the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Apr 2023
Characterizing the near-global cloud vertical structures over land using high-resolution radiosonde measurements
Hui Xu
Bing Tong
Jinqiang Zhang
Tianmeng Chen
Xiaoran Guo
Jian Zhang
Wenqing Chen
Abstract. Cloud remains one of the largest uncertainties in weather and climate research due to the lack of fine-resolution observations of cloud vertical structure (CVS) on large scale. In this research, near-global CVS is characterized by high-vertical-resolution twice daily radiosonde observations from 374 stations over land. It is found that the cloud base heights (CBHs) from the radiosondes have a higher correlation coefficient (R = 0.91) with the millimeter wavelength cloud radar than that with the ERA5 reanalysis (R = 0.49). Overall, cloudy skies occur 65.3 % (69.5 %) of the time, of which 55.4 % (53.8 %) are one-layer clouds at 0000 (1200) UTC. Most multi-layer clouds are two-layer clouds, accounting for 62.2 % (61.1 %) among multi-layer clouds for 0000 (1200) UTC. Geographically, one-layer clouds tend to occur over arid regions, whereas two-layer clouds do not show any clear spatial preference. The cloud bases and tops over arid regions are higher compared with humid regions albeit with smaller cloud thickness (CT). Clouds tend to have lower bases and thinner layer thicknesses as the number of cloud layer increases. The global mean CT, CBH, and cloud top height (CTH) are 4.89 ± 1.36 (5.37 ± 1.58), 3.15 ± 1.15 (3.07 ± 1.06), and 8.04 ± 1.60 (8.44 ± 1.52) km above ground level (AGL) at 0000 (1200) UTC, respectively. The occurrence frequency of clouds is bimodal with lower peaks between 0.5 and 3 km AGL and upper peaks between 6 and 10 km AGL. The CBH, CTH and CT undergo almost the same seasonality that their magnitudes are greater in the boreal summer than in the winter. As expected, the occurrence frequencies of clouds exhibit pronounced diurnal cycles in different seasons. In boreal summer, clouds tend to form as sun rises and the occurrence frequencies increase from morning to later afternoon, with the peak in the early afternoon at altitudes 6–12 km; while in boreal winter, clouds have peak occurrence frequencies in the morning. The relations between surface meteorological variables and moisture with CBH are investigated as well, showing that CBH are generally more significantly correlated with 2 m RH (RH2m) and 2 m T (T2m) than with surface pressure and 10 m wind speed. Larger T2m and smaller RH2m always correspond to higher CBH. In most cases CBHs are negatively correlated to soil water content. The near-global CVS obtained from high-vertical-resolution radiosonde in this study can provide key data support for improving the accuracy of cloud radiative forcing simulation in climate models.
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Hui Xu et al.
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-472', Anonymous Referee #1, 20 May 2023
General
This paper examines the near-global cloud vertical structures using two years of radiosonde data. I do not find any major flaws with their methodology and conclusions, and the statistical results could be a nice contribution to modeling global cloud radiative effects. However, clarifications are needed to make this paper a compelling story. I suggest returning to the authors for minor revision.
Major
The Introduction section listed several previous works using lowering resolution radiosonde data to retrieval cloud boundaries but did not include a summary of what were found from those works, what are the main statistical and conclusions from those works. Most importantly, the authors should articular what are novel in the current study, in addition to higher resolution data.
Minor
Line 17-19: cloud base height correlate with millimeter wavelength radar?
Line 52: do you mean the Chang and Li retrievals have large discrepancies? Discrepancies relative to what?
Line 55-56: the last sentence needs to be revised. Polar orbiting satellites can have short revisit periods such as AQUA/TERRA. Do you mean ‘narrower nadir views’ ?
Line 58: cloud radars
Line 75: do you mean the vertical resolution, horizontal resolution, or temporal resolution?
Line 75-79: it will be more intuitive to understand the difference of ‘resolution’ (whatever it refers to) from previous and current radiosondes if you can provide several numbers here.
