Articles | Volume 22, issue 6
https://doi.org/10.5194/nhess-22-1911-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-22-1911-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
On the correlation between a sub-level qualifier refining the danger level with observations and models relating to the contributing factors of avalanche danger
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Stephanie Mayer
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Cristina Pérez-Guillén
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Günter Schmudlach
Skitourenguru GmbH, Zurich, Switzerland
Kurt Winkler
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Related authors
Frank Techel, Karsten Müller, Christopher Marquardt, and Christoph Mitterer
EGUsphere, https://doi.org/10.5194/egusphere-2025-3349, https://doi.org/10.5194/egusphere-2025-3349, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
We studied how avalanche forecasters across Europe used a new tool called the EAWS Matrix to assess avalanche danger levels. Despite different approaches, many services used the Matrix in similar ways. Our findings can help to further improve the Matrix and support more consistent avalanche forecasts, leading to more reliable and credible avalanche information for people in snow-covered mountain regions.
Leonie Schäfer, Frank Techel, Günter Schmudlach, and Ross S. Purves
EGUsphere, https://doi.org/10.5194/egusphere-2025-2344, https://doi.org/10.5194/egusphere-2025-2344, 2025
Short summary
Short summary
Backcountry skiing is a popular form of recreation in Switzerland and worldwide, despite numerous avalanche accidents and fatalities that are recorded each year. There is a need for spatially explicit information on backcountry usage for effective risk estimations and avalanche forecast verification. We successfully used GPS tracks and online engagement data to model daily backcountry skiing base rates in the Swiss Alps based on a set of snow, weather, temporal and environmental variables.
Cristina Pérez-Guillén, Frank Techel, Michele Volpi, and Alec van Herwijnen
Nat. Hazards Earth Syst. Sci., 25, 1331–1351, https://doi.org/10.5194/nhess-25-1331-2025, https://doi.org/10.5194/nhess-25-1331-2025, 2025
Short summary
Short summary
This study assesses the performance and explainability of a random-forest classifier for predicting dry-snow avalanche danger levels during initial live testing. The model achieved ∼ 70 % agreement with human forecasts, performing equally well in nowcast and forecast modes, while capturing the temporal dynamics of avalanche forecasting. The explainability approach enhances the transparency of the model's decision-making process, providing a valuable tool for operational avalanche forecasting.
Alessandro Maissen, Frank Techel, and Michele Volpi
Geosci. Model Dev., 17, 7569–7593, https://doi.org/10.5194/gmd-17-7569-2024, https://doi.org/10.5194/gmd-17-7569-2024, 2024
Short summary
Short summary
By harnessing AI models, this work enables processing large amounts of data, including weather conditions, snowpack characteristics, and historical avalanche data, to predict human-like avalanche forecasts in Switzerland. Our proposed model can significantly assist avalanche forecasters in their decision-making process, thereby facilitating more efficient and accurate predictions crucial for ensuring safety in Switzerland's avalanche-prone regions.
Frank Techel, Stephanie Mayer, Ross S. Purves, Günter Schmudlach, and Kurt Winkler
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-158, https://doi.org/10.5194/nhess-2024-158, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
We evaluate fully data- and model-driven predictions of avalanche danger in Switzerland and compare them with human-made avalanche forecasts as a benchmark. We show that model predictions perform similarly to human forecasts calling for a systematic integration of forecast chains into the forecasting process.
Karsten Müller, Frank Techel, and Christoph Mitterer
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-48, https://doi.org/10.5194/nhess-2024-48, 2024
Preprint under review for NHESS
Short summary
Short summary
Avalanche forecasting is crucial for mountain safety. Tools like the European Avalanche Danger Scale and Matrix set standards for forecasters, but consistency still varies. We analyzed the use of the EAWS Matrix, aiding danger level assignment. Our analysis shows inconsistencies, suggesting further need for refinement and training.
