the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 26 Sep 2023
A Survey of Radiative and Physical Properties of North Atlantic Mesoscale Cloud Morphologies from Multiple Identification Methodologies
Ryan Eastman
Isabel Louise McCoy
Hauke Schulz
Robert Wood
Abstract. Three supervised neural network cloud classification routines are applied to daytime MODIS Aqua imagery and compared for the year 2018 over the North Atlantic Ocean: The Morphology Identification Data Aggregated over the Satellite-era (MIDAS), which specializes in subtropical stratocumulus (Sc) clouds; Sugar, Gravel, Flowers, and Fish (SGFF), which is focused on shallow cloud systems in the tropical trade winds; and the community record of marine low cloud mesoscale morphology supported by the NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) dataset, which is focused on shallow clouds globally.
Comparisons of co-occurrence and vertical and geographic distribution show that morphologies are classified in geographically distinct regions: shallow suppressed and deeper aggregated and disorganized cumulus are seen in the tropical trade winds. Shallow Sc types are frequent in subtropical subsidence regions. More vertically developed solid stratus and open and closed cell Sc are frequent in the mid-latitude storm track. Differing classifier routines favor noticeably different distributions of equivalent types.
Average scene albedo is more strongly correlated with cloud albedo than cloud amount for each morphology. Cloud albedo is strongly correlated with the fraction of optically thin cloud cover. The albedo of each morphology is dependent on latitude and location in the mean anticyclonic wind flow over the N. Atlantic. Strong rain rates are associated with middling values of albedo for many cumuliform types, hinting at a complex relationship between the presence of heavily precipitating cores and cloud albedo. The presence of ice at cloud top is associated with higher albedos. For a constant albedo, each morphology displays a distinct set of physical characteristics.
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Ryan Eastman et al.
Status: open (extended)
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RC1: 'Comment on egusphere-2023-2118', Anonymous Referee #1, 21 Oct 2023
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Summary:
The authors compare three different supervised neural network classifications of low cloud morphologies in the North Atlantic. The geographic distributions, the overlap statistics and the radiative and physical properties of the different morphologies are discussed in detail. The authors find that the all-sky albedo is more strongly correlated to cloud albedo then cloud amount for nearly all morphologies, and that each morphology displays a distinct set of physical characteristics.
I find the paper to be very well-written and a suitable contribution to ACP. The analyses are carefully done and clearly explained. I only have some minor comments that I detail in the following.
Main comments:
- Some more comparison of MIDAS and MEASURES ‘shared’ categories: I was expecting that most differences between MIDAS and MEASURES is in the disorganized Cu type, which is distributed over more classes in MEASURES. However, there are very pronounced differences in the Open MCC class for example. I think the authors should analyse and explain these differences in a bit more detail. Fig. 3d for example shows that MEASURES hardly identifies Open MCCs. Do you understand why this is the case?
5 shows the overlap of MEASURES with MIDAS Open MCCs, but are there also cases where MIDAS detects Open MCCs but MEASURES doesn’t detect anything? And if so, what are the conditions / regions where this occurs? Also in e.g. Fig. 14, Open MCC from the MIDAS and MEASURES classifiers seem to be the furthest away compared to e.g. the closed and disorganized morphologies. Any ideas why this is so? - Seasonality & diurnality: As the authors have data for an entire year, I’d find it very interesting to see the seasonal cycle of morphology occurrence, e.g. in the subdomains shown in Figure 13. Likewise, if nighttime morphologies would be available, a few words on the diurnal cycles of the different classifiers would be very interesting.
- Rain rate data: I’d like to see 1-2 sentence near L166 regarding how well this routine for deriving rain rates works for the low clouds considered in the study. Especially since the authors find a ‘curious difference’ when comparing mean and peak OD versus rain rate (L305), I wonder whether this isn’t related to the way the rain rates are derived.
More specific comments:
- The SGFF morphologies are mostly written in italics in the manuscript, but not the other morphologies. I’d suggest to also write e.g. Suppressed Cu in italics.
- L33: Maybe talk about Stratocumulus and Cumulus as archetypal cloud types than rather cloud organizations?
- Paragraphs starting in L45 and L52: I’d suggest to switch the sequence of the two paragraphs, as the three routines are only introduced in L52 but already discussed in L45.
- L158: I didn’t fully understand that ‘spaced 333m apart along the satellite ground track’ refers to a horizontal spatial resolution of 333 m. Maybe rewrite.
- L254f: I am a bit surprised that SGFF doesn’t show a lot of within-routine overlap. Previous studies like Vial et al. (2021, https://doi.org/10.1002/qj.4103) mentioned a lot of overlap among SGFF morphologies. What is different here?
- Refer to some literature already in the results section: I’d suggest to add a reference to Mieslinger et al. 2022 in L274; a reference to the statement in L279 that “cloud amount as a proxy ....”; and a reference to McCoy et al. (2023) in L298.
- L280ff: I find the conclusion in L286 regarding the complex picture of morphology and location interesting, but wonder whether it needs Fig. 7-9 in the main text for this. Maybe a selection of the most important subplots is enough? There are already a lot of Figures with many panels and I find it hard to digest all the information. So this could be a good point to reduce information.
- L418ff: I don’t really see what you mean here, e.g. how we can see the change from stratiform types to Flowers from Fig. 5, and what suppressed Cu evolves into. Please clarify.
- L425f: Maybe good to mention cold pools as a potential driving process in this context.
- L428: This summary of Leahy et al. (2012) is confusing and seems to contradict what is written in the next sentence. Please rewrite.
- L453: perterbations --> perturbations
Comments on Figures:
- 1: some colors in panel a) differ from the colors of the three categories. Please explain. Also, please enlarge the axis labels and morphology legends.
- 2-4: I’d suggest to combine all of them in one figure, such that they don’t distribute over different pages. I’d also suggest to use a different color scale – for Fish it’s not visible easily if we’re at the lower or upper end of the range.
- 14 and 15: I find the comparison in these figures very interesting! Suggestions: Change MCC to MIDAS in the figure legends. And add in the caption what filled vs. hollow symbols refer to.
Citation: https://doi.org/10.5194/egusphere-2023-2118-RC1 - Some more comparison of MIDAS and MEASURES ‘shared’ categories: I was expecting that most differences between MIDAS and MEASURES is in the disorganized Cu type, which is distributed over more classes in MEASURES. However, there are very pronounced differences in the Open MCC class for example. I think the authors should analyse and explain these differences in a bit more detail. Fig. 3d for example shows that MEASURES hardly identifies Open MCCs. Do you understand why this is the case?
Ryan Eastman et al.
Ryan Eastman et al.
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