the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 19 Mar 2025
A new method to retrieve relative humidity profiles from a synergy of Raman lidar, microwave radiometer and satellite
Abstract. Precise continuous measurements of relative humidity (RH) vertical profiles in the troposphere have emerged as a considerable scientific issue. In recent years, a combination of diverse ground-based remote sensing devices has effectively facilitated RH vertical profiling, leading to enhancements in spatial resolution and, in certain instances, measurement accuracy. This work introduces a newly developed approach for obtaining continuous RH profiles by integrating data from a Raman lidar, a microwave radiometer, and satellite sources. RH profiles obtained using synergistic approaches are subsequently compared with radiosonde data throughout a five-month observational study in China. Our suggested method for RH profiling demonstrates optimal concordance with the best correction coefficients R of 0.90 in Huhehaote (HHHT), 0.91 in Yibin (YB) and 0.93 in Qingyuan (QY), respectively. Accordingly, the mean bias (MB) reached the lowest values of 4.93 % in HHHT, 2.63 % in YB and 2.40 % in QY. The mean value of RH decreased with height and presented seasonal characteristics in QY. Finally, the RH height-time evolution in a convective case was analyzed. This study firstly integrates satellite data into ground-based measurement to provide information on RH profiles in China, which may aid in further evaluating their regional characteristic and their impacts on the local ecosystem.
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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
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Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2025-1171', Anonymous Referee #1, 31 Mar 2025
This study evaluates relative humidity (RH) profiles at 47 stations in China using a synergetic multi-source algorithm (dynamic optimal stitching algorithm), comparing the results with radiosonde data. The results suggest that the synergetic algorithm outperforms individual instruments (lidar, microwave radiometer, and satellite) at various altitudes, particularly by leveraging lidar data in the lower atmosphere (<3000 m) and satellite data in the upper atmosphere (>3000 m). The study is valuable and aligns with the scope of the journal. But some concerns should be addressed before acceptance:
- lines 57 and 60, the definition of MVR/MWR is conflict? Please make it clear, also in section 2.2
- The introduction could be enhanced. Although the introduction discusses the integration of Raman lidar and MWR for simultaneous RH profile retrieval, with multiple references cited, the authors do not clearly distinguish how their proposed method differs from existing techniques. The novelty should be emphasized.
- Section 3.1 requires more detailed explanation of the proposed method. Specifically, the formula for calculating the correction coefficients should be included in the main text, not just in Figure 2. Furthermore, using radiosonde as a reference and applying deviations from other measurements as weighting coefficients—how does this differ from traditional data assimilation methods, and what are the advantages?
- Are the biases between other measurements and radiosonde data time-dependent? How do these biases differ from the theoretical measurement errors of the instruments? This can be further discussed.
- The results from the synergetic algorithm in Figure 5 appear to outperform the best observational data. How can this be explained in terms of the algorithm formula presented in Figure 2?
- Some statements are unclear and confusing. For example: "But the signal-to-noise ratio (SNR) decreases with height, thus the threshold of SNR should be set." The purpose of setting an SNR threshold is to ensure signal reliability, not simply because SNR decreases with height. Furthermore, why is the threshold set to 3? Please provide references to support this choice.
- The font size in the figures is too small and should be adjusted for better readability.
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Citation: https://doi.org/10.5194/egusphere-2025-1171-RC1 - AC1: 'Reply on RC1', Zhenyi Chen, 22 Apr 2025
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RC2: 'Comment on egusphere-2025-1171', Anonymous Referee #2, 04 Apr 2025
General comments to authors：
The manuscript presents a new method for obtaining continuous relative humidity distribution by integrating multiple data. And it was validated through observation data from 47 stations in China over a period of five months. The results showed good consistency between the use of synergetic algorithm and radiosonde data. The additional monthly statistical analysis and case studies have also expanded the practical application scenarios of this method. I think this manuscript can be published in the journal Atmospheric Measurement Technology. But before that, I think it is necessary to answer the following questions and make minor modifications. But before that, I think it is necessary to answer the following questions and make minor modifications.
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Specific comments：
- In the introduction section, the author discusses the previous methods of using multi-source data such as radar to study relative humidity profiles, but does not explain how these differ from the synergetic algorithm mentioned in the manuscript. Please explain specifically where the proposed methods are new? What is the difference from before?
- The author mentioned in the introduction section (lines 69-76) that many literature studies have introduced data from Raman LiDAR and microwave radiometer to obtain continuous RH profile data, but did not elaborate on the differences between your method and theirs. This will confuse readers: where is your new method new? Please provide a supplementary description for this section.
- In section 2.1, when it comes to Raman differential absorption and setting the signal-to-noise ratio to 3, the description is unclear.
- In the Instrumentation section, line 118 mentions' The uncertainty of the instrument can reach a confidence level of 95.5%. '. This description is confusing.
- In the Methods and Evaluation section, the core steps of the dynamic optimal stitching algorithm (Figure 2) mentioned, such as correction coefficient calculation and weight allocation, lack mathematical formulas or quantitative descriptions. Suggest adding specific algorithm formulas and detailed explanations in the text rather than in the figure.
- There is a formatting issue with Table 2, please make the necessary changes.
- I noticed that after introducing the observation results of LiDAR, the maximum correlation coefficients R of the collaborative algorithm in HHHT, YB, and QY were 0.90, 0.91, and 0.93, respectively, which is very good. However, the RMSE of each instrument's individual data and sounding data exceeds 20% (Table 3). Does this indicate poor reliability of the data? What do you think about this?