Stephanie Mayer, Frank Techel, Jürg Schweizer, and Alec van Herwijnen
Nat. Hazards Earth Syst. Sci., 23, 3445–3465, https://doi.org/10.5194/nhess-23-3445-2023, https://doi.org/10.5194/nhess-23-3445-2023, 2023
Short summary
Short summary
We present statistical models to estimate the probability for natural dry-snow avalanche release and avalanche size based on the simulated layering of the snowpack. The benefit of these models is demonstrated in comparison with benchmark models based on the amount of new snow. From the validation with data sets of quality-controlled avalanche observations and danger levels, we conclude that these models may be valuable tools to support forecasting natural dry-snow avalanche activity.
Elisabeth D. Hafner, Frank Techel, Rodrigo Caye Daudt, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
Nat. Hazards Earth Syst. Sci., 23, 2895–2914, https://doi.org/10.5194/nhess-23-2895-2023, https://doi.org/10.5194/nhess-23-2895-2023, 2023
Short summary
Short summary
Oftentimes when objective measurements are not possible, human estimates are used instead. In our study, we investigate the reproducibility of human judgement for size estimates, the mappings of avalanches from oblique photographs and remotely sensed imagery. The variability that we found in those estimates is worth considering as it may influence results and should be kept in mind for several applications.
Stephanie Mayer, Alec van Herwijnen, Frank Techel, and Jürg Schweizer
The Cryosphere, 16, 4593–4615, https://doi.org/10.5194/tc-16-4593-2022, https://doi.org/10.5194/tc-16-4593-2022, 2022
Short summary
Short summary
Information on snow instability is crucial for avalanche forecasting. We introduce a novel machine-learning-based method to assess snow instability from snow stratigraphy simulated with the snow cover model SNOWPACK. To develop the model, we compared observed and simulated snow profiles. Our model provides a probability of instability for every layer of a simulated snow profile, which allows detection of the weakest layer and assessment of its degree of instability with one single index.
Cristina Pérez-Guillén, Frank Techel, Martin Hendrick, Michele Volpi, Alec van Herwijnen, Tasko Olevski, Guillaume Obozinski, Fernando Pérez-Cruz, and Jürg Schweizer
Nat. Hazards Earth Syst. Sci., 22, 2031–2056, https://doi.org/10.5194/nhess-22-2031-2022, https://doi.org/10.5194/nhess-22-2031-2022, 2022
Short summary
Short summary
A fully data-driven approach to predicting the danger level for dry-snow avalanche conditions in Switzerland was developed. Two classifiers were trained using a large database of meteorological data, snow cover simulations, and danger levels. The models performed well throughout the Swiss Alps, reaching a performance similar to the current experience-based avalanche forecasts. This approach shows the potential to be a valuable supplementary decision support tool for assessing avalanche hazard.
Veronika Hutter, Frank Techel, and Ross S. Purves
Nat. Hazards Earth Syst. Sci., 21, 3879–3897, https://doi.org/10.5194/nhess-21-3879-2021, https://doi.org/10.5194/nhess-21-3879-2021, 2021
Short summary
Short summary
How is avalanche danger described in public avalanche forecasts? We analyzed 6000 textual descriptions of avalanche danger in Switzerland, taking the perspective of the forecaster. Avalanche danger was described rather consistently, although the results highlight the difficulty of communicating conditions that are neither rare nor frequent, neither small nor large. The study may help to refine the ways in which avalanche danger could be communicated to the public.
Jürg Schweizer, Christoph Mitterer, Benjamin Reuter, and Frank Techel
The Cryosphere, 15, 3293–3315, https://doi.org/10.5194/tc-15-3293-2021, https://doi.org/10.5194/tc-15-3293-2021, 2021
Short summary
Short summary
Snow avalanches threaten people and infrastructure in snow-covered mountain regions. To mitigate the effects of avalanches, warnings are issued by public forecasting services. Presently, the five danger levels are described in qualitative terms. We aim to characterize the avalanche danger levels based on expert field observations of snow instability. Our findings contribute to an evidence-based description of danger levels and to improve consistency and accuracy of avalanche forecasts.
