the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 13 Apr 2023
A Large-scale Validation of Snowpack Simulations in Support of Avalanche Forecasting Focusing on Critical Layers
Abstract. Avalanche warning services increasingly employ large-scale snow stratigraphy simulations to improve their insight into the current state of the snowpack. These simulations contain information about thin, persistent critical avalanche layers that are buried within the snowpack and are fundamental drivers of avalanche hazard. However, the data volume, data complexity, and unknown validity have so far limited the value of the simulations for operational decisions. We attribute this at least partially to a lack of research that validates the simulations for their capability to represent the existence and instability of known critical layers at the regional scale. To address this knowledge gap, we present methods that enable meaningful comparisons between regional assessments of avalanche forecasters and snowpack simulations that are distributed across entire forecast regions. We applied these methods to operational data sets of ten winter seasons and three public forecast regions in western Canada and thereby quantified the performance of the Canadian weather and snowpack model chain to represent persistent critical avalanche layers. We found that the overall probability of detecting a known critical layer in the simulations can be as high as 75 % when accepting a low probability of 40 % that any simulated layer is actually of operational concern in reality. Furthermore, we explored patterns that characterize which layers were represented well and which were not. Faceted layers, for example, were captured well but also caused most false alarms, whereas surface hoar layers tended to be less prevalent but in return were mostly of operational concern when modeled. Overall, our results suggest that the simulations provide a valuable starting point for targeted field observations as well as a rich complementary information source that can help alert forecasters about the existence of specific critical layers or provide an independent perspective on their instability. However, we do not believe that the existing model chain is sufficiently reliable to generate assessments purely based on simulations. We conclude by presenting our vision of a real-time operational validation suite that can help forecasters develop a better understanding of the simulations' strengths and weaknesses by continuously comparing assessments and simulations in a user-friendly manner.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2023-420', Jürg Schweizer, 05 May 2023
Please see the supplement for the review comments.
- AC1: 'Reply on RC1', Florian Herla, 16 Jun 2023
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CC1: 'Comment on egusphere-2023-420', Ron Simenhois, 06 May 2023
This is a well-written and interesting manuscript with impressive results. Below are a few general comments on the manuscript.
- This work focuses on identifying weak layers for avalanche forecasting. However, a large body of research highlights the importance of both the weak layer and slab for avalanche formation. I believe the manuscript will benefit from a short discussion about why this work focuses on weak layers only.
- The authors mention that this work aims to help with dry avalanche forecasting toward the end of the manuscript. It will add to the manuscript's clarity if this is mentioned earlier.
- This work concentrates on snowpack layers' grain type (persistent weak layers). Other weak layer characteristics that may or may not be more important than grain type, like depth, hardness, grain size, etc., are only mentioned toward the end as potential for future work. Again, I think the manuscript will benefit from a short discussion of why these weak layer characteristics are not part of this work.
Citation: https://doi.org/10.5194/egusphere-2023-420-CC1 - AC2: 'Reply on CC1', Florian Herla, 16 Jun 2023
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RC2: 'Comment on egusphere-2023-420', Anonymous Referee #2, 02 Apr 2024
EGUSphere 2023-420
Review of “A Large-scale Validation of Snowpack Simulations in Support of Avalanche Forecasting Focusing on Critical Layers” by Florian Herla, Pascal Haegeli, Simon Horton, and Patrick Mair.
This is overall a well written and motivated manuscript, although heavy on jargon, technical language and acronyms in places, and assuming the reader is an ‘expert’ in some places without full explanations. The manuscript is likely aimed at a very specific audience, and does little to make it more accessible to those who might be at the periphery of snowpack simulations. That said, it is clearly a substantive work which will be good for those doing snopack simulations, although they will need to read it several times to fully understand what is being done and how it is being done.
I have made a series of suggestions below, in no order of importance, but rather notes as reading through it, and sometimes returning back to different parts of the manuscript. Because no major issues came up, I would suggest that this go through minor changes. Many of the following are stylistic and to improve the structure/content, with an occasional query about meaning and details given.
- [Abstract] Consider whether to put more quantitative summaries into the abstract (currently is very narrative).
- Insert somewhere in the first paragraph of the introduction “the subject of this paper” so it is clear that this will be the subject to be discussed.
- The introduction is strong, well cited, but would benefit (because of its length of five paragraphs, a sentence at the end of the first paragraph stating something like “In the rest of this introduction we will….” To signal to the reader where you are headed.
- After the first sentence of Section 2, tell us how the Section 2 will be organized.
