (e.g., PARSIVEL, Joss…).
Minor comments
- Providing Ze – R relations for X and Ka-bands (using reflectivity as independent variable in such relations) in Fig. 3 would be informative. Using reflectivity as independent variable in such relations)
- I believe you are assuming that rain-rate and DSDs at the disdrometer level and at the radar resolution volume are the same. Please specify.
- In different parts of the manuscript you use “dB” units instead of “dBZ” units (e.g., lines 216-217, Y-axis in Figs, 2b, 4c, and other instances), and “dBZ” units instead of “dB” units (e.g., lines 253, 271 and other instances).
- The manuscript could benefit from additional editing.
- You use abbreviations “KaSACR” and “SACR” interchangeably. I suggest using KaSACR everywhere.
Citation: https://doi.org/10.5194/egusphere-2024-2615-RC1 -
AC1: 'Reply on RC1', Min Deng, 12 Nov 2024
Thank you for your feedback and for emphasizing the importance of addressing calibration trends in cloud radars to reduce biases in radar-based retrievals. We appreciate your suggestion for revisions and are committed to enhancing the clarity and impact of the manuscript. The item-by-item response to your comments and suggestion are provided in the attached file highlighted in blue.
-
RC2: 'Comment on egusphere-2024-2615', Anonymous Referee #2, 05 Nov 2024
The manuscript by Deng et al. describes a method for monitoring trends in calibration of ARM cloud radars and the effect of wet radome attenuation (WRA). Both aspects are important because the number of cloud radars working at Ka band is increasing also because there are (at some extent), affordable radars commercially available that are used mainly for research purposes. Their performance should be monitored and the dependency of the impact on measured reflectivity factor of the WRA, which seems predominant over precipitation and gas attenuation must be predicted. The proposed method is based on a linear relation between the difference (in dB) between the Ze predicted from disdrometer measurements at ground and the one radar measured by radar at 500 meters. With proper averaging, intercept and slope of the relation express the system bias and the rainfall-dependent WRA, respectively.
The current manuscript focuses on one ARM setup. It would be beneficial to generalize the method for different radars and disdrometers. Extending the method to laser disdrometers should not be too difficult, although laser disdrometer measurements are generally considered less accurate than video disdrometer measurements. Laser disdrometers should be available in the TRACER campaign. Extending the method to other types of radomes could be challenging, as radome attenuation depends on many factors, including the hydrophobicity of the radome material, its aging, and the geometry of the radome. The Gibbs formula and the works by Gorgucci et al., Frasier et al., and Schneebeli et al. (doi:10.5194/amt-5-2183-2012) refer to an X-band radar with a hemispherical radome, which might explain the different behavior observed in Figure 6.
Minor Suggestions:
- Please replace “K” or “K” with “k” for specific attenuation to allow a better identification (e.g., in Figures 2a and 4a).
- In Figure 5, please remove the “corr” within the panels or improve the panels.
- The different uses of Ze, which can be predicted or measured with different radars, can be confusing. Consider using superscripts to differentiate them, although this is just a suggestion.
Citation: https://doi.org/10.5194/egusphere-2024-2615-RC2 -
AC2: 'Reply on RC2', Min Deng, 12 Nov 2024
Thank you for your thoughtful comments and suggestions. We appreciate your input on generalizing our method to different disdrometer measures and fitting functions. The item-by-item response to your comments and suggestions are provided in the attached file highlighted in blue.
Peer review completion
Journal article(s) based on this preprint
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Scott E. Giangrande
Michael P. Jensen
Karen Johnson
Christopher R. Williams
Jennifer M. Comstock
Ya-Chien Feng
Alyssa Matthews
Iosif A. Lindenmaier
Timothy G. Wendler
Marquette Rocque
Aifang Zhou
Edward Luke
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(3278 KB) - Metadata XML
-
Supplement
(168 KB) - BibTeX
- EndNote
- Final revised paper