Articles | Volume 22, issue 6
https://doi.org/10.5194/nhess-22-2031-2022
© 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-22-2031-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
14 Jun 2022
Research article | Highlight paper |
| 14 Jun 2022
| 14 Jun 2022
Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland
Cristina Pérez-Guillén
CORRESPONDING AUTHOR
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Frank Techel
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Martin Hendrick
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Michele Volpi
Swiss Data Science Center, ETH Zurich and EPFL, Zurich, Switzerland
Alec van Herwijnen
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Tasko Olevski
Swiss Data Science Center, ETH Zurich and EPFL, Zurich, Switzerland
Guillaume Obozinski
Swiss Data Science Center, ETH Zurich and EPFL, Zurich, Switzerland
Fernando Pérez-Cruz
Swiss Data Science Center, ETH Zurich and EPFL, Zurich, Switzerland
Department of Computer Science, ETH Zurich, Zurich, Switzerland
Jürg Schweizer
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Related authors
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.
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.
Frank Techel, Stephanie Mayer, Cristina Pérez-Guillén, Günter Schmudlach, and Kurt Winkler
Nat. Hazards Earth Syst. Sci., 22, 1911–1930, https://doi.org/10.5194/nhess-22-1911-2022, https://doi.org/10.5194/nhess-22-1911-2022, 2022
Short summary
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.
Frank Techel, Ross S. Purves, Stephanie Mayer, Günter Schmudlach, and Kurt Winkler
Nat. Hazards Earth Syst. Sci., 25, 3333–3353, https://doi.org/10.5194/nhess-25-3333-2025, https://doi.org/10.5194/nhess-25-3333-2025, 2025
Short summary
Short summary
We tested how well fully data- and model-driven avalanche forecasts compare to human-made forecasts, which also integrate added context like field observations or model output. Using data from Switzerland over three winters, we found that models – even without this extra input – performed nearly as well. While human forecasts still have a slight edge, model predictions already offer reliable support for daily avalanche forecasting.
Francois Kamper, Fabian Walter, Patrick Paitz, Matthias Meyer, Michele Volpi, and Mathieu Salzmann
EGUsphere, https://doi.org/10.5194/egusphere-2025-3864, https://doi.org/10.5194/egusphere-2025-3864, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
We use anomaly detection to automatically find patterns in seismic data that may signal dangerous mass-movement events such as landslides, glacier collapses, or debris flows. Because such movements are rare, our approach reduces the amount of data that must be analyzed to find them, whether by experts or clustering procedures. We demonstrate the usefulness of our approach by mining for mass movements in Switzerland and Greenland.
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.
Grégoire Bobillier, Bertil Trottet, Bastian Bergfeld, Ron Simenhois, Alec van Herwijnen, Jürg Schweizer, and Johan Gaume
Nat. Hazards Earth Syst. Sci., 25, 2215–2223, https://doi.org/10.5194/nhess-25-2215-2025, https://doi.org/10.5194/nhess-25-2215-2025, 2025
Short summary
Short summary
Our study investigates the initiation of snow slab avalanches. Combining experimental data with numerical simulations, we show that on gentle slopes, cracks form and propagate due to compressive fractures within a weak layer. On steeper slopes, crack velocity can increase dramatically after approximately 5 m due to a fracture mode transition from compression to shear. Understanding these dynamics provides a crucial missing piece in the puzzle of dry-snow slab avalanche formation.
Jakob Boyd Pernov, William H. Aeberhard, Michele Volpi, Eliza Harris, Benjamin Hohermuth, Sakiko Ishino, Ragnhild B. Skeie, Stephan Henne, Ulas Im, Patricia K. Quinn, Lucia M. Upchurch, and Julia Schmale
Atmos. Chem. Phys., 25, 6497–6537, https://doi.org/10.5194/acp-25-6497-2025, https://doi.org/10.5194/acp-25-6497-2025, 2025
Short summary
Short summary
Particulate methanesulfonic acid (MSAp) is vital for the Arctic climate system. Numerical models struggle to reproduce the MSAp seasonal cycle. We evaluate three numerical models and one reanalysis product’s ability to simulate MSAp. We develop data-driven models for MSAp at four Arctic stations. The data-driven models outperform the numerical models and reanalysis product and identified precursor source-, chemical-processing-, and removal-related features as being important for modeling MSAp.
Philipp L. Rosendahl, Johannes Schneider, Grégoire Bobillier, Florian Rheinschmidt, Bastian Bergfeld, Alec van Herwijnen, and Philipp Weißgraeber
Nat. Hazards Earth Syst. Sci., 25, 1975–1991, https://doi.org/10.5194/nhess-25-1975-2025, https://doi.org/10.5194/nhess-25-1975-2025, 2025
Short summary
Short summary
Avalanche formation depends on crack propagation in weak snow layers, but the conditions that stop a crack remain unclear. We show that slab touchdown reduces the energy driving crack growth, which can halt propagation even under static conditions. This suggests that crack arrest is influenced not only by snowpack variability or dynamics but also by mechanical interactions within the snowpack. Our findings refine avalanche prediction models and improve hazard assessment.