Elisabeth D. Hafner, Frank Techel, Silvan Leinss, and Yves Bühler
The Cryosphere, 15, 983–1004, https://doi.org/10.5194/tc-15-983-2021, https://doi.org/10.5194/tc-15-983-2021, 2021
Short summary
Short summary
Satellites prove to be very valuable for documentation of large-scale avalanche periods. To test reliability and completeness, which has not been satisfactorily verified before, we attempt a full validation of avalanches mapped from two optical sensors and one radar sensor. Our results demonstrate the reliability of high-spatial-resolution optical data for avalanche mapping, the suitability of radar for mapping of larger avalanches and the unsuitability of medium-spatial-resolution optical data.
Frank Techel, Karsten Müller, and Jürg Schweizer
The Cryosphere, 14, 3503–3521, https://doi.org/10.5194/tc-14-3503-2020, https://doi.org/10.5194/tc-14-3503-2020, 2020
Short summary
Short summary
Exploring a large data set of snow stability tests and avalanche observations, we quantitatively describe the three key elements that characterize avalanche danger: snowpack stability, the frequency distribution of snowpack stability, and avalanche size. The findings will aid in refining the definitions of the avalanche danger scale and in fostering its consistent usage.
Frank Techel, Karsten Müller, Christopher Marquardt, and Christoph Mitterer
EGUsphere, https://doi.org/10.5194/egusphere-2025-3349, https://doi.org/10.5194/egusphere-2025-3349, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
We studied how avalanche forecasters across Europe used a new tool called the EAWS Matrix to assess avalanche danger levels. Despite different approaches, many services used the Matrix in similar ways. Our findings can help to further improve the Matrix and support more consistent avalanche forecasts, leading to more reliable and credible avalanche information for people in snow-covered mountain regions.
Leonie Schäfer, Frank Techel, Günter Schmudlach, and Ross S. Purves
EGUsphere, https://doi.org/10.5194/egusphere-2025-2344, https://doi.org/10.5194/egusphere-2025-2344, 2025
Short summary
Short summary
Backcountry skiing is a popular form of recreation in Switzerland and worldwide, despite numerous avalanche accidents and fatalities that are recorded each year. There is a need for spatially explicit information on backcountry usage for effective risk estimations and avalanche forecast verification. We successfully used GPS tracks and online engagement data to model daily backcountry skiing base rates in the Swiss Alps based on a set of snow, weather, temporal and environmental variables.
Cristina Pérez-Guillén, Frank Techel, Michele Volpi, and Alec van Herwijnen
Nat. Hazards Earth Syst. Sci., 25, 1331–1351, https://doi.org/10.5194/nhess-25-1331-2025, https://doi.org/10.5194/nhess-25-1331-2025, 2025
Short summary
Short summary
This study assesses the performance and explainability of a random-forest classifier for predicting dry-snow avalanche danger levels during initial live testing. The model achieved ∼ 70 % agreement with human forecasts, performing equally well in nowcast and forecast modes, while capturing the temporal dynamics of avalanche forecasting. The explainability approach enhances the transparency of the model's decision-making process, providing a valuable tool for operational avalanche forecasting.
Stephanie Mayer, Martin Hendrick, Adrien Michel, Bettina Richter, Jürg Schweizer, Heini Wernli, and Alec van Herwijnen
The Cryosphere, 18, 5495–5517, https://doi.org/10.5194/tc-18-5495-2024, https://doi.org/10.5194/tc-18-5495-2024, 2024
Short summary
Short summary
Understanding the impact of climate change on snow avalanche activity is crucial for safeguarding lives and infrastructure. Here, we project changes in avalanche activity in the Swiss Alps throughout the 21st century. Our findings reveal elevation-dependent patterns of change, indicating a decrease in dry-snow avalanches alongside an increase in wet-snow avalanches at elevations above the current treeline. These results underscore the necessity to revisit measures for avalanche risk mitigation.