- Line 81. Indicate here or elsewhere the normal months for the winter season. In particular, if you state a winter season of 2013, is this from 2012 to 2013 or 2013 to 2014.
- Figure 1. Put ALP, TL, BTL into figure caption where they appear (alpine [ALP], etc.). For all figure captions, please ensure that you acknowledge source of data explicitly in the figure caption.
- Figure 1. Please include a scale for S2S, GNP, BYK insets. For overall figure map, put the line indicating 100 km slightly lower, so one can really see that it is the ‘length’ for 100 km. In figure caption, indicate size of grid cell (in addition to where it is mentioned in the text).
- Line 95 and other locations, m is m asl? If so, be clear.
- Line 92. “Overall, we selected 1004 grid points (Fig. 1) covering an area of ******”.
- For elevations within your classes S2S, GNP, BYK, it would be good to know the distribution of the elevation points, and some idea of which way these slopes are facing, along with any prominent wind directions.
- Because of the large number of acronyms used in this paper, I recommend that early on you have a Table of Acronyms (Table 1) to make it easier for the reader in what is a fairly ‘dense’ paper.
- Line 99, Lehning et al., 2002a, b (not b, a)
- General: first time important acronyms are given, spell out, e.g., HRDPS (High Resolution Deterministic Prediction System)
- Section 2.3 “ of the winter season” Again, please define or give us an idea of how the winter season varies, or if the same months are used, which these are. If it is just the standard definition of November to March, that is fine, but state, and whether or not border line months (or other months) also would become important.
- Section 2 for data, I would have liked to have seen perhaps 1-2 other figures (e.g., photos, bulletins, maps) representing the real data that was used. Not a strong requirement on this, but it would have been helpful to bring this back to reality of what is being modelled for the reader.
- Figure 2. These colours do not work in the PDF downloaded, and are very difficult to read. For example, white on bright green or white on bright blue, are not recommended. Font size getting too small. Figure caption, define all acronyms, and tell us what the different colours mean. This could be overall a stronger flowchart and figure caption as currently it would need the author next to the reader to explain what they are seeing.
- Figure 3. Similar to figure 2 in terms of colours used (hard to distinguish all of these). Perhaps use https://colorbrewer2.org/ to help you pick your colour palettes. Rather than refer us to Sect. 2.2. for colours, refer us to a table or put them actually in the caption. Mostly well explained in terms of the figure caption, except I did not follow the dashed horizontal line, and why the vertical grey line on the left of the formation line goes ‘before’ the arrow. I did not get ‘time’ here—the text states that the burial window is four days, but the solid line for the burial window is 3 days and the dash line 2 days. Can time be made clearer in text caption and the figure, that each box horizontally represents one day (I think)?
- Figure 4. Similar comments as above, but I was unclear about the dash vertical vs. the solid vertical line and dark grey vs. light grey in ‘a’. What is the dark horizontal line in ‘a’? For part ‘d’, can you move the ‘of concern’ over a bit to the ‘yes’ and ‘no’ so it is not confused as being a secondary axis for ‘c’? Please make clearer the time axis, that this is 2019 to 2020 (I think). This can be signalled in the axis and in the figure caption. When I pair the figure with the text, there is a lot that feels left out in explanation (either in figure caption or in the text) and again, it almost needs the authors to be with the readers to explain each aspect. For the violoin plot in ‘b’, do you not need to have 0.0 to 1.0 on y-axis, and define what is meant by the white dot, and the black bars (there are MANY ways of doing violin plots, you cannot assume a give way is being shown here that everyone will automatically understand).
- [Minor] Line 273. Why are you putting in a page number (unless this is a direct quote, in which case you should have “ “).
- Equation 2: What is FN’? [You define FN, but not FN’]. Ah, never mind, I see now that it is a comma, but looks like a ‘
- Table of variables (could be combined with table of acronyms). There are a lot of variables—consider having a table of acronyms and variables introduced early on, defining each variable, name, units, etc.
- Section 5.1 is a substantive discussion on insights.
- Can these be better broken out rather than having almost three pages of narrative text, so as to make this easier for higher level reading.
- Can this be brought back a bit more to the broader literature (two citations seems really few for bringing this back to the wider community and what has been done).
Citation: https://doi.org/10.5194/egusphere-2023-420-RC2
Model code and software
Critical Layer Validation—Data and Code F. Herla, P. Haegeli, S. Horton, and P. Mair https://doi.org/10.17605/OSF.IO/W7PJY
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