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.
Quentin Duchemin, Maria Grazia Zanoni, Marius G. Floriancic, Hansjörg Seybold, Guillaume Obozinski, James W. Kirchner, and Paolo Benettin
EGUsphere, https://doi.org/10.5194/egusphere-2025-1591, https://doi.org/10.5194/egusphere-2025-1591, 2025
Short summary
Short summary
We introduce GAMCR, a data-driven model that estimates how catchments respond to individual precipitation events. We validate GAMCR on synthetic data and demonstrate its ability to investigate the characteristic runoff responses from real-world hydrologic series. GAMCR provides new data-driven opportunities to understand and compare hydrological behavior across different catchments worldwide.
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.
Amelie Fees, Michael Lombardo, Alec van Herwijnen, Peter Lehmann, and Jürg Schweizer
The Cryosphere, 19, 1453–1468, https://doi.org/10.5194/tc-19-1453-2025, https://doi.org/10.5194/tc-19-1453-2025, 2025
Short summary
Short summary
Glide-snow avalanches release at the soil–snow interface due to a loss of friction, which is suspected to be linked to interfacial water. The importance of the interfacial water was investigated with a spatio-temporal monitoring setup for soil and local snow on an avalanche-prone slope. Seven glide-snow avalanches were released on the monitoring grid (winter seasons 2021/22 to 2023/24) and provided insights into the source, quantity, and spatial distribution of interfacial water before avalanche release.
Jan Svoboda, Marc Ruesch, David Liechti, Corinne Jones, Michele Volpi, Michael Zehnder, and Jürg Schweizer
Geosci. Model Dev., 18, 1829–1849, https://doi.org/10.5194/gmd-18-1829-2025, https://doi.org/10.5194/gmd-18-1829-2025, 2025
Short summary
Short summary
Accurately measuring snow height is key for modeling approaches in climate science, snow hydrology, and avalanche forecasting. Erroneous snow height measurements often occur when snow height is low or changes, for instance during snowfall in summer. We prepare a new benchmark dataset with annotated snow height data and demonstrate how to improve the measurement quality using modern deep-learning approaches. Our approach can be easily implemented in a data pipeline for snow modeling.
Michael Lombardo, Amelie Fees, Anders Kaestner, Alec van Herwijnen, Jürg Schweizer, and Peter Lehmann
EGUsphere, https://doi.org/10.5194/egusphere-2025-304, https://doi.org/10.5194/egusphere-2025-304, 2025
Short summary
Short summary
Water flow in snow is important for many applications including snow hydrology and avalanche forecasting. This work investigated the role of capillary forces at the soil-snow interface during capillary rise experiments using neutron radiography. The results showed that the properties of both the snow and the transitional layer below the snow affected the water flow. This work will allow for better representations of water flow across the soil-snow interface in snowpack models.
Bastian Bergfeld, Karl W. Birkeland, Valentin Adam, Philipp L. Rosendahl, and Alec van Herwijnen
Nat. Hazards Earth Syst. Sci., 25, 321–334, https://doi.org/10.5194/nhess-25-321-2025, https://doi.org/10.5194/nhess-25-321-2025, 2025
Short summary
Short summary
To release a slab avalanche, a crack in a weak snow layer beneath a cohesive slab has to propagate. Information on that is essential for assessing avalanche risk. In the field, information can be gathered with the propagation saw test (PST). However, there are different standards on how to cut the PST. In this study, we experimentally investigate the effect of these different column geometries and provide models to correct for imprecise field test geometry effects on the critical cut length.
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.
Amelie Fees, Alec van Herwijnen, Michael Lombardo, Jürg Schweizer, and Peter Lehmann
Nat. Hazards Earth Syst. Sci., 24, 3387–3400, https://doi.org/10.5194/nhess-24-3387-2024, https://doi.org/10.5194/nhess-24-3387-2024, 2024
Short summary
Short summary
Glide-snow avalanches release at the ground–snow interface, and their release process is poorly understood. To investigate the influence of spatial variability (snowpack and basal friction) on avalanche release, we developed a 3D, mechanical, threshold-based model that reproduces an observed release area distribution. A sensitivity analysis showed that the distribution was mostly influenced by the basal friction uniformity, while the variations in snowpack properties had little influence.