Line 107: change ‘considered’ to ‘included’
Line 115: an accuracy of
Line 124-125: references for the ERA5 reanalysis are needed here
Line 168: enters a moist layer
Line 190: can you explain why a max-RH is needed to detect a cloud layer? What is inter-RH in Table 1 and Figure 2? Is it the RH between consecutive cloud layers?
Line 184-191: do you do any averaging or smoothing for the RH and T profiles, considering they are in high vertical resolution?
Figure 3: I suggest change sounding times to 00UTC and 12UTC to be consistent with your intro text
line 223: maybe change the word ‘correctly’ to ‘reasonably’
line 313-314: these result in the occurrence
line 368: oceanic climate
Citation: https://doi.org/10.5194/egusphere-2023-472-RC1 - AC1: 'Reply on RC1', Jianping Guo, 04 Aug 2023
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RC2: 'Comment on egusphere-2023-472', Anonymous Referee #2, 27 Jun 2023
Review of “Characterizing the near-global cloud vertical structures over land using high-resolution radiosonde measurements” by Xu et al., for publication in EGUsphere
General Comments
The main point of this manuscript examines cloud vertical structure using radiosonde data from 374 land stations. Millimeter wavelength radar estimated cloud boundaries have a high correlation to radiosondes relative to ERA-5 derived cloud vertical structure, which is unsurprising. This study analyzes multi-layer clouds, with their analysis noting several instances of 3 or more cloud layers measured by a single radiosonde. This study is packed with interesting information about global cloud statistics, particularly how they vary in different regions of the world and for liquid, mixed and ice phase clouds. Their results discussing seasonal cloud boundaries are in very good agreement with several previous studies also using radiosondes for cloud property measurements. The figures are very high quality and complement the text very well.
There are few areas where this manuscript needs improvement. First, there is very little discussion about the radiosonde types or any discussion of measurement calibration/uncertainty. This is extremely important given the volume of radiosondes and noting that different versions (e.g., the Vaisala RS41 and RS92) were developed differently. The Vaisala RS92 in particular is prone to an RH dry bias, and there is no mention if those sondes (if they were used at all) employed any sort of correction or homogenization to the global database (aside from what we know about the GRUAN database). The authors need to make these points much more clear and do a better job of convincing the reader that the measurements are indeed homogenized. I think this can be accomplished in 1-2 additional paragraphs in the methods section, along with a table highlighting manufacturer/temperature/humidity (etc.) uncertainty and accuracy, along with documented studies noting any biases. Second, I think the authors missed a fantastic opportunity to explore their results in the context of relative humidity with respect to ice or RH(ice). RH(ice) is key for ice cloud formation, and though there are many studies that caution against the use of radiosonde relative humidity especially at high altitudes, the statistics of RH(ice) would be interesting to present nonetheless as it would give clear indication which climates around the world are most conducive to ice supersaturation. If the authors choose to add this to the paper, they will need to also ensure the uncertainty is well documented. In addition, there are several technical, grammatical and spelling errors in this manuscript that – while not significant in volume – was distracting and made the paper hard to read at times. I encourage the readers to carefully check their work for these errors.
Overall, this paper is a very extensive analysis of global cloud coverage that fits well within the scope of EGUsphere, and should be considered for publication after addressing several comments below.
Specific Comments
L17: It would be good to elaborate a bit here in the abstract where these 374 land stations are partitioned.
L37-48: This is a solid introductory motivation.
L57: This is a bit awkwardly written. Perhaps consider moving the Hahn et al. (2001) reference to the end of the sentence.
L57-61: I would be careful making the assertion that coverage of these ground-based radars/lidars/ceilometers are limited to “a few locations”. You should expand this paragraph by at least 2-3 sentences and highlight where these locations are, and demonstrate to the reader that these measurements are indeed few. Otherwise, it undermines (in my opinion) a big part of the motivation of this research. The Atmospheric Radiation Measurement (ARM) program has many of these sites listed and available, and are definitely more than a few.
