the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Apr 2026
Aerosol Vertical Distributions Shaped by Boundary Layer Dynamics in a Coastal Urban Environment: Insights from the TRACER Campaign
Abstract. Aerosol vertical distributions are a major source of uncertainty in quantifying aerosol–cloud–climate interactions. Using observations collected during the 2022 TRACER campaign in Houston, Texas, we investigate how Atmospheric boundary layer dynamics shape the vertical structure of aerosol populations in a coastal urban environment. Our lidar retrieval combines micropulse lidar backscatter with ground-based aerosol measurements to obtain aerosol concentration profiles. We introduce a new parameterized fitting function that captures the characteristic S-shaped aerosol profiles associated with boundary layer processes. This parameterization is applied to case studies demonstrating how boundary-layer dynamics, including turbulent mixing, capping inversion strength, and sea-breeze circulations, govern aerosol vertical distributions. Finally, we estimate aerosol vertical profiles from boundary layer height and gradients in potential temperature profile using our proposed aerosol profile parameterization function. These findings provide a physically grounded parameterization for inferring aerosol vertical profiles in locations where aerosol sampling is limited to surface measurements.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-1748', Anonymous Referee #1, 26 May 2026
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RC2: 'Comment on egusphere-2026-1748', Anonymous Referee #2, 25 Jun 2026
Aerosol Vertical Distributions Shaped by Boundary Layer Dynamics in a Coastal Urban Environment: Insights from the TRACER Campaign
Chen et al., EGUsphere-2026-1748
Overview:
This manuscript applies a parameterization, developed in Chen et al., AMT, 2025, to represent vertical aerosol profiles during a TRACER campaign in Houston in the summer of 2022 using lidar remote sensing retrievals, meteorological profiles from a radiosonde and ground-based measurements of aerosol concentrations from a condensation particle counter. In this manuscript, the parameterization from Chen et al., AMT, 2025 was adapted to include the representation of aerosol layers aloft. Eight variables are used to represent a function that provides an accurate representation of different structures in vertical aerosol profiles including gradients at the surface and top of the boundary layer. The authors present three diverse case studies that describe common aerosol and meteorological conditions for the Houston region including 1) stratified aerosol layer beneath a strong inversion, 2) a boundary layer that develops and collapses, and 3) a case that demonstrates the evolution of the aerosol profile related to sea-breeze circulation. As part of the main focus of the manuscript, the authors estimate boundary layer height using two peer-reviewed methods – bulk Richardson number and potential temperature gradient to assess atmospheric stability and mixing of the aerosol across an inversion. BLH estimates are then compared to height of the lidar-retrieved aerosol gradient (r_m).
One of the main objectives of the manuscript is to develop a method to estimate vertical aerosol profiles combining ground-based measurements of aerosol and meteorological profiles from a radiosonde for situations where lidar observations or airborne measurements of aerosol are not available.
Main comments:
The main issue with the manuscript is that the authors note that the relationship between BLH and r_m is weak (r-squared < 0.2) and show in Figure 11 that most estimates of BLH are lower than the height of the lidar-retrieved aerosol gradient (r_m). Given the scatter of the data in Figure 11 (and Figure 12), the gray zone cannot represent the 95% confidence level for the range of BLH observed during the campaign. Consequently, applying a linear relationship between the two parameters (r_m = 0.27 * BLH + 1.25) as shown in Figure 11 is not informative – and the uncertainties of using meteorological profiles to estimate vertical profiles of aerosol concentrations need to be quantified (in Section 3.6).
Similarly, the authors hypothesize that stronger stability suppresses vertical mixing leading to sharper transitions in aerosol concentrations at the top of the boundary layer. However, Figure 12 also shows a weak relationship between stability (d_theta/dz) and transition gradient of aerosol concentrations (k) and contradicts the authors’ statements that d_theta/dz is a useful metric for representing the strength of the aerosol gradient.
While statistically significant (low p-values), the predictive power of the relationships using BLH and d_theta/d_z remains low. Section 3.6 then attempts to use the weak relationships (BLH vs r_m and d_theta/dz vs. k) to predict the vertical profiles of aerosols using meteorological profiles and measured aerosol number concentrations at the surface. The authors compare two predicted cases with lidar profiles (Figure 13) and acknowledge the limits in their proposed method of using estimated BLH values as a reliable predictor of r_m height and atmospheric stability (d_theta.dz) to represent the gradient (k) of aerosol concentrations at r_m.
The manuscript would benefit from an in-depth analysis of the uncertainties of using meteorological profiles as a sole basis for predicting vertical profiles of aerosol. In this context, Figure 7 shows that BLH estimates are much lower than r_m heights in three out of the four cases when BLH is defined by the stable layers closest to the surface. It would strengthen the manuscript in context of the stated objectives to explore the atmospheric conditions and estimates of BLH that define boundary layer height in terms of lidar retrievals for a viable estimate of r_m height and k.
Other comments:
The legend in the figures showing the retrieved vertical aerosol concentration profile (e.g., Figure 5) shows a gray shaded zone representing aerosol uncertainty, which appears vanishingly small. It seems the uncertainty is calculated based on variability in total backscatter coefficient (Chen et al., AMT, 2025). However, viable calculation of uncertainty related to aerosol number concentrations should also incorporate the variability and dependence of the aerosol size distribution, as well as hygroscopicity corrections. Values of aerosol hygroscopicity and corrections factors are not stated in the manuscript and cite unpublished work (personal communication).
One main assumption is that the aerosol size distribution remains the same vertically; however, previous experiments have shown large contribution of ultrafine aerosol in the Houston boundary layer, while layers aloft show the dominant presence of an accumulation mode (e.g., Brock et al., 1993; Lance et al., 2009). At the end of the manuscript, the authors recognize the need for in-situ observations of small aerosol particles that are not detected by elastic backscatter measurements from the lidar. How much does a change in the aerosol size distribution impact estimate of aerosol number concentrations? Have the authors compared the lidar-retrieved profiles of aerosol number concentrations with in-situ airborne observations?
In addition, the lidar profiles consistently show high aerosol concentrations at the surface – even for neutrally buoyant boundary layer (for example, Figure 10g) when the boundary layer is expected to be well-mixed. How much does the uncertainty in lidar backscatter retrieval at near ranges impact the estimates of the aerosol number concentrations?
The justification for choosing 10 cm-3 for the free troposphere aerosol number concentration needs to be cited.
Citation: https://doi.org/10.5194/egusphere-2026-1748-RC2
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