Eliza Harris, Philipp Fischer, Maciej P. Lewicki, Dominika Lewicka-Szczebak, Stephen J. Harris, and Fernando Perez-Cruz
Biogeosciences, 21, 3641–3663, https://doi.org/10.5194/bg-21-3641-2024, https://doi.org/10.5194/bg-21-3641-2024, 2024
Short summary
Short summary
Greenhouse gases are produced and consumed via a number of pathways. Quantifying these pathways helps reduce the climate and environmental footprint of anthropogenic activities. The contribution of the pathways can be estimated from the isotopic composition, which acts as a fingerprint for these pathways. We have developed the Time-resolved FRactionation And Mixing Evaluation (TimeFRAME) model to simplify interpretation and estimate the contribution of different pathways and their uncertainty.
Gwendolyn Dasser, Valentin T. Bickel, Marius Rüetschi, Mylène Jacquemart, Mathias Bavay, Elisabeth D. Hafner, Alec van Herwijnen, and Andrea Manconi
EGUsphere, https://doi.org/10.5194/egusphere-2024-1510, https://doi.org/10.5194/egusphere-2024-1510, 2024
Short summary
Short summary
Understanding snowpack wetness is crucial for predicting wet snow avalanches, but detailed data is often limited to certain locations. Using satellite radar, we monitor snow wetness spatially continuously. By combining different radar tracks from Sentinel-1, we improved spatial resolution and tracked snow wetness over several seasons. Our results indicate higher snow wetness to correlate with increased wet snow avalanche activity, suggesting our method can help identify potential risk areas.
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
Revised manuscript accepted 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.
Mathieu Le Breton, Éric Larose, Laurent Baillet, Yves Lejeune, and Alec van Herwijnen
The Cryosphere, 17, 3137–3156, https://doi.org/10.5194/tc-17-3137-2023, https://doi.org/10.5194/tc-17-3137-2023, 2023
Short summary
Short summary
We monitor the amount of snow on the ground using passive radiofrequency identification (RFID) tags. These small and inexpensive tags are wirelessly read by a stationary reader placed above the snowpack. Variations in the radiofrequency phase delay accurately reflect variations in snow amount, known as snow water equivalent. Additionally, each tag is equipped with a sensor that monitors the snow temperature.
Raphaël de Fondeville, Zheng Wu, Enikő Székely, Guillaume Obozinski, and Daniela I. V. Domeisen
Weather Clim. Dynam., 4, 287–307, https://doi.org/10.5194/wcd-4-287-2023, https://doi.org/10.5194/wcd-4-287-2023, 2023
Short summary
Short summary
We propose a fully data-driven, interpretable, and computationally scalable framework to characterize sudden stratospheric warmings (SSWs), extract statistically significant precursors, and produce machine learning (ML) forecasts. By successfully leveraging the long-lasting impact of SSWs, the ML predictions outperform sub-seasonal numerical forecasts for lead times beyond 25 d. Post-processing numerical predictions using their ML counterparts yields a performance increase of up to 20 %.
Bastian Bergfeld, Alec van Herwijnen, Grégoire Bobillier, Philipp L. Rosendahl, Philipp Weißgraeber, Valentin Adam, Jürg Dual, and Jürg Schweizer
Nat. Hazards Earth Syst. Sci., 23, 293–315, https://doi.org/10.5194/nhess-23-293-2023, https://doi.org/10.5194/nhess-23-293-2023, 2023
Short summary
Short summary
For a slab avalanche to release, the snowpack must facilitate crack propagation over large distances. Field measurements on crack propagation at this scale are very scarce. We performed a series of experiments, up to 10 m long, over a period of 10 weeks. Beside the temporal evolution of the mechanical properties of the snowpack, we found that crack speeds were highest for tests resulting in full propagation. Based on these findings, an index for self-sustained crack propagation is proposed.
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.
Frank Techel, Stephanie Mayer, Cristina Pérez-Guillén, Günter Schmudlach, and Kurt Winkler
Nat. Hazards Earth Syst. Sci., 22, 1911–1930, https://doi.org/10.5194/nhess-22-1911-2022, https://doi.org/10.5194/nhess-22-1911-2022, 2022
Short summary
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.
Achille Capelli, Franziska Koch, Patrick Henkel, Markus Lamm, Florian Appel, Christoph Marty, and Jürg Schweizer
The Cryosphere, 16, 505–531, https://doi.org/10.5194/tc-16-505-2022, https://doi.org/10.5194/tc-16-505-2022, 2022
Short summary
Short summary
Snow occurrence, snow amount, snow density and liquid water content (LWC) can vary considerably with climatic conditions and elevation. We show that low-cost Global Navigation Satellite System (GNSS) sensors as GPS can be used for reliably measuring the amount of water stored in the snowpack or snow water equivalent (SWE), snow depth and the LWC under a broad range of climatic conditions met at different elevations in the Swiss Alps.