Alessandro Maissen, Frank Techel, and Michele Volpi
Geosci. Model Dev., 17, 7569–7593, https://doi.org/10.5194/gmd-17-7569-2024, https://doi.org/10.5194/gmd-17-7569-2024, 2024
Short summary
Short summary
By harnessing AI models, this work enables processing large amounts of data, including weather conditions, snowpack characteristics, and historical avalanche data, to predict human-like avalanche forecasts in Switzerland. Our proposed model can significantly assist avalanche forecasters in their decision-making process, thereby facilitating more efficient and accurate predictions crucial for ensuring safety in Switzerland's avalanche-prone regions.
Frank Techel, Stephanie Mayer, Ross S. Purves, Günter Schmudlach, and Kurt Winkler
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-158, https://doi.org/10.5194/nhess-2024-158, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
We evaluate fully data- and model-driven predictions of avalanche danger in Switzerland and compare them with human-made avalanche forecasts as a benchmark. We show that model predictions perform similarly to human forecasts calling for a systematic integration of forecast chains into the forecasting process.
Andri Simeon, Cristina Pérez-Guillén, Michele Volpi, Christine Seupel, and Alec van Herwijnen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-76, https://doi.org/10.5194/gmd-2024-76, 2024
Revised manuscript under review for GMD
Short summary
Short summary
Avalanche seismic detection systems are key for forecasting, but distinguishing avalanches from other seismic sources remains challenging. We propose novel autoencoder models to automatically extract features and compare them with standard seismic attributes. These features are then used to classify avalanches and noise events. The autoencoder feature classifiers have the highest sensitivity to detect avalanches, while the standard seismic classifier performs better overall.
Karsten Müller, Frank Techel, and Christoph Mitterer
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-48, https://doi.org/10.5194/nhess-2024-48, 2024
Preprint under review for NHESS
Short summary
Short summary
Avalanche forecasting is crucial for mountain safety. Tools like the European Avalanche Danger Scale and Matrix set standards for forecasters, but consistency still varies. We analyzed the use of the EAWS Matrix, aiding danger level assignment. Our analysis shows inconsistencies, suggesting further need for refinement and training.
Stephanie Mayer, Frank Techel, Jürg Schweizer, and Alec van Herwijnen
Nat. Hazards Earth Syst. Sci., 23, 3445–3465, https://doi.org/10.5194/nhess-23-3445-2023, https://doi.org/10.5194/nhess-23-3445-2023, 2023
Short summary
Short summary
We present statistical models to estimate the probability for natural dry-snow avalanche release and avalanche size based on the simulated layering of the snowpack. The benefit of these models is demonstrated in comparison with benchmark models based on the amount of new snow. From the validation with data sets of quality-controlled avalanche observations and danger levels, we conclude that these models may be valuable tools to support forecasting natural dry-snow avalanche activity.
Elisabeth D. Hafner, Frank Techel, Rodrigo Caye Daudt, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
Nat. Hazards Earth Syst. Sci., 23, 2895–2914, https://doi.org/10.5194/nhess-23-2895-2023, https://doi.org/10.5194/nhess-23-2895-2023, 2023
Short summary
Short summary
Oftentimes when objective measurements are not possible, human estimates are used instead. In our study, we investigate the reproducibility of human judgement for size estimates, the mappings of avalanches from oblique photographs and remotely sensed imagery. The variability that we found in those estimates is worth considering as it may influence results and should be kept in mind for several applications.
Stephanie Mayer, Alec van Herwijnen, Frank Techel, and Jürg Schweizer
The Cryosphere, 16, 4593–4615, https://doi.org/10.5194/tc-16-4593-2022, https://doi.org/10.5194/tc-16-4593-2022, 2022
Short summary
Short summary
Information on snow instability is crucial for avalanche forecasting. We introduce a novel machine-learning-based method to assess snow instability from snow stratigraphy simulated with the snow cover model SNOWPACK. To develop the model, we compared observed and simulated snow profiles. Our model provides a probability of instability for every layer of a simulated snow profile, which allows detection of the weakest layer and assessment of its degree of instability with one single index.