North Slope Alaska:
Zhang, D., Wang, Z., Luo, T., Yin, Y., and Flynn, C., 2017: The occurrence of ice production in slightly supercooled Arctic stratiform clouds as observed by ground-based remote sensors at the ARM NSA site, J. Geophys. Res. Atmos., 122, 2867– 2877, doi:10.1002/2016JD026226.
Tropical Western Pacific (note there were 3 sites):
Comstock, J. M., Protat, A., McFarlane, S. A., Delanoë, J., and Deng, M., 2013: Assessment of uncertainty in cloud radiative effects and heating rates through retrieval algorithm differences: Analysis using 3 years of ARM data at Darwin, Australia, J. Geophys. Res. Atmos., 118, 4549–4571, doi:10.1002/jgrd.50404.
Eastern North Atlantic:
Giangrande, S. E., Wang, D., Bartholomew, M. J., Jensen, M. P., Mechem, D. B., Hardin, J. C., & Wood, R. (2019). Midlatitude oceanic cloud and precipitation properties as sampled by the ARM Eastern North Atlantic Observatory. Journal of Geophysical Research: Atmospheres, 124, 4741– 4760. https://doi.org/10.1029/2018JD029667.
Southern Great Plains Site:
Dong, X., Minnis, P., Xi, B., Sun-Mack, S., and Chen, Y., 2008: Comparison of CERES-MODIS stratus cloud properties with ground-based measurements at the DOE ARM Southern Great Plains site, J. Geophys. Res., 113, D03204, doi:10.1029/2007JD008438.
L72-76: You should review the “cirrus cloud detection algorithm” subsection in Dzambo and Turner (2016) as their method provided a viable radiosonde/ground-based radar/lidar collocation algorithm. Their method was by no means perfect, but their method established both spatial and temporal restrictions to ensure a radiosonde was indeed launched into a cloud.
Dzambo, A. M., and Turner, D. D. (2016), Characterizing relative humidity with respect to ice in midlatitude cirrus clouds as a function of atmospheric state, J. Geophys. Res. Atmos., 121, 12,253– 12,269, doi:10.1002/2015JD024643.
Also consider the role of their “lag time” correction, which is also in this section.
L82: Where in the world are these 374 land stations? A few examples would be good to note here for the reader.
L90: technical correction: “... we also investigate the relationship between CBH, surface meteorology, and moisture.”
Section beginning at L95: There is a very important piece of information missing from this section... the manufacturing information of all radiosondes used in your database. Were these radiosondes Vaisala RS-92? Vaisala RS-41? Because your results are very sensitive to the relative humidity measurements of the radiosonde, it is also necessary to know what humidity sensors are on each radiosonde, and by extension, it is further necessary to know and understand the relative humidity uncertainty with each. There are numerous studies discussing the topic about relative humidity corrections in radiosondes. Vaisala RS-92 radiosondes have a very well documented dry bias in their measurements, and to the extent of my knowledge, only the GRUAN database of radiosondes have their humidity products homogenized between different versions. I strongly recommend updating this section of the paper with at least a paragraph discussing the humidity measurements, as well as adding a table of the different sensors from each manufacturer, perhaps something like: manufacturer, years used, reference for sensor, instrument uncertainty, and (if applicable) known biases and corrections such as those for the RS-92.
Wang, J., L. Zhang, A. Dai, F. Immler, M. Sommer, and H. Vömel, 2013: Radiation Dry Bias Correction of Vaisala RS92 Humidity Data and Its Impacts on Historical Radiosonde Data. J. Atmos. Oceanic Technol., 30, 197–214, https://doi.org/10.1175/JTECH-D-12-00113.1.
Miloshevich, L. M., Vömel, H., Whiteman, D. N., and Leblanc, T. (2009), Accuracy assessment and correction of Vaisala RS92 radiosonde water vapor measurements, J. Geophys. Res., 114, D11305, doi:10.1029/2008JD011565.
