scholarly journals On the importance of snowpack stability, its frequency distribution, and avalanche size in assessing the avalanche danger level: a data-driven approach

2020 ◽  
Author(s):  
Frank Techel ◽  
Karsten Müller ◽  
Jürg Schweizer
Keyword(s):  
Frequency Distribution ◽  
Large Data ◽  
Relevant Information ◽  
Data Driven ◽  
Data Set ◽  
Avalanche Size ◽  
Quantitative Evidence ◽  
Avalanche Hazard ◽  
Danger Level ◽  
Avalanche Danger

Abstract. Consistency in assigning an avalanche danger level when forecasting or locally assessing avalanche hazard is essential, but challenging to achieve, as relevant information is often scarce and must be interpreted in the light of uncertainties. Furthermore, the definitions of the danger levels, an ordinal variable, are vague and leave room for interpretation. Decision tools, developed to assist in assigning a danger level, are primarily experience-based due to a lack of data. Here, we address this lack of quantitative evidence by exploring a large data set of stability tests (N = 10,125) and avalanche observations (N = 39,017) from two countries related to the three key factors that characterize avalanche danger: snowpack stability, its frequency distribution and avalanche size. We show that the frequency of the most unstable locations increases with increasing danger level. However, a similarly clear relation between avalanche size and danger level was not found. Only for the higher danger levels the size of the largest avalanche per day and warning region increased. Furthermore, we derive stability distributions typical for the danger levels 1-Low to 4-High using four stability classes (very poor, poor, fair and good), and define frequency classes (none or nearly none, a few, several and many) describing the frequency of the most unstable locations. Combining snowpack stability, its frequency and avalanche size in a simulation experiment, typical descriptions for the four danger levels are obtained. Finally, using the simulated snowpack distributions together with the largest avalanche size in a step-wise approach, as proposed in the Conceptual Model of Avalanche Hazard, we present an example for a data-driven look-up table for avalanche danger assessment. Our findings may aid in refining the definitions of the avalanche danger scale and in fostering its consistent usage.

scholarly journals On the importance of snowpack stability, the frequency distribution of snowpack stability, and avalanche size in assessing the avalanche danger level

2020 ◽  
Vol 14 (10) ◽  
pp. 3503-3521
Author(s):  
Frank Techel ◽  
Karsten Müller ◽  
Jürg Schweizer
Keyword(s):  
Frequency Distribution ◽  
Large Data ◽  
Relevant Information ◽  
Key Factors ◽  
Data Set ◽  
Avalanche Size ◽  
Quantitative Evidence ◽  
Decision Tools ◽  
Danger Level ◽  
Avalanche Danger

Abstract. Consistency in assigning an avalanche danger level when forecasting or locally assessing avalanche hazard is essential but challenging to achieve, as relevant information is often scarce and must be interpreted in light of uncertainties. Furthermore, the definitions of the danger levels, an ordinal variable, are vague and leave room for interpretation. Decision tools developed to assist in assigning a danger level are primarily experience-based due to a lack of data. Here, we address this lack of quantitative evidence by exploring a large data set of stability tests (N=9310) and avalanche observations (N=39 017) from two countries related to the three key factors that characterize avalanche danger: snowpack stability, the frequency distribution of snowpack stability, and avalanche size. We show that the frequency of the most unstable locations increases with increasing danger level. However, a similarly clear relation between avalanche size and danger level was not found. Only for the higher danger levels did the size of the largest avalanche per day and warning region increase. Furthermore, we derive stability distributions typical for the danger levels 1-Low to 4-High using four stability classes (very poor, poor, fair, and good) and define frequency classes describing the frequency of the most unstable locations (none or nearly none, a few, several, and many). Combining snowpack stability, the frequency of stability classes and avalanche size in a simulation experiment, typical descriptions for the four danger levels are obtained. Finally, using the simulated stability distributions together with the largest avalanche size in a stepwise approach, we present a data-driven look-up table for avalanche danger assessment. Our findings may aid in refining the definitions of the avalanche danger scale and in fostering its consistent usage.

