the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Dec 2024
A method to retrieve mixed phase cloud vertical structure from airborne lidar
Abstract. A technique was developed to provide cloud phase information using data collected by the NASA Langley airborne High Spectral Resolution Lidar systems with a particular emphasis on mixed phase cloud conditions, where boundaries and gradients in the distribution of ice and liquid water are critically important for microphysical and radiative processes. The method is based on the established use of depolarization to identify ice particles but incorporates a new method to separate the ice depolarization from the depolarization produced by multiple scattering in dense liquid clouds. Clouds assured to be liquid-only based on ambient temperature were used to train an empirical model of the multiple scattering depolarization that results at different ranges from the lidar. The method classifies lidar observations as liquid dominant, mixed phase and ice dominant and has an additional categorization for oriented ice. For evaluation of the retrieval, a two aircraft approach was used with the lidar observing the same clouds that were concurrently sampled with in situ microphysical probes. Aircraft matchups were able to track the individual cloud elements and capture marked changes in the distribution of liquid and ice across flight segments of typically 20–100 km. Qualitative features relating to localized changes in the cloud top temperature, cloud morphology and convective circulations were generally replicated between the lidar phase classification and the in situ microphysical data. Quantitative evaluation of the phase classification was carried out using a subset of fifteen cloud scenes that satisfied strict aircraft colocation and microphysical requirements. Using the in situ microphysical data, it was found that ice extinction fractions of 14 % and 76 % most closely matched the upper and lower bounds of the lidar mixed phase classification.
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Status: open (until 24 Jan 2025)
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RC1: 'Comment on egusphere-2024-3844', Anonymous Referee #1, 16 Jan 2025
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First review of “A method to retrieve mixed phase cloud vertical structure from airborne lidar” by Crosbie et al.,
January 15, 2025
Reviewer Recommendation:
Somewhere between Minor and Major revisions required
Summary:
The submitted manuscript introduces an empirical model in an attempt to separate the origin of depolarization observed with a lidar. In my understanding, the authors take observations that are expected to be from liquid only clouds, as screened by temperature, and tune an empirical model to tightly match the depolarization profile observed. This model accounts for depolarization due to multiple scattering. When taking the same model and applying it to clouds that are not necessarily expected to be liquid only, deviations are then attributed to ice. In general, this approach seems reasonable and caveats and assumptions are fairly well documented.
In a world that is often filled with algorithms that over promise and under deliver, this manuscript is, in my opinion, candid with limitations and expectations. I don’t often get to write that and I think it is important to note. In general, the paper is well written; it has generally excellent clarity and citations are appropriate in my opinion. It fits well within my understanding of the scope of AMT and I believe it is a contribution that should be published.
I note some elements that I find unclear, and I believe some revisions will improve the draft. That said, it is my recommendation that this manuscript be accepted with revisions (somewhere between major and minor).
Major Comments:
- My prime concern is that I am unclear how far to trust the validation of this technique. In general, I am sympathetic to the fact that you are using airborne measurements and that they are generally infrequent (especially because in this case you require 2 planes in the same patch of sky simultaneously) and you are trying to see into a region where few technique exist. However, in Figure 6 & 7 the flight track of the Falcon and your measurements seem to be worryingly far away for a worryingly large fraction of time. This seems to be the crux of the problem: lidar has poor penetration depth into clouds but you need coincident measurements to validate the technique. My specific comments/questions are:
- It might be best to specify how much total data overlap there is (in terms of hours or kilometers or measurements or something else), i.e. when the Falcon is within the realm of your measurements that you trust (to my novice eye, 15-20 km range on Figure 6 seems to be reasonable overlap but there seems to be almost none from 30-40 km on the same figure).
- Lines 428-429: It is clear to me that you are picking +-300 meters as a threshold because you need to have some reasonable sample size. However, it is unclear to me how sensitive to this limit (+- 300 meters) your results are? 300-meter penetration into liquid clouds seems optimistic. Your Figure 3 & 4 suggests that your penetration depth into clouds may reach this threshold but can also be limited to 50-60 meters. As I read this, I think either: 1) your results need to be shown to be robust for various depths of observation or 2) the sensitivity to that change needs to be established. At minimum, if you use a more restrictive threshold, I believe you need to comment on what it does to your interpretation of results.
- Line 380-382: Given that you are using these examples as validation for your method, it seems like a non sequitur to specify that the method works and that the Falcon’s data can’t be used because it was too far away.
- I presume the GRD case from Table 1 has no flight validation data. Is that true? If that is true…is it a fair control case? If not, you should more clearly say how that data is compared/validated. For example..where is your temperature data coming from?
- Section 2.3.1 and Lines 412-414: My understanding of the observation of oriented ice crystals is that their properties are highly dependent on the angle of observation. For example looking at Noel and Chepfer 2010, it is my understanding that a change from 0.3 degrees from nadir and 3 degrees from nadir basically removed CALIOP’s sensitivity to oriented ice crystals. With that being said, are your observational legs done at nadir exactly or is there some angle of attack? It the plane pitching/rolling in any way that would change your sensitivity? Does that affect your measurements of oriented ice crystals? A comment clarifying your sensitivity would be warranted in my opinion.
Noel, V., and H. Chepfer (2010), A global view of horizontally oriented crystals in ice clouds from Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), J. Geophys. Res., 115, D00H23, doi:10.1029/2009JD012365
Minor Comments:
- Lines 122-124: You mention cross talk in the molecular channel due to imperfect extinction by your HSRL filter. This physical effect is absent your equation 2. I would suggest including that term.
- Equations 10/11/13: It is unclear to use the same variable (z) as both the integrand and the upper bound of integration. Suggest integrating over z’ or some other stylized version to differentiate your two variables.
- Line 192: I would expect this value to be lower (like maybe 8 to 10). I think therefore that a citation for the lidar ratio used here is needed.
- Table 1: What is “Cloud Temperature”? Is it really cloud-top temperature or average temperature or something else? Please clarify.
- Table 2: I would suggest adding the RMSE value that would be calculated if using your “Final” coefficient values as another row. It would be helpful to see how much your averaged coefficients alter the fit to each test case.
- Table 2: Why is your GRD case included in Table 1 and not here?
- Figure 2: I would suggest changing your colors for each case to perhaps use different symbols (diamonds, triangles and so forth). For example, I am having a hard time differentiating ACTHIGH and GRD. Additionally, I am not Red-Green colorblind but CPEX and ACTLOW1 might be difficult to differentiate for someone that is.
- Figure 3: Why is there a huge gap in measurements from 60 meters to 120 meters? Is that last data point truly trustworthy or are your data filters kicking in with just one point serendipitously falling into the end?
- Figure 3: It might be helpful to add an ordinate axis for optical depth instead of just using physical depth.
- Figure 3 & 4: I would include in the caption what the difference between gray and blue does is (specifically looking at panels C/F/I). I presume it is for observed and modeled depolarization, respectively, but it would be good to clarify.
Citation: https://doi.org/10.5194/egusphere-2024-3844-RC1 - My prime concern is that I am unclear how far to trust the validation of this technique. In general, I am sympathetic to the fact that you are using airborne measurements and that they are generally infrequent (especially because in this case you require 2 planes in the same patch of sky simultaneously) and you are trying to see into a region where few technique exist. However, in Figure 6 & 7 the flight track of the Falcon and your measurements seem to be worryingly far away for a worryingly large fraction of time. This seems to be the crux of the problem: lidar has poor penetration depth into clouds but you need coincident measurements to validate the technique. My specific comments/questions are:
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