Vömel, H., and Coauthors, 2007: Radiation Dry Bias of the Vaisala RS92 Humidity Sensor. J. Atmos. Oceanic Technol., 24, 953–963, https://doi.org/10.1175/JTECH2019.1.
Dzambo, A. M., Turner, D. D., and Mlawer, E. J.: Evaluation of two Vaisala RS92 radiosonde solar radiative dry bias correction algorithms, Atmos. Meas. Tech., 9, 1613–1626, https://doi.org/10.5194/amt-9-1613-2016, 2016.
Jensen, M. P., Holdridge, D. J., Survo, P., Lehtinen, R., Baxter, S., Toto, T., and Johnson, K. L.: Comparison of Vaisala radiosondes RS41 and RS92 at the ARM Southern Great Plains site, Atmos. Meas. Tech., 9, 3115–3129, https://doi.org/10.5194/amt-9-3115-2016, 2016.
de Boer, G., Calmer, R., Jozef, G. et al. Observing the Central Arctic Atmosphere and Surface with University of Colorado uncrewed aircraft systems. Sci Data 9, 439 (2022). https://doi.org/10.1038/s41597-022-01526-9 (see methods section of this paper)
These are papers that should provide good context for RS41 and RS92 humidity measurements. As for the other radiosondes that may have been used in your study, please search for and add documentation similar to what these studies have in addressing humidity measurements.
L125: The comment here about the ERA-Interim is unnecessary.
L130: The inclusion of soil moisture content as part of your analysis is interesting, but can you provide context (perhaps a reference or two) showing how this ERA-5 variable was used in previous studies (especially for surface latent fluxes, clouds, or something similar).
L143: I am not convinced this is the best version of RH with respect to ice to use. Murphy and Koop (2005) did an extensive review of the available RH(ice) equations, pointing out an error (at the time) in the World Meteorological Organization’s primary equation. Review this paper, and at minimum, comment on how this choice of equation might vary from the other formulations listed here. Goff and Gratch (1946) is very commonly used.
Murphy, D.M. and Koop, T. (2005), Review of the vapour pressures of ice and supercooled water for atmospheric applications. Q.J.R. Meteorol. Soc., 131: 1539-1565. https://doi.org/10.1256/qj.04.94.
L191: Do you mean to say “Otherwise, this layer is discarded from the analysis”?
L192: I already mentioned this once, but it might be worthwhile referring to Dzambo and Turner (2016) and using their time-lag correction for collocating radiosonde and ground-based radar measurements. With merged clouds, I don’t think it would change your result much given the correlation is quite high already.
L192 (technical correction): Just say “To obtain robust cloud structures, …"
L223: I would say “accurately” over “correctly”, since both instruments are limited in attaining truly “correct” measurements.
L225: Do you mean R^2 values?
L228-230: This is inaccurate. ERA-5 assimilates geostationary satellite radiance measurements, which are used for a host of applications. Review Hersbach et al. (2020) more carefully, particularly the sections discussing satelltie radiance assimilation and the cloud parameterization schemes used to evolve clouds in their output.
Hersbach, H, Bell, B, Berrisford, P, et al. The ERA5 global reanalysis. Q J R Meteorol Soc. 2020; 146: 1999– 2049. https://doi.org/10.1002/qj.3803.
L238-240: I don’t doubt these conclusions, however, discussing the cloud parameterization schemes used for ERA-5 would make these statements more convincing to the reader, because it would help explain why/how cloud base heights in ERA-5 were lower than CloudSat/CALIPSO. Also keep in mind that CloudSat has a blind zone below 750m (see the Stephens et al. Reference you already cited).
L243-244: Is this true globally, or just the regions where the radiosonde data were available?
L336: Cloud bases above the tropopause are often the result of “overshooting tops” from deeply penetrating cumulonimbus (thunderstorms).
