the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 14 Mar 2025
Atmospheric Boundary Layer in the Atlantic: the desert dust impact
Abstract. We investigate the dynamics of the atmospheric Boundary Layer (BL) over the Atlantic Ocean, with a focus on the region surrounding Cabo Verde during the Joint Aeolus Tropical Atlantic Campaign (JATAC), using a combination of ground-based PollyXT and Doppler lidars, satellite lidar data from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), radiosondes, and the model outputs of the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). The comparison of CALIPSO lidar results with ECMWF/IFS reanalysis for 2012–2022, revealed strong correlations for BL top over open ocean regions but weaker relation over dust-affected areas closer to the African continent. In these regions, space lidar indicated lower BL tops during daytime than those estimated by ECMWF/IFS. Observations in Cabo Verde highlight distinctive Marine Atmospheric Boundary Layer (MABL) characteristics, such as limited diurnal evolution, but also show the potential for BL heights to reach up to 1 km, driven by factors like strong winds that increase mechanical turbulence. Additionally, the challenges in estimating the BL height using lidar-derived aerosol mixing height versus profiling of meteorological parameters acquired from radiosondes are illustrated, examining cases with strong and weaker inversions that affect the vertical mixing and the penetration of dust particles within the BL. The findings underline the need for further improvements in the ECMWF/IFS reanalysis model towards capturing the complex interactions between marine and dust-laden air masses over the Atlantic, which are essential for constraining the dynamic processes in BL and aerosol-cloud interactions.
- Preprint
(30630 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 14 May 2025)
-
RC1: 'Comment on egusphere-2025-1105', Anonymous Referee #1, 11 Apr 2025
reply
The paper examines the Boundary Layer over marine and West African regions. It utilizes various instruments to compute the Planetary Boundary Layer Height (PBLH) using both space-based and ground-based measurements, along with ECMWF outputs. The study discusses the similarities and differences among the various technologies and retrieval algorithms employed. It effectively characterizes the horizontal variability of the PBLH in marine and land regions during September and compare it to ECMWF retrievals. However, questions about the methodology and robustness of some analyses are problematic.
Major comments:- Why did you choose to use Relative Humidity (RH) to calculate the PBLH from sonde measurements, instead of using potential temperature profiles? Potential temperature is typically the more common variable utilized to derive the PBLH from radiosondes. RH measurements are often avoided due to their higher uncertainty, making them less reliable compared to potential temperature (see Liu and Liang, 2010).
- Is there any reason why you chose to do these analyses in September? It would also be worthwhile to evaluate other months, especially when the SAL activity typically ramps up between mid-June and mid-August.
- While the slope of a linear regression and the correlation coefficient are related, they are not same. The authors argue that there is a significant correlation among the different retrieved PBLHs. However, they fail to include any correlation coefficients, basing their claims solely on the slope. Additionally, the analyses presented in Sections 3.1 and 3.2 would benefit from the inclusion of scatter plots and correlation coefficients (or the R² value from the linear regression) to better illustrate the agreement between the CALIPSO and ECMWF data. Although the mean values may be similar, the variability from day to day can differ significantly.
- Constraining your analyses to only September limits your investigation's robustness, especially in section 3.3. In the radiosonde measurements, you only have 3 cases, which does not allow you to even make any meaningful conclusion about the relationship between the retrieved PBLH from the radiosonde and CALIPSO.
- I think the paper would benefit from a richer discussion about the improvements needed in ECMWF in representing the PBLH, especially during the continental region of West Africa, where you observed the highest differences.
Minor comments
- Although the authors mentioned the instruments and techniques used to compute the PBLH, the description of the methodology is missing some information that will allow its reproduction. In particular, the WCT is very sensitive to the dilation factor in the Haar function, but this term is not included in the methodology section, nor are the integration limits. In addition, people typically preprocess lidar data before computing the PBLH, either by interpolating between pressure intervals or horizontally averaging to increase the SNR. I wonder if the authors made anything like this
- L50: Spaceborne lidar signal not only attenuates as it approaches the surface due to the presence of clouds. The weakened return signals result from longer travel distances from the satellite platform to the earth’s surface, which lead to a lowered SNR.
- Figure 3: The authors mentioned that the PBLH was retrieved using both the WCT and the gradient methods for Lidar measurements. However, in Figure 3, the authors only showed the WCT for the PollyXT and the gradient for CALIPSO.
- In line with the previous comment, you should also include in Figures 5 and 6.
the retrieved PBLH from the WCT for the CALIPSO data.
- Why did you choose to use Relative Humidity (RH) to calculate the PBLH from sonde measurements, instead of using potential temperature profiles? Potential temperature is typically the more common variable utilized to derive the PBLH from radiosondes. RH measurements are often avoided due to their higher uncertainty, making them less reliable compared to potential temperature (see Liu and Liang, 2010).
Citation: https://doi.org/10.5194/egusphere-2025-1105-RC1
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 127 | 27 | 6 | 160 | 3 | 4 |
- HTML: 127
- PDF: 27
- XML: 6
- Total: 160
- BibTeX: 3
- EndNote: 4
Viewed (geographical distribution)
| Country | # | Views | % |
|---|---|---|---|
| United States of America | 1 | 61 | 32 |
| Greece | 2 | 44 | 23 |
| France | 3 | 23 | 12 |
| Germany | 4 | 10 | 5 |
| Cyprus | 5 | 6 | 3 |
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
- 61