the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 26 May 2025
Wind-cloud interactions observed with Aeolus spaceborne Doppler Wind Lidar
Abstract. Model based studies have shown interactions between wind vertical profiles and cloudiness, but few observational studies corroborate them. The unique observations of Aeolus spaceborne Doppler wind lidar can contribute to fill this gap. In this paper, we merged global Aeolus observations of cloud profiles at full horizontal resolution (3 km along orbit track) with co-located profiles of horizontal winds.
We first observed wind-cloud interactions at regional scale over the Indian Ocean. Aeolus captures the strengthening of the Tropical Easterly Jet in early June 2020, with wind speeds exceeding 40 ms-1 in its core, and a simultaneous increase of high cloud fraction up to above 30 %, until the decay of the jet during fall.
Secondly, we observed wind-cloud interactions at cloud scale (between 3–100 km) in different regions. Over the Indian Ocean as well as over cumulus and stratocumulus dominated regions, we found that the wind shear in cloudy sky is always smaller than the wind shear in the clear sky surrounding the cloud (statistically significant). In addition, we found that the wind speed difference between the cloud and its surrounding clear sky increases with the clear sky wind shear, especially in cumulus (R=0.93) and stratocumulus (R=0.89) dominated regions. This study demonstrated that despite its coarse resolution, Aeolus can capture wind perturbations induced by convective momentum transport.
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RC1: 'Comment on egusphere-2025-2065', Anonymous Referee #1, 11 Jun 2025
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The manuscript by Titus et al. investigates wind-cloud interactions using data from the spaceborne Doppler wind lidar onboard the Aeolus satellite. To this end, Aeolus Level-1A data are processed to create a cloud mask at 3 km horizontal resolution, which is then used to resample the Aeolus wind data to a finer grid than that available in the original Level-2B wind product. The resampling approach is validated using airborne Doppler wind lidar measurements as well as a regional weather model simulation.
Using the resulting Aeolus cloud mask and wind data at 3-km resolution, the authors analyze the relationship between cloud cover, wind speed, and vertical wind shear within the troposphere during boreal summer 2020, at various spatial scales and across different global regions. Among other findings, they show that wind shear under cloudy-sky conditions is significantly smaller than in the surrounding clear sky. Moreover, the wind speed difference between cloudy and clear-sky conditions increases with the clear-sky wind shear, particularly in regions dominated by cumulus and stratocumulus clouds.
General comment:
This study is of significant interest to researchers in atmospheric physics, especially those studying global circulation and its interaction with cloud formation and development. The manuscript is well-structured, and the figures effectively support the analysis. The approach, starting from the Aeolus L1A product to derive a cloud mask at full 3-km resolution, is particularly innovative and promising.
However, the methodology outlined in Section 2.1 raises several questions and appears prone to potential systematic errors (see specific comments below). Similarly, the subsequent resampling of the Aeolus L2B wind data using the derived cloud mask needs further clarification to prevent misunderstandings about the approach.
While the use of airborne wind measurements and model simulations lends some confidence to the method, it is based on a few examples from a restricted geographical area. This raises the question of how representative these examples are for the much larger dataset analyzed later in the manuscript.
The results regarding wind-cloud interactions at both regional and sub-100 km scales are insightful and illustrate the capability of Aeolus to detect such phenomena, despite its relatively coarse resolution. As such, the study opens avenues for further research into topics such as jet stream shifts and their coupling with cloud radiative effects.
I recommend the manuscript for publication, contingent on the authors addressing the specific issues outlined below.
Specific comments:
- Lines 72 ff.: The authors correctly state that Aeolus measured the projection of the horizontal wind on the laser line-of-sight (LOS). However, the symbol u is used throughout the manuscript (e.g., in Eq. (3), Figs. 6–11) to denote the horizontal LOS (HLOS) wind speed, which may be misleading, as u traditionally refers to the zonal wind component. I suggest adopting the notation vHLOS, which is commonly used in Aeolus-related literature.
- Line 120: To my knowledge, the number of accumulated backscatter profiles averaged onboard is 18, not 16.