Homeyer, C. R., and M. R. Kumjian, 2015: Microphysical Characteristics of Overshooting Convection from Polarimetric Radar Observations. J. Atmos. Sci., 72, 870–891, https://doi.org/10.1175/JAS-D-13-0388.1.
L349: You mean to say “East Asia”. Please check your manuscript for technical, grammar and spelling errors, as I have noticed several to this point in the manuscript.
L353-355: I agree with this conclusion.
L386: “cloud base for clouds...”
L389-390: This is a good result, and consistent with many previous studies.
L398-399: This is because the western US is often dry near the surface, hence boundary layer heights tend to be much deeper. I would add this information to this part of the text.
L416: I agree.
L420-450: Referring to my previous comments about RH(ice), it would be good to note layers where RH(ice) exceeds 100%. Several studies note the presence of “subvisible” cirrus, which to this point your study does not mention. Subvisible cirrus are typically contained to the tropics, and worth elaborating here in perhaps 1-2 sentences. Additionally, analyzing RH(ice) would add very scientifically interesting detail to your study by identifying which climates have the most frequent ice saturation observations, which is extremely important for ice cloud formation.
Section 3.6: I generally agree with the conclusions presented in this section.
The conclusions section is a good summary, though may need to be updated depending on what the authors choose to do in addressing my comments across the previous sections.
Citation: https://doi.org/10.5194/egusphere-2023-472-RC2 - AC2: 'Reply on RC2', Jianping Guo, 04 Aug 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-472', Anonymous Referee #1, 20 May 2023
General
This paper examines the near-global cloud vertical structures using two years of radiosonde data. I do not find any major flaws with their methodology and conclusions, and the statistical results could be a nice contribution to modeling global cloud radiative effects. However, clarifications are needed to make this paper a compelling story. I suggest returning to the authors for minor revision.
Major
The Introduction section listed several previous works using lowering resolution radiosonde data to retrieval cloud boundaries but did not include a summary of what were found from those works, what are the main statistical and conclusions from those works. Most importantly, the authors should articular what are novel in the current study, in addition to higher resolution data.
Minor
Line 17-19: cloud base height correlate with millimeter wavelength radar?
Line 52: do you mean the Chang and Li retrievals have large discrepancies? Discrepancies relative to what?
Line 55-56: the last sentence needs to be revised. Polar orbiting satellites can have short revisit periods such as AQUA/TERRA. Do you mean ‘narrower nadir views’ ?
Line 58: cloud radars
Line 75: do you mean the vertical resolution, horizontal resolution, or temporal resolution?
Line 75-79: it will be more intuitive to understand the difference of ‘resolution’ (whatever it refers to) from previous and current radiosondes if you can provide several numbers here.
Line 107: change ‘considered’ to ‘included’
Line 115: an accuracy of
Line 124-125: references for the ERA5 reanalysis are needed here
Line 168: enters a moist layer
Line 190: can you explain why a max-RH is needed to detect a cloud layer? What is inter-RH in Table 1 and Figure 2? Is it the RH between consecutive cloud layers?
Line 184-191: do you do any averaging or smoothing for the RH and T profiles, considering they are in high vertical resolution?
Figure 3: I suggest change sounding times to 00UTC and 12UTC to be consistent with your intro text
line 223: maybe change the word ‘correctly’ to ‘reasonably’
line 313-314: these result in the occurrence
line 368: oceanic climate
Citation: https://doi.org/10.5194/egusphere-2023-472-RC1 - AC1: 'Reply on RC1', Jianping Guo, 04 Aug 2023
-
RC2: 'Comment on egusphere-2023-472', Anonymous Referee #2, 27 Jun 2023
Review of “Characterizing the near-global cloud vertical structures over land using high-resolution radiosonde measurements” by Xu et al., for publication in EGUsphere
General Comments
The main point of this manuscript examines cloud vertical structure using radiosonde data from 374 land stations. Millimeter wavelength radar estimated cloud boundaries have a high correlation to radiosondes relative to ERA-5 derived cloud vertical structure, which is unsurprising. This study analyzes multi-layer clouds, with their analysis noting several instances of 3 or more cloud layers measured by a single radiosonde. This study is packed with interesting information about global cloud statistics, particularly how they vary in different regions of the world and for liquid, mixed and ice phase clouds. Their results discussing seasonal cloud boundaries are in very good agreement with several previous studies also using radiosondes for cloud property measurements. The figures are very high quality and complement the text very well.