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.
Sebastian Landwehr, Michele Volpi, F. Alexander Haumann, Charlotte M. Robinson, Iris Thurnherr, Valerio Ferracci, Andrea Baccarini, Jenny Thomas, Irina Gorodetskaya, Christian Tatzelt, Silvia Henning, Rob L. Modini, Heather J. Forrer, Yajuan Lin, Nicolas Cassar, Rafel Simó, Christel Hassler, Alireza Moallemi, Sarah E. Fawcett, Neil Harris, Ruth Airs, Marzieh H. Derkani, Alberto Alberello, Alessandro Toffoli, Gang Chen, Pablo Rodríguez-Ros, Marina Zamanillo, Pau Cortés-Greus, Lei Xue, Conor G. Bolas, Katherine C. Leonard, Fernando Perez-Cruz, David Walton, and Julia Schmale
Earth Syst. Dynam., 12, 1295–1369, https://doi.org/10.5194/esd-12-1295-2021, https://doi.org/10.5194/esd-12-1295-2021, 2021
Short summary
Short summary
The Antarctic Circumnavigation Expedition surveyed a large number of variables describing the dynamic state of ocean and atmosphere, freshwater cycle, atmospheric chemistry, ocean biogeochemistry, and microbiology in the Southern Ocean. To reduce the dimensionality of the dataset, we apply a sparse principal component analysis and identify temporal patterns from diurnal to seasonal cycles, as well as geographical gradients and
hotspotsof interaction. Code and data are open access.
Nora Helbig, Michael Schirmer, Jan Magnusson, Flavia Mäder, Alec van Herwijnen, Louis Quéno, Yves Bühler, Jeff S. Deems, and Simon Gascoin
The Cryosphere, 15, 4607–4624, https://doi.org/10.5194/tc-15-4607-2021, https://doi.org/10.5194/tc-15-4607-2021, 2021
Short summary
Short summary
The snow cover spatial variability in mountains changes considerably over the course of a snow season. In applications such as weather, climate and hydrological predictions the fractional snow-covered area is therefore an essential parameter characterizing how much of the ground surface in a grid cell is currently covered by snow. We present a seasonal algorithm and a spatiotemporal evaluation suggesting that the algorithm can be applied in other geographic regions by any snow model application.
Zheng Wu, Bernat Jiménez-Esteve, Raphaël de Fondeville, Enikő Székely, Guillaume Obozinski, William T. Ball, and Daniela I. V. Domeisen
Weather Clim. Dynam., 2, 841–865, https://doi.org/10.5194/wcd-2-841-2021, https://doi.org/10.5194/wcd-2-841-2021, 2021
Short summary
Short summary
We use an advanced statistical approach to investigate the dynamics of the development of sudden stratospheric warming (SSW) events in the winter Northern Hemisphere. We identify distinct signals that are representative of these events and their event type at lead times beyond currently predictable lead times. The results can be viewed as a promising step towards improving the predictability of SSWs in the future by using more advanced statistical methods in operational forecasting systems.
Bastian Bergfeld, Alec van Herwijnen, Benjamin Reuter, Grégoire Bobillier, Jürg Dual, and Jürg Schweizer
The Cryosphere, 15, 3539–3553, https://doi.org/10.5194/tc-15-3539-2021, https://doi.org/10.5194/tc-15-3539-2021, 2021
Short summary
Short summary
The modern picture of the snow slab avalanche release process involves a
dynamic crack propagation phasein which a whole slope becomes detached. The present work contains the first field methodology which provides the temporal and spatial resolution necessary to study this phase. We demonstrate the versatile capabilities and accuracy of our method by revealing intricate dynamics and present how to determine relevant characteristics of crack propagation such as crack speed.
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.
Michaela Wenner, Clément Hibert, Alec van Herwijnen, Lorenz Meier, and Fabian Walter
Nat. Hazards Earth Syst. Sci., 21, 339–361, https://doi.org/10.5194/nhess-21-339-2021, https://doi.org/10.5194/nhess-21-339-2021, 2021
Short summary
Short summary
Mass movements constitute a risk to property and human life. In this study we use machine learning to automatically detect and classify slope failure events using ground vibrations. We explore the influence of non-ideal though commonly encountered conditions: poor network coverage, small number of events, and low signal-to-noise ratios. Our approach enables us to detect the occurrence of rare events of high interest in a large data set of more than a million windowed seismic signals.
Bettina Richter, Alec van Herwijnen, Mathias W. Rotach, and Jürg Schweizer
Nat. Hazards Earth Syst. Sci., 20, 2873–2888, https://doi.org/10.5194/nhess-20-2873-2020, https://doi.org/10.5194/nhess-20-2873-2020, 2020
Short summary
Short summary
We investigated the sensitivity of modeled snow instability to uncertainties in meteorological input, typically found in complex terrain. The formation of the weak layer was very robust due to the long dry period, indicated by a widespread avalanche problem. Once a weak layer has formed, precipitation mostly determined slab and weak layer properties and hence snow instability. When spatially assessing snow instability for avalanche forecasting, accurate precipitation patterns have to be known.
