Very relevant work, that aims to formalize processes that have thus far relied on inherently non-transparent expert assessment, and as such form a valuable complement to, and improvement of said assessment. We are living in an interesting time where avalanche forecasting is developing rapidly due to the application of modeling techniques long shunned by (skeptical) practitioners.  These authors have played a crucial role in removing some of that skepticism with previous work, and this current work is no exception.

Generally, this paper is well written, clear and shows novel methods. 

One generic reservation I have with models like these, that rely on tuning of parameters to match a regional dataset, is the applicability to other regions. This becomes even more of a concern when these are model chains, where several of these 'tuned' models are strung together. In this case , Mayer's model for dry slab stability (Pmax) is tuned (or trained as it is a ML model) on snow profiles around Davos, CH. That model is then used in the author's clustering model in Canada. 

One can question if the parameter choice would have been different if the Random Forest model of Mayer 2022 had been trained on Canadian profiles (with more new snow, less wind?). Or if someone takes this clustering model and applies it to a region with a very different snow climate (e.g. Norway). Would the parameter choice still be optimal?

This is not meant as critique to the paper, but more as a general concern to the community. Answering these questions implies a lot of work of a type that is not valued as much in the scientific community as much as doing 'new' things is. However for the rigidity of avalanche forecasts , it is just as important. 

Perhaps some discussion on the applicability of this method to other areas, and the dependency on training/tuning  data would improve the paper?

Specific comments:

- Figure 4 is not clear in black and white. Whereas being readable in black&white is too much to ask for figures 7-10, for this figure a simple linestyle change (dotted/dashed or markers) may make it readable in black and white ( for example on an eReader.)

- Sect 4.4. 

    - Is there a reason why Beta values > 0.1 were not investigated? If so please mention/explain.

    - line 210: "The number of human-assessed forecast regions changed 12 times over 107 days, with region arrangements changing on 34 days."

      The into mentions "115 days when both model and human data were available for analysis." . What is this discrepancy (107 vs 115)?

    - Fig 6: It is not clear to me how the ARI supports the choice of Beta=0.02. I see how 6a supports that, if the goal is to mimic the human assessment of the nr of regions. But 6b is unclear (to me). Intuitively I would say the ARI should be high on days where nothing changes, but you want the clusters to change only when the snowpack changes. How does ARI reflect that?

- Sect 5.3: "default fuzzines parameter r=2" : previously (sect 4.2) r was determined/picked to be optimal at 1.25; why is it 2 here?

      

- Out of curiosity: did you see the clustering change as solar irradiation (and thus the difference between N and S) became larger, i.e. throughout the season?

  - in general; how do you make representative profiles for a region when solar irradiation leads to big differences in aspect? Or is the clustering only done for dry (ENW) avalanche profiles?

The manuscript "Clustering simulated snow profiles to form avalanche forecast regions" present a very interesting method to dynamically identify areas of similar avalanche hazard. The proposed method takes care of the spatial and temporal coherence of provided cluster which may be a significant help for the forecasters and allow for further time-based or geographic analyses using such model. The idea itself of clustering the simulated profiles is highly relevant to help forecasters in the analysis of snow models data. Hence, I think such work is highly valuable for Copernicus journal readers. However, I have some concerns that may be addressed before publication.

The selection of hyperparameters (alpha, beta, r and k) is not fully convincing due to the selected metrics (maybe a better description and justification would clarify this point) and method to the optimization of each parameter independently of other parameters. This would not be a problem if we had the conviction at the end that the model performs well in any situation. However, the final results of clustering are only provided on one year, which is the year on which the parameters have been optimized. This is not the state of the art of evaluation of Statistical/ML models. Moreover, this year seems quite specific as one day is said to be representative of the whole winter (line 264). Thus, we do not have a representative idea of the performances of such model even though it seems to be highly promising. We know that we can reproduce with a good performance clustering of the past for which we have "the ground truth" available (human clustering here) to optimize clustering but not how such model could perform the next year/in forecasting mode.

With the addition of results on a different dataset, this paper would make a high interest paper for the snow and avalanche community. An alternative solution is to demonstrate with a sensitivity parameter analysis (combining the 4 parameters) that the results are not highly sensitive to such parameter tuning. However, I believe that an additional year or at least some situations not in the tuning dataset should be available.