There are few areas where this manuscript needs improvement. First, there is very little discussion about the radiosonde types or any discussion of measurement calibration/uncertainty. This is extremely important given the volume of radiosondes and noting that different versions (e.g., the Vaisala RS41 and RS92) were developed differently. The Vaisala RS92 in particular is prone to an RH dry bias, and there is no mention if those sondes (if they were used at all) employed any sort of correction or homogenization to the global database (aside from what we know about the GRUAN database). The authors need to make these points much more clear and do a better job of convincing the reader that the measurements are indeed homogenized. I think this can be accomplished in 1-2 additional paragraphs in the methods section, along with a table highlighting manufacturer/temperature/humidity (etc.) uncertainty and accuracy, along with documented studies noting any biases. Second, I think the authors missed a fantastic opportunity to explore their results in the context of relative humidity with respect to ice or RH(ice). RH(ice) is key for ice cloud formation, and though there are many studies that caution against the use of radiosonde relative humidity especially at high altitudes, the statistics of RH(ice) would be interesting to present nonetheless as it would give clear indication which climates around the world are most conducive to ice supersaturation. If the authors choose to add this to the paper, they will need to also ensure the uncertainty is well documented. In addition, there are several technical, grammatical and spelling errors in this manuscript that – while not significant in volume – was distracting and made the paper hard to read at times. I encourage the readers to carefully check their work for these errors.
Overall, this paper is a very extensive analysis of global cloud coverage that fits well within the scope of EGUsphere, and should be considered for publication after addressing several comments below.
Specific Comments
L17: It would be good to elaborate a bit here in the abstract where these 374 land stations are partitioned.
L37-48: This is a solid introductory motivation.
L57: This is a bit awkwardly written. Perhaps consider moving the Hahn et al. (2001) reference to the end of the sentence.
L57-61: I would be careful making the assertion that coverage of these ground-based radars/lidars/ceilometers are limited to “a few locations”. You should expand this paragraph by at least 2-3 sentences and highlight where these locations are, and demonstrate to the reader that these measurements are indeed few. Otherwise, it undermines (in my opinion) a big part of the motivation of this research. The Atmospheric Radiation Measurement (ARM) program has many of these sites listed and available, and are definitely more than a few.
North Slope Alaska:
Zhang, D., Wang, Z., Luo, T., Yin, Y., and Flynn, C., 2017: The occurrence of ice production in slightly supercooled Arctic stratiform clouds as observed by ground-based remote sensors at the ARM NSA site, J. Geophys. Res. Atmos., 122, 2867– 2877, doi:10.1002/2016JD026226.
Tropical Western Pacific (note there were 3 sites):
Comstock, J. M., Protat, A., McFarlane, S. A., Delanoë, J., and Deng, M., 2013: Assessment of uncertainty in cloud radiative effects and heating rates through retrieval algorithm differences: Analysis using 3 years of ARM data at Darwin, Australia, J. Geophys. Res. Atmos., 118, 4549–4571, doi:10.1002/jgrd.50404.
Eastern North Atlantic:
Giangrande, S. E., Wang, D., Bartholomew, M. J., Jensen, M. P., Mechem, D. B., Hardin, J. C., & Wood, R. (2019). Midlatitude oceanic cloud and precipitation properties as sampled by the ARM Eastern North Atlantic Observatory. Journal of Geophysical Research: Atmospheres, 124, 4741– 4760. https://doi.org/10.1029/2018JD029667.
