My previous comment appears as "CC", so I re-posted my comment as "RC" here.

Overview:

This paper presents CloudViT, a novel cloud classification method based on Vision Transformers (ViTs) and cloud properties derived from MODIS satellite data. The authors aim to classify cloud types across global datasets using spatial patterns of cloud properties such as cloud top height (CTH), cloud optical thickness (COT), and cloud water path (CWP). The method is evaluated on co-located ground-based observations and satellite data, producing accurate classifications of different cloud types. The approach is further tested with applications to General Circulation Models (GCMs), notably ICON-Sapphire, showcasing CloudViT's ability to generalize cloud type retrievals at kilometer-scale resolution.

CloudViT leverages self-supervised learning for pretraining and contrastive learning to overcome the limited number of labeled cloud observations. The method is robust, showing competitive performance when compared to traditional methods and CNN-based approaches, and effectively captures global cloud distributions, including complex cloud types like cumuliform and stratiform clouds. I think the paper is suitable for acceptance with minor revisions.

Minor Comments:

L142: Change "retrieved" to the verb form "retrieve."

L177: Replace "requires" with "require" to agree with the plural subject.

L209: In the sentence "this type of model, alongside CNNs, are," replace "are" with the singular verb "is" to agree with the subject "this type of model."

L323: Change "cardinal" to "cardinality" to correctly refer to the size or number of elements in a set.

L587-L593: I believe it would be beneficial to discuss the limitations, such as follows：

Since MODIS data is collected through near-nadir scanning, observations in high-latitude regions become oblique, leading to distortions and errors in cloud property retrievals, such as cloud top height and optical thickness. This could potentially affect the model’s performance in polar regions.

The paper shows results of using a ViT model that is pretrained on MODIS data to classify cloud scenes into 4/10 cloud types as defined by WMO. The authors go into great details at times on model training choices etc. The paper is, however, light on physics. The classification performance of the models shown in the paper is honestly quite poor. Accuracy of 0.46 and F1 score of 0.43 for the best model on test data cannot be treated as state-of-the-art. Note that the statistics for the training data are not that much better either.  There is something not quite right about for this paper, either the choice of training data, the training procedure, or something else because ViT models are quite capable as the author wrote in the intro, yet the resulting performance is so poor. We urge the authors to investigate this glaring mismatch and improve the model's performance. Otherwise, results from application of such a model are highly unreliable, which defeats the purpose. I therefore suggest a major revision. 

Technically, I do not see anything wrong with the general approach in terms of engineering. The authors described how they approached the problem, and given enough data and if the approach is sound, the models used in this paper should give us highly performing models.

The authors also did not do a thorough job at reviewing the literature on cloud type classification using machine learning/ deep learning. Their introduction to the subject seems a bit vague. I suggest the authors pay more attention to the actual physics instead of details of engineering and implementation because this is not an applied machine learning journal. 

Minor comments:

Line 34-35: references are needed. This sentence is also a bit disconnected from previous ones.

lines 39-41: references are needed

Lines 46-47: please rewrite this sentence because it is confusing to read.

Line 51: the classification is not done pixel-wise as far as I'm aware.

Lines 62-68: incomplete review of cloud type classifcation

Line 82: 'robust retrievals': this is usually not considered a retrieval since in cloud remote sensing community retrievals have specific meaning.

Line 103: I'm dubious on the point that such classification provides 'a high level of precision'.

Line 112: again, I'm not sure why 'retrievals' are used here. It reads off. 

Figure 1: should at least contain panels that show the actual number of training data. 

Lines 177-178: not clear what this sentence means.

Line 178-179: present the actual number of training data samples to give readers a clear idea.

Line 190: 128x128 pixels are not the same as 128kmx128km.

Line 197: sloppy language use. Suggest to change. 

Line 199: is 4&10 much more detailed than 9 types?

Lines 215-217: what? This sentence is quite confusing. Please rewrite.

Line 243: please use terms consistently. Do not use different terms to refer to the same thing.

Anything after Table 2 and Figure 5 is not worth discussing too much because the performance is just not acceptable. The authors need to dig deeper into their data, approach, or something else to find ways to improve the results before application.