I also detail below more specific or detailed points.

1. Line 59 you limit the study to flat, sheltered terrain at treeline elevation. This seems an interesting selection here, but therefore the results are compared to human assessed forecast (e.g. Fig 7a). This human assessed forecast should take into account all elevations. Thus, is this really comparable ? In the discussion, you underline the high impact on elevation on the results of clustering (Bouchayer, 2017, line 295 and following).

2. Line 60, you state that the method used to obtain simulated profiles is of little relevance. I do not agree. I understand the idea to put it in appendix not to break the flow of the paper (which is quite easy to read). However, choices in the simulated profiles can highly impact the results. Maybe this sentence could be modified. In particular, I noted line 355 that you selected meteorological data from 2 models. I wonder if this could impact the results as the climatology of meteorological data may be different between models, especially as the resolution are very different (so the representation of mountainous area is significantly different). I would be at least interested to have a map with the selected points for each model to ensure that there is no obvious correlation between clustering results and the choice of meteorological model.

3. Line 100-103: Could you explain why you state that "this method closely aligns with forecasters' criteria..." ?

4. Line 109-110 "that are more likely to align... than would result from basic Euclidian distances": a distance between two areas may be defined differently than the distance between centroid, especially it could be defined as the minimum distance between two points, each on in each area. This latter Euclidian distance would be relevant. Maybe this part of sentence is not relevant here, you made a choice that seem simple but relevant for your goal.

5. Equation 2: Notation $u^r_iv$ may not be clear. I supposed this is $u_iv$ at power r. If so maybe, the power could be put more clearly with a parenthesis, for instance.

6. Line 130: What is the value of threshold tolerance? Do you use a usually used value or how do you select this value?

7. Line 143: How do you select these ranges.? Especially the range for r is surprising as you said later that the default fussiness parameter is 2 (line 261).

8. Line 159: you here use distance between centroid whereas you stated line 109-110 that it was not relevant

9. Line 163: could you provide a reference and/or explain the goal of this metric. I am not sure I understand.

10. Same as 9 for line 167-171. "high-quality" clustering is not sufficient for me to understand the specificity of the score.

11. Line 179: "An elbow in the within between ratio... between 1.2 and 1.35": The within between ratio decrease after 1.35 and lower is better for this ratio, so I would not have chosen values between 1.2 and 1.35 with this ratio.

12. Line 196: Determination of k value. Why do you select different k values based on different scores and then average the different k values? With such method, you do not have any constraint on the values of the metrics (especially as these metrics are not always monotonous) while using the lower k value that are above/below thresholds for different scores for instance would give stronger results.

13. Line 210-213: "XX days" (used 3 times) is not fully clear. Is this "XX times over 107 days" ? Then, use "times" rather than "days" to be coherent with first use.

14. The average adjusted range index is not defined. Is this the average of adjusted range index for all couples of successive days.

15. Line 213: I suggest "0.69 suggesting *more* frequent and complex changes" unless you have a justified threshold on average adjusted rand index to separate between complex and simple clustering.

16. Figure 6: You here have both the influence of k and beta on the adjusted rand index (ARI). If the number of cluster is higher in the automatic clustering compared to reference human clustering but lower adjusted rand index even though the clustering is perfect because some human areas are partitioned into several clusters that varies against days while the union of clusters do not vary and match the human clusters. Why not doing as for other parameters and separate the effect of k and beta by fixing k to have a relevant comparison of ARI values?

17. Figure 7 and 8 top: please report the limits of public forecast on clustering maps to ease the reading of the graph.

18. Figure 7 and 8 top: You do not discuss the differences between human clustering and automatic clustering. For some misclassified regions, the fuzzy clustering show the uncertainty (e.g. 54% in the south of northern region) however, in some cases, there is no uncertainty shown (e/g/ >90% for south of eastern cluster and some in the south of the center region). Taking human clustering as the reference may be one limit here that could be discussed or maybe it is a limit of the method. Such result make me want to know more! However, I understand that such details may not be the core of such paper and should not take too much space.

19. Line 288-289: "Furthermore, expanding this distance...": I do not understand this sentence and what you have in mind. Maybe could you develop?