Southern Great Plains Site:
Dong, X., Minnis, P., Xi, B., Sun-Mack, S., and Chen, Y., 2008: Comparison of CERES-MODIS stratus cloud properties with ground-based measurements at the DOE ARM Southern Great Plains site, J. Geophys. Res., 113, D03204, doi:10.1029/2007JD008438.
L72-76: You should review the “cirrus cloud detection algorithm” subsection in Dzambo and Turner (2016) as their method provided a viable radiosonde/ground-based radar/lidar collocation algorithm. Their method was by no means perfect, but their method established both spatial and temporal restrictions to ensure a radiosonde was indeed launched into a cloud.
Dzambo, A. M., and Turner, D. D. (2016), Characterizing relative humidity with respect to ice in midlatitude cirrus clouds as a function of atmospheric state, J. Geophys. Res. Atmos., 121, 12,253– 12,269, doi:10.1002/2015JD024643.
Also consider the role of their “lag time” correction, which is also in this section.
L82: Where in the world are these 374 land stations? A few examples would be good to note here for the reader.
L90: technical correction: “... we also investigate the relationship between CBH, surface meteorology, and moisture.”
Section beginning at L95: There is a very important piece of information missing from this section... the manufacturing information of all radiosondes used in your database. Were these radiosondes Vaisala RS-92? Vaisala RS-41? Because your results are very sensitive to the relative humidity measurements of the radiosonde, it is also necessary to know what humidity sensors are on each radiosonde, and by extension, it is further necessary to know and understand the relative humidity uncertainty with each. There are numerous studies discussing the topic about relative humidity corrections in radiosondes. Vaisala RS-92 radiosondes have a very well documented dry bias in their measurements, and to the extent of my knowledge, only the GRUAN database of radiosondes have their humidity products homogenized between different versions. I strongly recommend updating this section of the paper with at least a paragraph discussing the humidity measurements, as well as adding a table of the different sensors from each manufacturer, perhaps something like: manufacturer, years used, reference for sensor, instrument uncertainty, and (if applicable) known biases and corrections such as those for the RS-92.
Wang, J., L. Zhang, A. Dai, F. Immler, M. Sommer, and H. Vömel, 2013: Radiation Dry Bias Correction of Vaisala RS92 Humidity Data and Its Impacts on Historical Radiosonde Data. J. Atmos. Oceanic Technol., 30, 197–214, https://doi.org/10.1175/JTECH-D-12-00113.1.
Miloshevich, L. M., Vömel, H., Whiteman, D. N., and Leblanc, T. (2009), Accuracy assessment and correction of Vaisala RS92 radiosonde water vapor measurements, J. Geophys. Res., 114, D11305, doi:10.1029/2008JD011565.
Vömel, H., and Coauthors, 2007: Radiation Dry Bias of the Vaisala RS92 Humidity Sensor. J. Atmos. Oceanic Technol., 24, 953–963, https://doi.org/10.1175/JTECH2019.1.
Dzambo, A. M., Turner, D. D., and Mlawer, E. J.: Evaluation of two Vaisala RS92 radiosonde solar radiative dry bias correction algorithms, Atmos. Meas. Tech., 9, 1613–1626, https://doi.org/10.5194/amt-9-1613-2016, 2016.
Jensen, M. P., Holdridge, D. J., Survo, P., Lehtinen, R., Baxter, S., Toto, T., and Johnson, K. L.: Comparison of Vaisala radiosondes RS41 and RS92 at the ARM Southern Great Plains site, Atmos. Meas. Tech., 9, 3115–3129, https://doi.org/10.5194/amt-9-3115-2016, 2016.
de Boer, G., Calmer, R., Jozef, G. et al. Observing the Central Arctic Atmosphere and Surface with University of Colorado uncrewed aircraft systems. Sci Data 9, 439 (2022). https://doi.org/10.1038/s41597-022-01526-9 (see methods section of this paper)
These are papers that should provide good context for RS41 and RS92 humidity measurements. As for the other radiosondes that may have been used in your study, please search for and add documentation similar to what these studies have in addressing humidity measurements.