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.
Louis Quéno, Charles Fierz, Alec van Herwijnen, Dylan Longridge, and Nander Wever
The Cryosphere, 14, 3449–3464, https://doi.org/10.5194/tc-14-3449-2020, https://doi.org/10.5194/tc-14-3449-2020, 2020
Short summary
Short summary
Deep ice layers may form in the snowpack due to preferential water flow with impacts on the snowpack mechanical, hydrological and thermodynamical properties. We studied their formation and evolution at a high-altitude alpine site, combining a comprehensive observation dataset at a daily frequency (with traditional snowpack observations, penetration resistance and radar measurements) and detailed snowpack modeling, including a new parameterization of ice formation in the 1-D SNOWPACK model.
Cited articles
Baggi, S. and Schweizer, J.: Characteristics of wet-snow avalanche activity: 20 years of observations from a high alpine valley (Dischma, Switzerland), Nat. Hazards, 50, 97–108, https://doi.org/10.1007/s11069-008-9322-7, 2009. a
Bavay, M. and Egger, T.: MeteoIO 2.4.2: a preprocessing library for
meteorological data, Geosci. Model Dev., 7, 3135–3151, https://doi.org/10.5194/gmd-7-3135-2014, 2014. a
Bowler, N. E.: Explicitly accounting for observation error in categorical
verification of forecasts, Mon. Weather Rev., 134, 1600–1606, https://doi.org/10.1175/MWR3138.1, 2006. a
Brabec, B. and Meister, R.: A nearest-neighbor model for regional avalanche
forecasting, Ann. Glaciol., 32, 130–134, https://doi.org/10.3189/172756401781819247,
2001. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. a
Bühler, Y., Hafner, E. D., Zweifel, B., Zesiger, M., and Heisig, H.: Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps, The Cryosphere, 13, 3225–3238,
https://doi.org/10.5194/tc-13-3225-2019, 2019. a
Chen, C., Liaw, A., and Breiman, L.: Using random forest to learn imbalanced data, University of California, Berkeley, 110, 24, 2004. a
Davis, R. E., Elder, K., Howlett, D., and Bouzaglou, E.: Relating storm and
weather factors to dry slab avalanche activity at Alta, Utah, and Mammoth
Mountain, California, using classification and regression trees, Cold Reg.
Sci. Technol., 30, 79–89, https://doi.org/10.1016/S0165-232X(99)00032-4, 1999. a
Dkengne Sielenou, P., Viallon-Galinier, L., Hagenmuller, P., Naveau, P., Morin, S., Dumont, M., Verfaillie, D., and Eckert, N.: Combining random forests and class-balancing to discriminate between three classes of avalanche activity in the French Alps, Cold Reg. Sci. Technol., 187, 103276,
https://doi.org/10.1016/j.coldregions.2021.103276, 2021. a, b
Dreier, L., Harvey, S., van Herwijnen, A., and Mitterer, C.: Relating
meteorological parameters to glide-snow avalanche activity, Cold Reg. Sci.
Technol., 128, 57–68, https://doi.org/10.1016/j.coldregions.2016.05.003, 2016. a
EAWS: Information pyramid,
https://www.avalanches.org/standards/information-pyramid/ (last access: 18 June 2021), 2021b. a
EAWS: Avalanche Problems, Edited, EAWS – European Avalanche Warning
Services,
https://www.avalanches.org/wp-content/uploads/2019/05/Typical_avalanche_problems-EAWS.pdf
(last access: 18 June 2021), 2021c. a
Fierz, C., Armstrong, R. L., Durand, Y., Etchevers, P., Greene, E., McClung,
D. M., Nishimura, K., Satyawali, P. K., and Sokratov, S. A.: The
international classification for seasonal snow on the ground, https://unesdoc.unesco.org/ark:/48223/pf0000186462 (last access: 31 May 2022), 2009. a
Föhn, P. M. B. and Schweizer, J.: Verification of avalanche danger with
respect to avalanche forecasting, in: Les apports de la recherche scientifique à la sécurité neige glace et avalanche. Actes de
Colloque, Chamonix, 30 mai–3 juin 1995, edited by: Sivardière, F., ANENA, Grenoble, France, 151–156, 1995. a
Frénay, B. and Verleysen, M.: Classification in the presence of label
noise: a survey, IEEE T. Neural Netw. Learn. Syst., 25, 845–869, 2013. a
Gaume, J., van Herwijnen, A., Chambon, G., Wever, N., and Schweizer, J.: Snow
fracture in relation to slab avalanche release: critical state for the onset
of crack propagation, The Cryosphere, 11, 217–228,
https://doi.org/10.5194/tc-11-217-2017, 2017. a
Guyon, I., Weston, J., Barnhill, S., and Vapnik, V.: Gene selection for cancer classification using support vector machines, Mach. Learn., 46, 389–422, 2002. a
Heck, M., Van Herwijnen, A., Hammer, C., Hobiger, M., Schweizer, J., and
Fäh, D.: Automatic detection of avalanches combining array classification
and localization, Earth Surf. Dynam., 7, 491–503,
https://doi.org/10.5194/esurf-7-491-2019, 2019. a
Hendrikx, J., Murphy, M., and Onslow, T.: Classification trees as a tool for
operational avalanche forecasting on the Seward Highway, Alaska, Cold Reg.