- At the end of section 4.1, the author analyzed the sources of errors. There are many sources of error analysis that lead to data uncertainty, such as the consistency of observation equipment? What is the uncertainty caused by regional differences? What is the physical essence that leads to differences here? There are many things worth pondering, which is why I recommend publishing this manuscript. It is meaningless to simply analyze these data differences.
- The case analysis relies on ERA5 reanalysis data to provide the weather circulation situation (Figure 7), and the results generated by the synergetic method with Figure 8 lack correlation explanation. This is very confusing.
- The conclusion section is cumbersome and not concise, please rephrase.
- AC2: 'Reply on RC2', Zhenyi Chen, 22 Apr 2025
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2025-1171', Anonymous Referee #1, 31 Mar 2025
This study evaluates relative humidity (RH) profiles at 47 stations in China using a synergetic multi-source algorithm (dynamic optimal stitching algorithm), comparing the results with radiosonde data. The results suggest that the synergetic algorithm outperforms individual instruments (lidar, microwave radiometer, and satellite) at various altitudes, particularly by leveraging lidar data in the lower atmosphere (<3000 m) and satellite data in the upper atmosphere (>3000 m). The study is valuable and aligns with the scope of the journal. But some concerns should be addressed before acceptance:
- lines 57 and 60, the definition of MVR/MWR is conflict? Please make it clear, also in section 2.2
- The introduction could be enhanced. Although the introduction discusses the integration of Raman lidar and MWR for simultaneous RH profile retrieval, with multiple references cited, the authors do not clearly distinguish how their proposed method differs from existing techniques. The novelty should be emphasized.
- Section 3.1 requires more detailed explanation of the proposed method. Specifically, the formula for calculating the correction coefficients should be included in the main text, not just in Figure 2. Furthermore, using radiosonde as a reference and applying deviations from other measurements as weighting coefficients—how does this differ from traditional data assimilation methods, and what are the advantages?
- Are the biases between other measurements and radiosonde data time-dependent? How do these biases differ from the theoretical measurement errors of the instruments? This can be further discussed.
- The results from the synergetic algorithm in Figure 5 appear to outperform the best observational data. How can this be explained in terms of the algorithm formula presented in Figure 2?
- Some statements are unclear and confusing. For example: "But the signal-to-noise ratio (SNR) decreases with height, thus the threshold of SNR should be set." The purpose of setting an SNR threshold is to ensure signal reliability, not simply because SNR decreases with height. Furthermore, why is the threshold set to 3? Please provide references to support this choice.
- The font size in the figures is too small and should be adjusted for better readability.
Â
Citation: https://doi.org/10.5194/egusphere-2025-1171-RC1 - AC1: 'Reply on RC1', Zhenyi Chen, 22 Apr 2025
-
RC2: 'Comment on egusphere-2025-1171', Anonymous Referee #2, 04 Apr 2025
General comments to authors：
The manuscript presents a new method for obtaining continuous relative humidity distribution by integrating multiple data. And it was validated through observation data from 47 stations in China over a period of five months. The results showed good consistency between the use of synergetic algorithm and radiosonde data. The additional monthly statistical analysis and case studies have also expanded the practical application scenarios of this method. I think this manuscript can be published in the journal Atmospheric Measurement Technology. But before that, I think it is necessary to answer the following questions and make minor modifications. But before that, I think it is necessary to answer the following questions and make minor modifications.
Â
Specific comments：
- In the introduction section, the author discusses the previous methods of using multi-source data such as radar to study relative humidity profiles, but does not explain how these differ from the synergetic algorithm mentioned in the manuscript. Please explain specifically where the proposed methods are new? What is the difference from before?
- The author mentioned in the introduction section (lines 69-76) that many literature studies have introduced data from Raman LiDAR and microwave radiometer to obtain continuous RH profile data, but did not elaborate on the differences between your method and theirs. This will confuse readers: where is your new method new? Please provide a supplementary description for this section.
- In section 2.1, when it comes to Raman differential absorption and setting the signal-to-noise ratio to 3, the description is unclear.
- In the Instrumentation section, line 118 mentions' The uncertainty of the instrument can reach a confidence level of 95.5%. '. This description is confusing.
- In the Methods and Evaluation section, the core steps of the dynamic optimal stitching algorithm (Figure 2) mentioned, such as correction coefficient calculation and weight allocation, lack mathematical formulas or quantitative descriptions. Suggest adding specific algorithm formulas and detailed explanations in the text rather than in the figure.
- There is a formatting issue with Table 2, please make the necessary changes.
- I noticed that after introducing the observation results of LiDAR, the maximum correlation coefficients R of the collaborative algorithm in HHHT, YB, and QY were 0.90, 0.91, and 0.93, respectively, which is very good. However, the RMSE of each instrument's individual data and sounding data exceeds 20% (Table 3). Does this indicate poor reliability of the data? What do you think about this?
- At the end of section 4.1, the author analyzed the sources of errors. There are many sources of error analysis that lead to data uncertainty, such as the consistency of observation equipment? What is the uncertainty caused by regional differences? What is the physical essence that leads to differences here? There are many things worth pondering, which is why I recommend publishing this manuscript. It is meaningless to simply analyze these data differences.
- The case analysis relies on ERA5 reanalysis data to provide the weather circulation situation (Figure 7), and the results generated by the synergetic method with Figure 8 lack correlation explanation. This is very confusing.
- The conclusion section is cumbersome and not concise, please rephrase.
- AC2: 'Reply on RC2', Zhenyi Chen, 22 Apr 2025
Peer review completion
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Chengli Ji
Qiankai Jin
Feilong Li
Yuyang Liu
Zhicheng Wang
Jiajia Mao
Xiaoyu Ren
Yan Xiang
Wanlin Jian
Peitao Zhao
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(7303 KB) - Metadata XML