L125: The comment here about the ERA-Interim is unnecessary.
L130: The inclusion of soil moisture content as part of your analysis is interesting, but can you provide context (perhaps a reference or two) showing how this ERA-5 variable was used in previous studies (especially for surface latent fluxes, clouds, or something similar).
L143: I am not convinced this is the best version of RH with respect to ice to use. Murphy and Koop (2005) did an extensive review of the available RH(ice) equations, pointing out an error (at the time) in the World Meteorological Organization’s primary equation. Review this paper, and at minimum, comment on how this choice of equation might vary from the other formulations listed here. Goff and Gratch (1946) is very commonly used.
Murphy, D.M. and Koop, T. (2005), Review of the vapour pressures of ice and supercooled water for atmospheric applications. Q.J.R. Meteorol. Soc., 131: 1539-1565. https://doi.org/10.1256/qj.04.94.
L191: Do you mean to say “Otherwise, this layer is discarded from the analysis”?
L192: I already mentioned this once, but it might be worthwhile referring to Dzambo and Turner (2016) and using their time-lag correction for collocating radiosonde and ground-based radar measurements. With merged clouds, I don’t think it would change your result much given the correlation is quite high already.
L192 (technical correction): Just say “To obtain robust cloud structures, …"
L223: I would say “accurately” over “correctly”, since both instruments are limited in attaining truly “correct” measurements.
L225: Do you mean R^2 values?
L228-230: This is inaccurate. ERA-5 assimilates geostationary satellite radiance measurements, which are used for a host of applications. Review Hersbach et al. (2020) more carefully, particularly the sections discussing satelltie radiance assimilation and the cloud parameterization schemes used to evolve clouds in their output.
Hersbach, H, Bell, B, Berrisford, P, et al. The ERA5 global reanalysis. Q J R Meteorol Soc. 2020; 146: 1999– 2049. https://doi.org/10.1002/qj.3803.
L238-240: I don’t doubt these conclusions, however, discussing the cloud parameterization schemes used for ERA-5 would make these statements more convincing to the reader, because it would help explain why/how cloud base heights in ERA-5 were lower than CloudSat/CALIPSO. Also keep in mind that CloudSat has a blind zone below 750m (see the Stephens et al. Reference you already cited).
L243-244: Is this true globally, or just the regions where the radiosonde data were available?
L336: Cloud bases above the tropopause are often the result of “overshooting tops” from deeply penetrating cumulonimbus (thunderstorms).
Homeyer, C. R., and M. R. Kumjian, 2015: Microphysical Characteristics of Overshooting Convection from Polarimetric Radar Observations. J. Atmos. Sci., 72, 870–891, https://doi.org/10.1175/JAS-D-13-0388.1.
L349: You mean to say “East Asia”. Please check your manuscript for technical, grammar and spelling errors, as I have noticed several to this point in the manuscript.
L353-355: I agree with this conclusion.
L386: “cloud base for clouds...”
L389-390: This is a good result, and consistent with many previous studies.
L398-399: This is because the western US is often dry near the surface, hence boundary layer heights tend to be much deeper. I would add this information to this part of the text.
L416: I agree.
L420-450: Referring to my previous comments about RH(ice), it would be good to note layers where RH(ice) exceeds 100%. Several studies note the presence of “subvisible” cirrus, which to this point your study does not mention. Subvisible cirrus are typically contained to the tropics, and worth elaborating here in perhaps 1-2 sentences. Additionally, analyzing RH(ice) would add very scientifically interesting detail to your study by identifying which climates have the most frequent ice saturation observations, which is extremely important for ice cloud formation.
Section 3.6: I generally agree with the conclusions presented in this section.
The conclusions section is a good summary, though may need to be updated depending on what the authors choose to do in addressing my comments across the previous sections.
Citation: https://doi.org/10.5194/egusphere-2023-472-RC2 - AC2: 'Reply on RC2', Jianping Guo, 04 Aug 2023
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