the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Jun 2025
Identifying MBL cloud boundaries and phase over the Southern Ocean using airborne radar and in-situ measurements during the SOCRATES campaign
Abstract. The Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) was an aircraft-based campaign (Jan 15 – Feb 26, 2018) using in-situ probes and remote sensors, targeting low-level clouds over the Southern Ocean (SO). A novel methodology was developed to identify cloud boundaries and classify cloud phases in marine boundary layer (MBL) clouds using airborne HIAPER Cloud Radar (HCR) and in-situ CDP+2D-S measurements. Cloud boundaries were determined using HCR reflectivity and spectrum width gradients. Single-layer low-level clouds accounted for ~85 % of observed cases. HCR-derived boundaries showed decent agreement with the Ceilometer and Micropulse lidar (MPL)-measurements during the Measurement of Aerosols, Radiation, and Clouds (MARCUS) ship-based campaign, with mean base and top differences of 0.04 km and 0.29 km. Additionally, HCR-derived cloud base heights correlated well (R = 0.78) with HSRL observations. A reflectivity–liquid water content (Z-LWC) relationship, LWC = 0.70Z0.29, was derived to retrieve LWC and liquid water path (LWP) from HCR profiles. The estimated LWP closely matched MARCUS microwave radiometer (MWR) retrievals, with a mean difference of 9.24 g/m². Cloud phase was classified using HCR-measurements, temperature, and LWP. Among single-layered LOW clouds, 48.8 % were classified as liquid, 23.3 % mixed-phase, and 6.9 % ice, with additional categories identified: drizzle (16.2 %), rain (3.4 %), and snow (1.5 %). The classification algorithm demonstrated over 90 % agreement with established phase detection methods. This study provides a robust framework for boundary and phase detection of MBL clouds, offering valuable insights into cloud microphysical processes over the SO and supporting future efforts in satellite algorithm development and climate model evaluation.
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Status: open (until 08 Jul 2025)
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RC1: 'Comment on egusphere-2025-874', Anonymous Referee #1, 18 Jun 2025
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1. Overview of the paper
This study analyzes low-level marine boundary layer (MBL) clouds over the Southern Ocean using airborne radar and in-situ data from the SOCRATES campaign. It introduces a method to detect cloud boundaries with radar reflectivity and spectrum width, derives an empirical relationship between reflectivity (Z) and liquid water content (LWC), and classifies cloud phases using radar, temperature, and LWP. Single-layer low clouds (<3 km) made up ~85% of cases. The Z–LWC relation enabled accurate LWP estimates, validated against observations. The cloud phase classification identified 48.8% liquid, 23.3% mixed-phase, and 6.9% ice clouds, and showed good agreement with other established methods. Overall, the study offers a robust radar-based approach for cloud boundary and phase detection, supporting better satellite retrievals and climate modeling over the Southern Ocean.
This resubmitted version of the study presents clear improvements both in writing quality and in the validation of the proposed methodology. The structure is more refined, and the explanations are clearer, which enhances the overall readability of the manuscript. One of the major additions is the comprehensive comparison with multiple existing methods for cloud boundary characterization. This significantly strengthens the robustness of the authors' approach. Based on these elements, I would recommend only minor revisions before final publication.
2. General suggestions
The scientific objective is clear, and the methodological approach is generally robust, particularly the development of a radar-based classification for cloud phase. The Supplement is clear and provides important supporting information, especially concerning the in-situ datasets and instrumentation details.
However, several issues hinder the manuscript’s clarity and accessibility. First, the text suffers from a lack of consistency in terminology and definitions—key terms and acronyms such as SLW or LOW are introduced without prior explanation. Secondly, there are redundancies and repetitions across the sections, especially in the presentation of the classification results, which could be streamlined for conciseness. Additionally, while the discussion around uncertainties is both important and informative, the writing would benefit from reformulation for clarity and structure. On the methodological side, the rationale for excluding certain cloud types (e.g., fully glaciated clouds) should be more explicitly justified, especially in the context of deriving a Z-LWC relationship. Finally, more attention should be given to harmonizing visual elements (e.g., figure fonts and legends) and providing clearer context in tables and figures, including the comparison of cloud boundaries with in-situ data.
Overall, while the work is of clear interest to the cloud microphysics and remote sensing communities, the revised version appears well-refined, with notable improvements in structure, clarity, and scientific rigor.
3. Specific comments and technical corrections
Abstract.
L.26 : If you are referring to your “LOW” class, you need to define the acronym before using it. Otherwise, you can remove the capital letters or use “low-level clouds”.
Introduction.
L.35 : This might not be necessary. However, the references are neither in chronological order nor in alphabetical order.
L.38 : "SLW" is not defined before being used.
L.53–60 : This paragraph on uncertainties is important and interesting but not very clear. The authors could try to rephrase it.
L.63 : This is a long sentence (6 lines); please split it into two.
L.73 : It’s a bit vague—what were these inconsistencies?
Results.
L.131 : Maybe merge this sentence with the one on L.127.
L.149 : The ice phase (> 200 µm), although minor, is not used in your method? Is it only useful for comparing your classification with Alessandro’s?
L.174 : When you refer to a “subset of the dataset”, does this correspond to the “5th to 95th percentile interval of the dataset”?
L.178 : “FIGURE” → “Figure”
L.244 : No space between ~ and 22.2%.
L.256 : This is essentially what you say in L.226 — consider avoiding the repetition.
L.265 : You should add the term “mean” as in your table, otherwise it’s confusing — it sounds like you’re referring to maximum values.
L.295 : How do you explain a larger mean difference for cloud top altitude?
L.314 : “FIGURE” → “Figure”
L.319–328 : Formatting issue in the label of Figure 4 and repetition in the text. Please correct this paragraph.
L.324 : I understand the importance of stating that you focus on low-level clouds, but this is repeated too many times.
L.324 : Repetition — please revise.
L.336 : The authors refer to drizzle in both the text and Figure 5. You mention both “drizzle” and “liquid drizzle” — what is the difference? When you say “drizzle”, are you referring to freezing drizzle?
L.330–376 : In this paragraph, the authors mention several thresholds for classifying phases and for Figure 6. Some thresholds are cited with references (Wu et al., 2020a; …) and others are not — how were these chosen?
L.377 : Does this kind of “misclassification” occur frequently? Can the authors quantify this? Does a cloud with LWP ≥ 20 g/m² and LWCs > 0.2 g/m³ necessarily exclude the presence of ice?
L.448 : The authors could add a sentence at the beginning of paragraph 4 stating that only low-level clouds are used, in order to avoid repeated reminders later.
L.481 : The concept of scale plays a crucial role in method comparison — the authors should emphasize this point more clearly in the text.
L.525 : The authors should describe Table 2 in more detail. Otherwise, it may not be necessary to keep it in the main text; consider moving it to the supplementary materials, since you only mention it once.
L.574 : The percentages are calculated on classes that are not present in both methods (Mix % and Melting %), which could explain the larger differences.
L.582 : Exactly — you could perhaps add that the mixed-phase strongly depends on the observation scale (microphysical <--> macrophysical).
Figures.
Figure 3 : The font size for y-ticks is not the same in panels f and h. You could also increase the x-ticks size in panel h to match the one in subplot d.
Figure 4 : There is an issue in the label: “4 Low-level cloud phase classification method and discussion”.
Citation: https://doi.org/10.5194/egusphere-2025-874-RC1
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