Sci. Technol., 97, 113–120, https://doi.org/10.1016/j.coldregions.2013.08.009, 2014. a
Hendrikx, J., Dreier, L., Ulivieri, G., Sanderson, J., Jones, A., and Steinkogler, W.: Evaluation of an infrasound detection system for avalanches in Rogers Pass, Canada, in: Proceedings ISSW 2018, International Snow Science Workshop, 7–12 October 2018, Innsbruck, Austria, 171–175, 2018. a
Jamieson, B., Campbell, C., and Jones, A.: Verification of Canadian avalanche bulletins including spatial and temporal scale effects, Cold Reg. Sci.
Technol., 51, 204–213, https://doi.org/10.1016/j.coldregions.2007.03.012, 2008. a
Jamieson, J. and Johnston, C.: Refinements to the stability index for
skier-triggered dry-slab avalanches, Ann. Glaciol., 26, 296–302,
https://doi.org/10.3189/1998AoG26-1-296-302, 1998. a, b, c
Kahneman, D., Sibony, O., and Sunstein, C. R.: Noise – A flaw in human judgment, Hachette Book Group, New York, USA, 454 pp., ISBN 10 0316451401, 2021. a
LaChapelle, E. R.: The fundamental processes in conventional Alavalanche
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, b, c, d
Lehning, M., Bartelt, P., Brown, B., Fierz, C., and Satyawali, P.: A physical
SNOWPACK model for the Swiss avalanche warning: Part II. Snow microstructure,
Cold Reg. Sci. Technol., 35, 147–167, https://doi.org/10.1016/S0165-232X(02)00073-3, 2002. a
Maas, A., Rottensteiner, F., and Heipke, C.: Using label noise robust logistic regression for automated updating of topographic geospatial databases., in: XXIII ISPRS Congress, Commission VII 3 (2016), 133–140. https://doi.org/10.5194/isprsannals-III-7-133-2016, 2016. a
Mayer, S., van Herwijnen, A., Ulivieri, G., and Schweizer, J.: Evaluating the
performance of an operational infrasound avalanche detection system at three
locations in the Swiss Alps during two winter seasons, Cold Reg. Sci. Technol., 173, 102962, https://doi.org/10.1016/j.coldregions.2019.102962, 2020. a
McClung, D. and Schaerer, P. A.: The avalanche handbook, The Mountaineers
Books, ISBN 13 978-0898868098, 2006. a
McClung, D. M.: Predictions in avalanche forecasting, Ann. Glaciol., 31, 377–381, https://doi.org/10.3189/172756400781820507, 2000. a
Mitterer, C. and Schweizer, J.: Analysis of the snow-atmosphere energy balance during wet-snow instabilities and implications for avalanche prediction, The Cryosphere, 7, 205–216, https://doi.org/10.5194/tc-7-205-2013, 2013. a
Möhle, S., Bründl, M., and Beierle, C.: Modeling a system for decision support in snow avalanche warning using balanced random forest and weighted random forest, in: Lecture notes in computer science: Vol. 8722, Artificial intelligence: methodology, systems, and applications, Proceedings, edited by: Agre, G., Hitzler, P., Krisnadhi, A. A., and Kuznetsov, S. O., Springer, 80–91, https://doi.org/10.1007/978-3-319-10554-3_8, 2014. a
Monti, F., Schweizer, J., and Fierz, C.: Hardness estimation and weak layer
detection in simulated snow stratigraphy, Cold Reg. Sci. Technol., 103,
82–90, https://doi.org/10.1016/j.coldregions.2014.03.009, 2014. a
Monti, F., Gaume, J., van Herwijnen, A., and Schweizer, J.: Snow instability
evaluation: calculating the skier-induced stress in a multi-layered snowpack,
Nat. Hazards Earth Syst. Sci., 16, 775–788, https://doi.org/10.5194/nhess-16-775-2016,
2016. a, b
Morin, S., Horton, S., Techel, F., Bavay, M., Coléou, C., Fierz, C., Gobiet, A., Hagenmuller, P., Lafaysse, M., Ližar, M., and Mitterer, C.,: 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, 102910, https://doi.org/10.1016/j.coldregions.2019.102910, 2020. a, b, c
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., and Vanderplas, J.: Scikit-learn: Machine learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011. a
Pelletier, C., Valero, S., Inglada, J., Champion, N., Marais Sicre, C., and
Dedieu, G.: Effect of training class label noise on classification
performances for land cover mapping with satellite image time series, Remote
Sens., 9, 173, https://doi.org/10.3390/rs9020173, 2017. a
Pérez-Guillén, C.: Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland, Renkulab [code], https://renkulab.io/gitlab/deapsnow/predictions_avalanche_danger-level_switzerland, last access: 9 June 2022. a
Pérez-Guillén, C., Techel, F., Hendrick, M., Volpi, M., van Herwijnen, A., Olevski, T., Obozinski, G., Pérez-Cruz, F., and Schweizer, J.: Weather, snowpack and danger ratings data for automated avalanche danger level predictions, EnviDat [data set], https://doi.org/10.16904/envidat.330, 2022. a
Perla, R. I.: On contributory factors in avalanche hazard evaluation, Can.
