Dear authors,

I'd first like to say that I found your manuscript very interesting. You were thorough in your evaluation of your model. I also like that your GitLab page encourages reproducibility and people to use your model. I took the liberty of directly annotating your manuscript. I think the content and research are very good, but the form and presentation quality could be improved so that it's easier to understand.

But here are some other more general comments:

• ML level: because you're aiming for an EGU journal, I think there might be a majority of readers who are not experts in ML. Therefore, I think that at some points, it's necessary to explain some terms (I've pointed some out in the manuscript). Be careful of the line where you become unnecessarily too technical and where you might lose some of your readers. I'd also add a section in the discussion advising people (who are unfamiliar with ML and are set on their traditional numerical models) on how to use your model.
• Consistency of terms: Be careful not to use too many names to refer to your model, sometimes CleanSnow, sometimes TCN. I'd stick to CleanSnow everywhere and use TCN only when you refer to the architecture; otherwise, it becomes very confusing. You devised a nice name for your model, so use it :) Re-read the manuscript and change it where needed.
• Cross-validation: The part about hyperparameter tuning of your model is briefly mentioned but very important. Did you do any cross-validation for this (if not, why not?)? And which hyperparameters were tuned and came out as best?
• Figures and their legend: your figure legends are generally concise and need more information. Although this is very tedious work, legends should respect a few things, such as acronyms that come up in the figure should be referred to (and generally explained) in the legend, and a reader should be able to understand the figure on its own without having to go look things up in the text. Please go over your legends again and make them more descriptive.
• Results: In your description of results (Section "Experiments"), when making statements that can be backed up by numbers in parenthesis, these numbers should be provided (such as F1 scores). There are a lot of F1 scores in your figures that can be easily used to back up your claims. Otherwise, the reader has to go look them up in the figures, and your statements seem empty. One good example where you do this is line 285, but this should be in all other results, too: "e.g., demonstrates that the model confidently classified snow (TPR = 99.4%) in contrast to the classification of snow-free ground with (TPR = 88.4%)".
• Grammatical tense: Be careful about mixing up too many tenses; sometimes, you switch from past to present without making too much sense. For the sake of consistency, try to keep the same when talking about the same things: for example, keep past tense when talking about your experiments and present tense for the results.
• Presentation of results and discussion: it seems to me that quite a lot of the results are simply repeated in the discussion, and that's not very interesting. I suggest that if a question comes up in the results, you discuss it immediately (for example, the negative effect of the solar variable). Otherwise, the reader doesn't get an explanation, reads on, forgets about it, and suddenly finds it again in the discussion. Instead of repeating results, the discussion, for example, also needs the limitations of CleanSnow.
• Repetitiveness: your text is quite long, and I think you can make it shorter by removing unnecessary repetitions. Some things to remove are repetitions of things said previously in other sections ("as previously described in ..."), which can just be a reference to a section. "As shown in Figure ..." can just be a statement with a "(Figure number)". I tried to strike out some things that jumped up to me, but I'll let you have a look.
• The problem of generalization: this comes back to the limitations of CleanSnow. You've shown that it generalizes well to stations within its training range but performs less well to those outside it. This is a normal limitation in ML but should be presented as such. CleanSnow will struggle when applied to a new station that is not within the distribution it's been trained on (which is normal because it's not like you did any transfer learning or something), but that's not a good generalization. So, I think your text needs more transparency about this limitation.
• I have an open question for you: you briefly mentioned input anomalies in your discussion. Did you notice any particular behavior for 2022 and 2023 (seeing as they're strong temperature anomalies)?

1. This study focused on the classification of snow height measurements based on AI techniques. However, the scientific significance was not sufficient enough, or why this work is important?
2. What’s the relationship between quality assessment and AI classification? In my view, this work doesn’t aim at improving data quality, just distinguishing possible anomalies from all station measurements. So how to reflect the advance of AI method in this work?
3. The structure of this article is not clear enough, please improve it and maintain some important research work. Now
4. In figure 1, how to determine the training and testing stations?
5. P3, lines 70-80. These descriptions should be moved to Section introduction.
6. P3, lines 80-85. Please give the physic basis.
7. Please provide a flowchart for this paper.
8. How to determine the truth data?
9. P6, lines 110-135. This paragraph should belong to methodology, thus, the title ‘3 Machine learning based snow cover classification’ is not suitable. This section should be method or methodology.
10. P8, ‘4.1 dataset’ should be introduced in methodology section, not here.
11. It is difficult for me to understand the logic and structure of this study.