Cristina Pérez-Guillén, Frank Techel, Martin Hendrick, Michele Volpi, Alec van Herwijnen, Tasko Olevski, Guillaume Obozinski, Fernando Pérez-Cruz, and Jürg Schweizer
Nat. Hazards Earth Syst. Sci., 22, 2031–2056, https://doi.org/10.5194/nhess-22-2031-2022, https://doi.org/10.5194/nhess-22-2031-2022, 2022
Short summary
Short summary
A fully data-driven approach to predicting the danger level for dry-snow avalanche conditions in Switzerland was developed. Two classifiers were trained using a large database of meteorological data, snow cover simulations, and danger levels. The models performed well throughout the Swiss Alps, reaching a performance similar to the current experience-based avalanche forecasts. This approach shows the potential to be a valuable supplementary decision support tool for assessing avalanche hazard.
Antoine Guillemot, Alec van Herwijnen, Eric Larose, Stephanie Mayer, and Laurent Baillet
The Cryosphere, 15, 5805–5817, https://doi.org/10.5194/tc-15-5805-2021, https://doi.org/10.5194/tc-15-5805-2021, 2021
Short summary
Short summary
Ambient noise correlation is a broadly used method in seismology to monitor tiny changes in subsurface properties. Some environmental forcings may influence this method, including snow. During one winter season, we studied this snow effect on seismic velocity of the medium, recorded by a pair of seismic sensors. We detected and modeled a measurable effect during early snowfalls: the fresh new snow layer modifies rigidity and density of the medium, thus decreasing the recorded seismic velocity.
Veronika Hutter, Frank Techel, and Ross S. Purves
Nat. Hazards Earth Syst. Sci., 21, 3879–3897, https://doi.org/10.5194/nhess-21-3879-2021, https://doi.org/10.5194/nhess-21-3879-2021, 2021
Short summary
Short summary
How is avalanche danger described in public avalanche forecasts? We analyzed 6000 textual descriptions of avalanche danger in Switzerland, taking the perspective of the forecaster. Avalanche danger was described rather consistently, although the results highlight the difficulty of communicating conditions that are neither rare nor frequent, neither small nor large. The study may help to refine the ways in which avalanche danger could be communicated to the public.
Jürg Schweizer, Christoph Mitterer, Benjamin Reuter, and Frank Techel
The Cryosphere, 15, 3293–3315, https://doi.org/10.5194/tc-15-3293-2021, https://doi.org/10.5194/tc-15-3293-2021, 2021
Short summary
Short summary
Snow avalanches threaten people and infrastructure in snow-covered mountain regions. To mitigate the effects of avalanches, warnings are issued by public forecasting services. Presently, the five danger levels are described in qualitative terms. We aim to characterize the avalanche danger levels based on expert field observations of snow instability. Our findings contribute to an evidence-based description of danger levels and to improve consistency and accuracy of avalanche forecasts.
Elisabeth D. Hafner, Frank Techel, Silvan Leinss, and Yves Bühler
The Cryosphere, 15, 983–1004, https://doi.org/10.5194/tc-15-983-2021, https://doi.org/10.5194/tc-15-983-2021, 2021
Short summary
Short summary
Satellites prove to be very valuable for documentation of large-scale avalanche periods. To test reliability and completeness, which has not been satisfactorily verified before, we attempt a full validation of avalanches mapped from two optical sensors and one radar sensor. Our results demonstrate the reliability of high-spatial-resolution optical data for avalanche mapping, the suitability of radar for mapping of larger avalanches and the unsuitability of medium-spatial-resolution optical data.
Frank Techel, Karsten Müller, and Jürg Schweizer
The Cryosphere, 14, 3503–3521, https://doi.org/10.5194/tc-14-3503-2020, https://doi.org/10.5194/tc-14-3503-2020, 2020
Short summary
Short summary
Exploring a large data set of snow stability tests and avalanche observations, we quantitatively describe the three key elements that characterize avalanche danger: snowpack stability, the frequency distribution of snowpack stability, and avalanche size. The findings will aid in refining the definitions of the avalanche danger scale and in fostering its consistent usage.