scholarly journals On the relation between avalanche occurrence and avalanche danger level

2019 ◽  
Author(s):  
Jürg Schweizer ◽  
Christoph Mitterer ◽  
Frank Techel ◽  
Andreas Stoffel ◽  
Benjamin Reuter
Keyword(s):  
Large Data ◽  
Swiss Alps ◽  
Data Sets ◽  
Snow Avalanches ◽  
Secondary Importance ◽  
Data Set ◽  
Avalanche Size ◽  
Wet Snow ◽  
Danger Level ◽  
Avalanche Danger

Abstract. In many countries with seasonally snow-covered mountain ranges warnings are issued to alert the public about imminent avalanche danger, mostly employing a 5-level danger scale. However, as avalanche danger cannot be measured, the charac-terization of avalanche danger remains qualitative. The probability of avalanche occurrence in combination with the ex-pected avalanche type and size decide on the degree of danger in a given forecast region (≳ 100 km2). To describe ava-lanche occurrence probability the snowpack stability and its spatial distribution need to be assessed. To quantify the rela-tion between avalanche occurrence and avalanche danger level we analyzed a large data set of visually observed ava-lanches from the region of Davos (Eastern Swiss Alps), all with mapped outlines, and compared the avalanche activity to the forecast danger level on the day of occurrence. The number of avalanches per day strongly increased with increasing danger level confirming that not only the release probability but also the frequency of locations with a weakness in the snowpack where avalanches may initiate from, increases within a region. Avalanche size did in general not increase with increasing avalanche danger level, suggesting that avalanche size may be of secondary importance compared to snowpack stability and its distribution when assessing the danger level. Moreover, the frequency of wet-snow avalanches was found to be higher than the frequency of dry-snow avalanches on a given day; also, wet-snow avalanches tended to be larger. This finding may indicate that the danger scale is not used consistently with regard to avalanche type. Although, observed ava-lanche occurrence and avalanche danger level are subject to uncertainties, our findings on the characteristics of avalanche activity may allow revisiting the definitions of the European avalanche danger scale. The description of the danger levels can be improved, in particular by quantifying some of the many proportional quantifiers. For instance, ‘many avalanches’, expected at danger level 4–High, means on the order of 10 avalanches per 100 km2. Whereas our data set is one of the most comprehensive, visually observed avalanche records are known to be inherently incomplete so that our results often refer to a lower limit and should be confirmed using other similarly comprehensive data sets.

scholarly journals On the relation between avalanche occurrence and avalanche danger level

2020 ◽  
Vol 14 (2) ◽  
pp. 737-750 ◽  
Author(s):  
Jürg Schweizer ◽  
Christoph Mitterer ◽  
Frank Techel ◽  
Andreas Stoffel ◽  
Benjamin Reuter
Keyword(s):  
Large Data ◽  
Swiss Alps ◽  
Data Sets ◽  
Snow Avalanches ◽  
Secondary Importance ◽  
Data Set ◽  
Avalanche Size ◽  
Wet Snow ◽  
Danger Level ◽  
Avalanche Danger