Geotech. J., 7, 414–419, https://doi.org/10.1139/t70-053, 1970. a
Pozdnoukhov, A., Purves, R. S., and Kanevski, M.: Applying machine learning
methods to avalanche forecasting, Ann. Glaciol., 49, 107–113,
https://doi.org/10.3189/172756408787814870, 2008. a
Pozdnoukhov, A., Matasci, G., Kanevski, M., and Purves, R. S.: Spatio-temporal avalanche forecasting with Support Vector Machines, Nat. Hazards Earth Syst. Sci., 11, 367–382, https://doi.org/10.5194/nhess-11-367-2011, 2011. a
Purves, R., Morrison, K., Moss, G., and Wright, D.: Nearest neighbours for
avalanche forecasting in Scotland – development, verification and
optimisation of a model, Cold Reg. Sci. Technol., 37, 343–355,
https://doi.org/10.1016/S0165-232X(03)00075-2, 2003. a
Richter, B., Schweizer, J., Rotach, M. W., and van Herwijnen, A.: Validating
modeled critical crack length for crack propagation in the snow cover model
SNOWPACK, The Cryosphere, 13, 3353–3366, https://doi.org/10.5194/tc-13-3353-2019, 2019. a
Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., and
Rigol-Sanchez, J. P.: An assessment of the effectiveness of a random forest
classifier for land-cover classification, ISPRS J. Photogram. Remote Sens., 67, 93–104, https://doi.org/10.1016/j.isprsjprs.2011.11.002, 2012. a
Schirmer, M., Lehning, M., and Schweizer, J.: Statistical forecasting of
regional avalanche danger using simulated snow-cover data, J. Glaciol., 55,
761–768, https://doi.org/10.3189/002214309790152429, 2009. a, b
Schweizer, J.: Verifikation des Lawinenbulletins, in: Schnee und Lawinen in den Schweizer Alpen, Winter 2004/2005, Wetter, Schneedecke und Lawinengefahr, Winterbericht SLF, edited by: Pielmeier, C., Aebi, M., and Schweizer, J., Eidg. Institut für Schnee- und Lawinenforschung SLF, Davos, Switzerland, 91–99, 2007. a, b, c
Schweizer, J. and Föhn, P. M.: Avalanche forecasting – an expert system
approach, J. Glaciol., 42, 318–332, https://doi.org/10.3189/S0022143000004172, 1996. a, b
Schweizer, J. and Jamieson, J. B.: A threshold sum approach to stability
evaluation of manual snow profiles, Cold Reg. Sci. Technol., 47, 50–59,
https://doi.org/10.1016/j.coldregions.2006.08.011, 2007. a
Schweizer, J., Föhn, P., and Plüss, C.: COGENSYS Judgment Processor (PARADOCS) als Hilfmittel für die Lawinenwarnung, Interner Bericht, Report No. 675, Eidgenössisches Institut für Schnee- und Lawinenforschung, https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:30627 (last access: 9 June 2022), 1992. a
Schweizer, J., Jamieson, J. B., and Skjonsberg, D.: Avalanche forecasting for transportation corridor and backcountry in Glacier National Park (BC, Canada), in: 25 Years of Snow Avalanche Research, Voss, Norway, 12–16 May 1998, NGI Publication, Vol. 203, edited by: Hestnes, E., Norwegian Geotechnical Institute, Oslo, Norway, 238–243, 1998. a
Schweizer, J., Bellaire, S., Fierz, C., Lehning, M., and Pielmeier, C.:
Evaluating and improving the stability predictions of the snow cover model
SNOWPACK, Cold Reg. Sci. Technol., 46, 52–59,
https://doi.org/10.1016/j.coldregions.2006.05.007, 2006. 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, b
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, b
SLF: Avalanche bulletin interpretation guide, WSL Institute for Snow and
Avalanche Research – SLF, edition December 2020, p. 53,
https://www.slf.ch/files/user_upload/SLF/Lawinenbulletin_Schneesituation/Wissen_zum_Lawinenbulletin/Interpretationshilfe/Interpretationshilfe_EN.pdf (last access: 2 June 2022), 2020. a, b