Cited articles
Birkeland, K.: Spatial patterns of snow stability through a small mountain
range, J. Glaciol., 47, 176–186, https://doi.org/10.3189/172756501781832250,
2001. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001. a
Bühler, Y., von Rickenbach, D., Stoffel, A., Margreth, S., Stoffel, L., and Christen, M.: Automated snow avalanche release area delineation – validation of existing algorithms and proposition of a new object-based approach for large-scale hazard indication mapping, Nat. Hazards Earth Syst. Sci., 18, 3235–3251, https://doi.org/10.5194/nhess-18-3235-2018, 2018. a, b, c
EAWS: Standards: avalanche size,
https://www.avalanches.org/standards/avalanche-size/ (last
access: 13 May 2022), 2019. a
EAWS: Standards: Avalanche danger scale,
https://www.avalanches.org/standards/avalanche-danger-scale/,
last access: 3 November 2020. a
EAWS: Definition of avalanche danger, avalanche danger level and their
contributing factors; presented at EAWS General Assembly, Davos, Switzerland,
2021, EAWS working group Matrix and Scale (working group members: Müller,
K., Bellido, G., Bertrando, L., Feistl, T., Mitterer, C., Palmgren, P.,
Sofia, S., and Techel, F.). presented at: EAWS General Assembly, Davos,
Switzerland, June 2021, 2021. a, b, c
Efron, B.: Bootstrap methods: another look at the jackknife, Ann.
Stat., 7, 1–26,
1979. a
Engeset, R. V., Pfuhl, G., Landrø, M., Mannberg, A., and Hetland, A.: Communicating public avalanche warnings – what works?, Nat. Hazards Earth Syst. Sci., 18, 2537–2559, https://doi.org/10.5194/nhess-18-2537-2018, 2018. a
Floyer, J., Klassen, K., Horton, S., and Haegeli, P.: Looking to the 20's:
computer-assisted avalanche forecasting in Canada, in: Proceedings ISSW 2016.
International Snow Science Workshop, 2–7 October 2016, Breckenridge, Co.,
1245–1249, 2016. a
Goffin, R. and Olson, J.: Is it all relative? Comparative judgments and the
possible improvement of self-ratings and ratings of others, Perspect.
Psychol. Sci., 6, 48–60, 2011. a
Hollander, M. and Wolfe, D.: Nonparametric Statistical Methods, New York, John
Wiley and Sons, 528 p., 1973. a
Hutter, V., Techel, F., and Purves, R. S.: How is avalanche danger described in textual descriptions in avalanche forecasts in Switzerland? Consistency between forecasters and avalanche danger, Nat. Hazards Earth Syst. Sci., 21, 3879–3897, https://doi.org/10.5194/nhess-21-3879-2021, 2021. a, b, c, d
Kuter, K.: Essential probability theory for data science (DSCI 500B), Saint
Mary's College, https://stats.libretexts.org/Courses/Saint_Mary's_College_Notre_Dame/DSCI_500B_Essential_Probability_Theory_for_Data_Science_(Kuter) (last access: 8 February 2022), 2020. a
LaChapelle, E.: The fundamental process in conventional avalanche
forecasting, J. Glaciol., 26, 75–84,
https://doi.org/10.3189/S0022143000010601, 1980. a, b
Lehning, M., Bartelt, P., Brown, B., Russi, T., Stöckli, U., and Zimmerli,
M.: Snowpack model calculations for avalanche warning based upon a new
network of weather and snow stations, Cold Reg. Sci. Technol., 30, 145–157, https://doi.org/10.1016/S0165-232X(99)00022-1, 1999. a
Logan, S. and Greene, E.: Patterns in avalanche events and regional scale
avalanche forecasts in Colorado, USA, in: Proceedings ISSW 2018.