- Lines 140 ff.: Please provide a reference for the hot pixel map corresponding to the end of the mission. Since only a subset of these pixels was active during the main analysis period (primarily boreal summer 2020), discarding all of them may be unnecessarily conservative. This approach could introduce systematic errors in the retrieval of particulate and molecular backscatter profiles, and in turn affect the cloud mask. This is particularly relevant given that only very few pixels are used in the retrieval.
- 2.1 and Appendix A: The derivation of particulate and molecular backscatter from the L1A signal appears oversimplified. Notably: a) The correction for solar background is not mentioned, although it also contributes to the overall signal. b) The use of signals from 3 and 6 pixels as proxies for particulate and molecular backscatter is a rough approximation. In contrast, Donovan et al. (2024b) describe a more rigorous method that involves determining crosstalk coefficients and accounts for the non-uniform intensity distribution across the Mie spectrometer. While a full implementation may be beyond the scope of this paper, the authors should explicitly acknowledge these simplifications and discuss their potential impact on the uncertainty in the derived cloud mask.
- Can the authors please comment on how the backscatter from aerosols, e.g., Saharan dust, is classified by their approach. I suspect that such regions are classified as “clear-sky”. What are the implications on their studies of wind-cloud interactions?
- Line 180: How can the total backscatter take values of exactly zero? I assume that, in case of strong signal attenuation, the values would fluctuate around zero due to noise. If a threshold was applied to discriminate such values, please specify.
- 282: Please clarify the term “closest L2B wind.” Is proximity evaluated in both latitude and altitude, relative to the bin center? This is important when duplicating L2B wind values, which are themselves derived from grouped L1B bins. I recommend to refer to the bin grouping algorithm described in the L2B Algorithm Theoretical Basis Document which should also be cited in the manuscript: https://earth.esa.int/eogateway/documents/20142/37627/Aeolus-L2B-Algorithm-ATBD.pdf
- Line 296: Including statistics on how many of the resampled winds labeled as “clear” and “cloudy” correspond to L2B Rayleigh-clear and Mie-cloudy wind types would strengthen confidence in the cloud-masking approach.
- Line 325 ff.: Are the reported variations in wind speed shown in HLOS units?
- 5(c)-(e): The effective vertical resolution of the resampled winds appears similar to that of the L2B product. Is this due to wind speed duplication across adjacent 3-km bins? If so, this should be discussed. Additionally, a finer vertical resolution of 480 m was expected, but is not evident, particularly at higher altitudes.
- Lines 552 ff.: The statement that “even at the coarse vertical and horizontal resolutions of Aeolus, it is possible to capture significantly different wind speeds within clouds and within their surroundings in shallow convection” may be misleading given that Fig. 9 presents airborne lidar data. This data has much lower random errors than Aeolus which enables to resolve the depicted features. Please clarify this distinction.
Technical corrections:
- 270: Define the term “observation_type” and specify which values represent “cloudy” and “clear.”
- Ensure a space between numerical values and their units throughout the manuscript (e.g., “480 m” instead of “480m”).
- 7, 8: Extend the color scales to show the full range of wind shear (Fig. 7) and wind speed (Fig. 8), respectively.
- Caption of Fig. 8: Insert a semicolon after “February 2020” to improve readability.
- 528: Define the acronym “LES” at first use.
- 550: Add missing punctuation at the end of the sentence.
- Caption of Fig. 9: Specify that the data shown were acquired with the airborne Doppler wind lidar.
- 4.3.2: At the beginning of the section, restate the time period covered by the dataset to aid reader comprehension.
- 10: Use more distinct colors for “TrSc u_clear surrounding cloud up” and “Cu u_clear surrounding cloud up,” which are difficult to distinguish.
- Caption of Fig. B1: The last sentence appears incomplete. Please revise.
- Line 792: Correct to: “can be explained”
- B6: Ensure consistency between the labels in the figure and the text: the terms “U_clear_neighbor_cloud_up” and “U_clear_neighbor_cloud_down” differ from the “surrounding” terminology used elsewhere.
Citation: https://doi.org/10.5194/egusphere-2025-2065-RC1
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