Sokolova, M. and Lapalme, G.: A systematic analysis of performance measures for classification tasks, Inform. Process. Manage., 45, 427–437, 2009. a
Statham, G., Haegeli, P., Greene, E., Birkeland, K., Israelson, C., Tremper,
B., Stethem, C., McMahon, B., White, B., and Kelly, J.: A conceptual model of
avalanche hazard, Nat. Hazards, 90, 663–691, https://doi.org/10.1007/s11069-017-3070-5, 2018. a, b
Strobl, C., Boulesteix, A.-L., Zeileis, A., and Hothorn, T.: Bias in random
forest variable importance measures: Illustrations, sources and a solution,
BMC Bioinf., 8, 1–21, https://doi.org/10.1186/1471-2105-8-25, 2007.
a
Techel, F.: On consistency and quality in public avalanche forecasting: a
data-driven approach to forecast verification and to refining definitions of
avalanche danger, PhD thesis, Department of Geography, University of Zurich, Zurich, Switzerland, https://doi.org/10.5167/uzh-199650, 2020. a, b, c, d, e
Techel, F., Zweifel, B., and Winkler, K.: Analysis of avalanche risk factors in backcountry terrain based on usage frequency and accident data in
Switzerland, Nat. Hazards Earth Syst. Sci., 15, 1985–1997,
https://doi.org/10.5194/nhess-15-1985-2015, 2015. a
Techel, F., Müller, K., and Schweizer, J.: On the importance of snowpack
stability, the frequency distribution of snowpack stability and avalanche
size in assessing the avalanche danger level, The Cryosphere, 14, 3503–3521, https://doi.org/10.5194/tc-2020-42, 2020a. a, b, c
Techel, F., Pielmeier, C., and Winkler, K.: Refined dry-snow avalanche danger
ratings in regional avalanche forecasts: Consistent? And better than random?,
Cold Reg. Sci. Technol., 180, 103162, https://doi.org/10.1016/j.coldregions.2020.103162, 2020b. a, b, c
Techel, F., Mayer, S., Pérez-Guillén, C., Schmudlach, G., and Winkler, K.: On the correlation between a sub-level qualifier refining the danger level with observations and models relating to the contributing factors of avalanche danger, Nat. Hazards Earth Syst. Sci., 22, 1911–1930, https://doi.org/10.5194/nhess-22-1911-2022, 2022. a
van Herwijnen, A., Heck, M., and Schweizer, J.: Forecasting snow avalanches
using avalanche activity data obtained through seismic monitoring, Cold Reg.
Sci. Technol., 132, 68–80, https://doi.org/10.1016/j.coldregions.2016.09.014, 2016. a
Wever, N., Fierz, C., Mitterer, C., Hirashima, H., and Lehning, M.: Solving
Richards Equation for snow improves snowpack meltwater runoff estimations in
detailed multi-layer snowpack model, The Cryosphere, 8, 257–274,
https://doi.org/10.5194/tc-8-257-2014, 2014. a
Winkler, K., Schmudlach, G., Degraeuwe, B., and Techel, F.: On the correlation between the forecast avalanche danger and avalanche risk taken by backcountry skiers in Switzerland, Cold Reg. Sci. Technol., 188, 103299,
https://doi.org/10.1016/j.coldregions.2021.103299, 2021. a, b
Zweifel, B., Hafner, E., Lucas, C., Marty, C., Techel, F., and Stucki, T.:
Schnee und Lawinen in den Schweizer Alpen. Hydrologisches Jahr 2018/19,
WSL Ber. 86, WSL-Institut für Schnee- und Lawinenforschung – SLF, Davos, 134 pp., https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:22232 (last access: 2 June 2022), 2019. a, b, c
Executive editor
The paper could have a strong impact in the entire Alpine region, where avalanche forecasting is a critical issue to manage
The paper could have a strong impact in the entire Alpine region, where avalanche forecasting is...
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.
A fully data-driven approach to predicting the danger level for dry-snow avalanche conditions in...
Altmetrics
Final-revised paper
Preprint