the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 13 Dec 2024
A New Technique to Retrieve Aerosol Vertical Profiles Using Micropulse Lidar and Ground-based Aerosol Measurements
Abstract. Accurately characterizing the vertical distribution of aerosols and their cloud-forming properties is crucial for understanding aerosol-cloud interactions and their impact on climate. This study presents a novel technique for retrieving vertical profiles of aerosols, cloud condensation nuclei (CCN), and ice nucleating particles (INPs) by combiningmicropulse lidar, radiosonde, and ground-based aerosol measurements. Herein, the technique is applied to data collected by our team at Texas A&M University during the Tracking Aerosol Convection Interactions ExpeRiment (TRACER) campaign. Aerosol size distribution and CCN counter data are used to estimate the value of the aerosol hygroscopicity parameter, κ. The derived κ, together with Mie scattering theory and the relative humidity profiles from the radiosonde, are then used to estimate how much the aerosols have grown at each altitude. This estimate is applied inversely to the aerosol backscatter coefficient profile to produce a dry aerosol backscatter coefficient profile. The dry aerosol backscatter coefficient profile is used to linearly scale surface measurements of aerosol, CCN, and INP concentrations. Combining lidar and ground-based aerosol measurements reduces the assumptions typically needed in lidar-based aerosol retrievals, resulting in a more accurate representation of vertical distributions of aerosol properties. The method could be readily applied to measurements in future field campaigns.
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RC1: 'Comment on egusphere-2024-3363', Anonymous Referee #1, 03 Apr 2025
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The manuscript presents a methodology to convert lidar backscatter profiles into cloud condensation nuclei (CCN) and ice nuclei (IN) number concentration profiles by scaling the lidar-derived profiles to in situ data measured at ground level. It describes the method with emphasis on the lidar analysis, cloud screening, and hygroscopic correction, and demonstrates its application through several case studies. Additionally, it compares measurements from two locations to evaluate the approach's potential. While the manuscript is well written, it lacks essential details regarding the methodology, particularly in describing error sources, and requires revisions from the authors.
Major Comments:
Although correcting for hygroscopic growth is a necessary step in converting backscatter profiles into number concentration profiles, the manuscript devotes excessive attention to this aspect while providing limited discussion on the relationship between aerosol optical and microphysical properties. It also offers technical details regarding the lidar data analysis, but omits essential aspects of the retrieval scheme.
A major drawback, in my opinion, is the large uncertainty in lidar backscatter retrieval at near ranges. While the authors perform an uncertainty analysis, they do not account for uncertainties arising from the overlap function, which can significantly affect the measurements in the near range. This issue is particularly relevant for a micropulse lidar (MPL), which typically has a full overlap height of about 5–6 km (Campbell et al., 2002), and around 2 km for MiniMPL (as I could only find here: https://www-air.larc.nasa.gov/missions/discover-aq/docs/pub/AMS_Berkoff_Finalv2.pdf ).
Campbell, J. R., D. L. Hlavka, E. J. Welton, C. J. Flynn, D. D. Turner, J. D. Spinhirne, V. S. Scott , and I. H. Hwang: Full-Time, Eye-Safe Cloud and Aerosol Lidar Observation at Atmospheric Radiation Measurement Program Sites: Instruments and Data Processing. J. Atmos. Oceanic Technol., 19, 431–442, https://doi.org/10.1175/1520-0426(2002)019<0431:FTESCA>2.0.CO;2, 2002
Another missing aspect is a discussion on the scaling of the backscatter coefficient to CCN/INP values. Despite having time-resolved lidar and in situ measurements, the study scales a single profile to a single CCN/INP value. How large was the scaling factor at different supersaturations, i.e., CCN/β_dry(R_0) and INP/β_dry(R_0)? More importantly, how did this factor evolve with time? Was there a correlation between backscatter values and CCN concentrations over time? Could you identify distinct patterns for different aerosol types? Addressing these questions could provide insight into the feasibility of retrieving CCN/INP profiles using lidar alone, as intended in previous studies referenced in the introduction.
Specific Comments:
Lines 78-79: Another commonly considered aerosol type is marine aerosol.
Lines 124-126: You mention that models and observations often rely on assumptions that introduce biases. Could you provide references to support this statement?
Line 166: You scale the aerosol backscatter to CCN and INP concentrations at ground level. However, how does this scaling evolve over time? What is the scaling factor? This crucial aspect is missing from the discussion.
Equation (2): Please use \exp instead of exp.
Figures 4e, 7, and 8: The profiles exhibit a steady increase in signal and backscatter products from 3 to 8 km. Is this a systematic artifact? Please address this pattern, which is visible in most presented profiles. Could it be associated with the overlap correction or other corrections or filtering?
Line 379: You assume a refractive index value. How does this assumption impact the humidity-effect correction? How much would your results change if the real part varied between 1.35 and 1.55? What about variations in the imaginary part?
Line 374: The step of computing aerosol size growth once the kappa value is determined is critical. I recommend including more details about this calculation beyond citing Petters and Kreidenweis (2007), such as providing the corresponding expression(s) or procedures.
Line 376: Please describe the Mie simulations in more detail. Which Mie package or code was used? Including the corresponding mathematical expressions would also be beneficial.
Line 381: Do you mean that aerosol size distributions are measured under dry ambient conditions? I assume the particles are dried before size distribution measurements, making the process independent of air humidity. Please clarify.
Line 391: The choice between the extinction and backscatter coefficient is important. While a constant lidar ratio holds for a given humidity, varying humidity also alters the lidar ratio, affecting the growth correction factor differently for the extinction and backscatter coefficients. Additionally, since the backscatter coefficient is the robust retrieved property, your Mie calculations should consider the backscattering cross-section instead of the extinction cross-section.
Lines 429-431: The quadratic fitting used to extend the retrieval beyond blind ranges appears to be an extrapolation. Could you provide more details on how this step works?
Figure 7c, 8d, 9d, 10d: I find it difficult to interpret these profiles. While I understand that you scale to different INP values at different temperatures, the approach differs from CCN concentrations, where supersaturation determines the number. Since INP concentration depends on air temperature, there should be a single INP profile based on the dry backscatter value and temperature. However, this relationship has not been discussed in the manuscript. Further analysis or clarification is needed for the INP retrievals.
Lines 533-534: You attribute differences between the two systems to slight misalignment. Could you clarify which misalignment you are referring to? Since MPL systems typically have a transceiver configuration, how would misalignment affect the near-range signal but not the far-range? These differences might instead stem from errors in the overlap function used to correct the signals. The increased signal between 6 and 8 km in the MiniMPL lidar suggests possible system artifacts (Fig. 11). When presenting the lidar analysis, I recommend including signals before and after overlap correction, as well as the overlap function itself. Additionally, how stable is this function over time?
Reference List: The absence of URLs for each reference makes reading and reviewing more difficult. Additionally, some journal names are incomplete.
Citation: https://doi.org/10.5194/egusphere-2024-3363-RC1
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