International Snow Science Workshop, 7–12 October 2018, Innsbruck, Austria,
1059–1062, 2018. a
MacGregor, D.: Principles of forecasting: a handbook for researchers and
practitioners, vol. 30 of International Series in Operations Research &
Management Science, chap. Decomposition for judgmental forecasting and
estimation, 107–123, Springer, Boston, MA,
https://doi.org/10.1007/978-0-306-47630-3_6, 2001. a
McClung, D.: The elements of applied avalanche forecasting, part I: The human
issues, Nat. Hazard., 26, 111–129, https://doi.org/10.1023/A:1015665432221, 2002. a
McClung, D. and Schaerer, P.: The Avalanche Handbook, The Mountaineers,
Seattle, WA., 3rd Edn., 2006. a
MeteoSwiss: COSMO forecasting system,
https://www.meteoswiss.admin.ch/home/measurement-and-forecasting-systems/warning-and-forecasting-systems/cosmo-forecasting-system.html,
last access: 6 January 2022. a
Miller, G.: The magical number seven, plus or minus two: Some limits on our
capacity for processing information, Psychol. Rev., 63, 81–97,
https://doi.org/10.1037/h0043158, 1956. a
Morin, S., Horton, S., Techel, F., Bavay, M., Coléou, C., Fierz, C., Gobiet,
A., Hagenmuller, P., Lafaysse, M., Ližar, M., Mitterer, C., Monti, F.,
Müller, K., Olefs, M., Snook, J. S., van Herwijnen, A., and Vionnet, V.:
Application of physical snowpack models in support of operational avalanche
hazard forecasting: A status report on current implementations and prospects
for the future, Cold Reg. Sci. Technol., 170, p. 102910,
https://doi.org/10.1016/j.coldregions.2019.102910, 2019. a, b
Murphy, A. H.: What is a good forecast? An essay on the nature of goodness in
weather forecasting, Weather Forecast., 8, 281–293,
https://doi.org/10.1175/1520-0434(1993)008<0281:WIAGFA>2.0.CO;2, 1993. a
Newcombe, R. G.: Interval estimation for the difference between independent
proportions: comparison of eleven methods, Stat. Med., 8,
873–890,
https://doi.org/10.1002/(sici)1097-0258(19980430)17:8<873::aid-sim779>3.0.co;2-i, 1998. a
Pérez-Guillén, C., Techel, F., Hendrick, M., Volpi, M., van Herwijnen, A., Olevski, T., Obozinski, G., Pérez-Cruz, F., and Schweizer, J.: Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2021-341, in review, 2021. a, b, c, d, e
R Core Team: R: A Language and Environment for Statistical Computing, R
Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (last access: 2 June 2022), 2020. a
Ramachandran, K. M. and Tsokos, C. P.: Mathematical Statistics with
Applications in R, Chap. 13 – Empirical methods, 531–568,
Academic Press, 3rd edn., https://doi.org/10.1016/B978-0-12-817815-7.00013-0, 2021. a, b
Ridout, M., Demetrio, C., and Hinde, J.: Models for count data with many zeros,
in: International Biometric Conference, Cape Town, Dec 1998, p. 13,
https://www.semanticscholar.org/paper/Models-for-count-data-with-many-zeros-Ridout-Dem%C3%A9trio/6a99f29a84a90284dabc3396296ab6cea806aa37 (last access: 2 June 2022),
1998. a
Schmudlach, G.: Skitourenguru, https://www.skitourenguru.ch,
last access: 6 January 2022. a
Schmudlach, G. and Köhler, J.: Automated avalanche risk rating of backcountry
ski routes, in: Proceedings ISSW 2016. International Snow Science Workshop,
2–7 October 2016, Breckenridge, Co., 2016, pp. 450–456, 2016. a
Schmudlach, G., Harvey, S., and Dürr, L.: How do experts interpret avalanche
terrain from a map?, in: Proceedings ISSW 2018. International Snow Science
Workshop, 7–12 Oct 2018, Innsbruck, Austria, 1674–1680, 2018. a
Schweizer, J.: The Rutschblock test – procedure and application in
Switzerland, The Avalanche Review, 20, 14–15, 2002. a
Schweizer, J. and Jamieson, B.: Snowpack tests for assessing snow-slope
instability, Ann. Glaciol., 51, 187–194,
https://doi.org/10.3189/172756410791386652, 2010. a, b
Schweizer, J., Kronholm, K., and Wiesinger, T.: Verification of regional
snowpack stability and avalanche danger, Cold Reg. Sci. Technol., 37,
277–288, https://doi.org/10.1016/S0165-232X(03)00070-3, 2003. a
Schweizer, J., Mitterer, C., Techel, F., Stoffel, A., and Reuter, B.: On the relation between avalanche occurrence and avalanche danger level, The Cryosphere, 14, 737–750, https://doi.org/10.5194/tc-14-737-2020, 2020. a
Schweizer, J., Mitterer, C., Reuter, B., and Techel, F.: Avalanche danger level characteristics from field observations of snow instability, The Cryosphere, 15, 3293–3315, https://doi.org/10.5194/tc-15-3293-2021, 2021. a
Simenhois, R. and Birkeland, K.: The Extended Column Test: Test effectiveness,
spatial variability, and comparison with the Propagation Saw Test, Cold
Reg. Sci. Technol., 59, 210–216,
https://doi.org/10.1016/j.coldregions.2009.04.001, 2009. a, b
SLF: Description of automated stations,
https://www.slf.ch/en/avalanche-bulletin-and-snow-situation/measured-values/description-of-automated-stations.html,
last access: 6 January 2022. a
St. Clair, A., Finn, H., and Hageli, P.: Where the rubber of the RISP model
meets the road: Contextualizing risk information seeking and processing with
an avalanche bulletin user typology, Int. J. Disaster Risk
Red., 66, 102626, https://doi.org/10.1016/j.ijdrr.2021.102626, 2021. a
Statham, G., Holeczi, S., and Shandro, B.: Consistency and accuracy of public
avalanche forecasts in Western Canada, in: Proceedings ISSW 2018,
International Snow Science Workshop, 7–12 October 2018, Innsbruck, Austria,
1491–1496, 2018b. a
Techel, F. and Schweizer, J.: On using local avalanche danger level estimates
for regional forecast verification, Cold Reg. Sci. Technol., 144,
52–62, https://doi.org/10.1016/j.coldregions.2017.07.012, 2017. a, b
Techel, F., Winkler, K., Walcher, M., van Herwijnen, A., and Schweizer, J.: On snow stability interpretation of extended column test results, Nat. Hazards Earth Syst. Sci., 20, 1941–1953, https://doi.org/10.5194/nhess-20-1941-2020, 2020c.
a, b, c
Techel, F.: Observational data: avalanche observations, danger signs and stability test results, Switzerland (2016/2017 to 2020/2021), https://doi.org/10.16904/envidat.329, 2022. a
Walcher, M., Mitterer, C., and Lanzanasto, N.: A concept of harmonizing
regional avalanche forecasting, in: Proceedings ISSW 2018, International Snow
Science Workshop, 7–12 October 2018, Innsbruck, Austria, 1166–1171, 2018. a
Wilks, D.: Statistical methods in the atmospheric sciences, vol. 100 of International Geophysics Series, Academic Press, San Diego CA, USA, 3rd
edn., 2011. a
Winkler, K. and Schweizer, J.: Comparison of snow stability tests: Extended
Column Test, Rutschblock test and Compression Test, Cold Reg. Sci.
Technol., 59, 217–226, https://doi.org/10.1016/j.coldregions.2009.05.003, 2009. a, b
Short summary
Can the resolution of forecasts of avalanche danger be increased by using a combination of absolute and comparative judgments? Using 5 years of Swiss avalanche forecasts, we show that, on average, sub-levels assigned to a danger level reflect the expected increase in the number of locations with poor snow stability and in the number and size of avalanches with increasing forecast sub-level.
Can the resolution of forecasts of avalanche danger be increased by using a combination of...
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