Abstract. In many countries with seasonally snow-covered mountain ranges warnings are issued to alert the public about imminent avalanche danger, mostly employing an ordinal, five-level danger scale. However, as avalanche danger cannot be measured, the characterization of avalanche danger remains qualitative. The probability of avalanche occurrence in combination with the expected avalanche type and size decide on the degree of danger in a given forecast region (≳100 km2). To describe avalanche occurrence probability, the snowpack stability and its spatial distribution need to be assessed. To quantify the relation between avalanche occurrence and avalanche danger level, we analyzed a large data set of visually observed avalanches (13 918 in total) from the region of Davos (eastern Swiss Alps, ∼300 km2), all with mapped outlines, and we compared the avalanche activity to the forecast danger level on the day of occurrence (3533 danger ratings). The number of avalanches per day strongly increased with increasing danger level, confirming that not only the release probability but also the frequency of locations with a weakness in the snowpack where avalanches may initiate from increase within a region. Avalanche size did not generally increase with increasing avalanche danger level, suggesting that avalanche size may be of secondary importance compared to snowpack stability and its distribution when assessing the danger level. Moreover, the frequency of wet-snow avalanches was found to be higher than the frequency of dry-snow avalanches for a given day and danger level; also, wet-snow avalanches tended to be larger. This finding may indicate that the danger scale is not used consistently with regard to avalanche type. Even though observed avalanche occurrence and avalanche danger level are subject to uncertainties, our findings on the characteristics of avalanche activity suggest reworking the definitions of the European avalanche danger scale. The description of the danger levels can be improved, in particular by quantifying some of the many proportional quantifiers. For instance, based on our analyses, “many avalanches”, expected at danger level 4-High, means on the order of at least 10 avalanches per 100 km2. Whereas our data set is one of the most comprehensive, visually observed avalanche records are known to be inherently incomplete so that our results often refer to a lower limit and should be confirmed using other similarly comprehensive data sets.

scholarly journals How is avalanche danger described in textual descriptions in avalanche forecasts in Switzerland? Consistency between forecasters and avalanche danger

2021 ◽  
Vol 21 (12) ◽  
pp. 3879-3897
Author(s):  
Veronika Hutter ◽  
Frank Techel ◽  
Ross S. Purves
Keyword(s):  
Data Driven ◽  
Extreme Conditions ◽  
Avalanche Size ◽  
Future Studies ◽  
The Public ◽  
Starting Point ◽  
Efficient Communication ◽  
Level 2 ◽  
Danger Level ◽  
Avalanche Danger

Abstract. Effective and efficient communication of expected avalanche conditions and danger to the public is of great importance, especially where the primary audience of forecasts are recreational, non-expert users. In Europe, avalanche danger is communicated using a pyramid, starting with ordinal levels of avalanche danger and progressing through avalanche-prone locations and avalanche problems to a danger description. In many forecast products, information relating to the trigger required to release an avalanche, the frequency or number of potential triggering spots, and the expected avalanche size is described exclusively in a textual danger description. These danger descriptions are, however, the least standardized part of avalanche forecasts. Taking the perspective of the avalanche forecaster and focusing particularly on terms describing these three characterizing elements of avalanche danger, we investigate first which meaning forecasters assign to the text characterizing these elements and second how these descriptions relate to the forecast danger level. We analyzed almost 6000 danger descriptions in avalanche forecasts published in Switzerland and written using a structured catalogue of phrases with a limited number of words. Words and phrases representing information describing these three elements were labeled and assigned to ordinal classes by Swiss avalanche forecasters. These classes were then related to avalanche danger. Forecasters were relatively consistent in assigning labels to words and phrases with Cohen's kappa values ranging from 0.64 to 0.87. Avalanche danger levels were also described consistently using words and phrases, with for example avalanche size classes increasing monotonically with avalanche danger. However, especially for danger level 2 (moderate), information about key elements of avalanche danger, for instance the frequency or number of potential triggering spots, was often missing in danger descriptions. In general, the analysis of the danger descriptions showed that extreme conditions are described in more detail than intermediate values, highlighting the difficulty of communicating conditions that are neither rare nor frequent or neither small nor large. Our results provide data-driven insights that could be used to refine the ways in which avalanche danger could be communicated. Furthermore, through the perspective of the semiotic triangle, relating a referent (the avalanche situation) through thought (the processing process) to symbols (the textual danger description), we provide an alternative starting point for future studies of avalanche forecast consistency and communication.

scholarly journals Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland

2021 ◽  
Author(s):  
Cristina Pérez-Guillén ◽  
Frank Techel ◽  
Martin Hendrick ◽  
Michele Volpi ◽  
Alec van Herwijnen ◽  
...  
Keyword(s):  
Decision Support ◽  
Snow Cover ◽  
Heterogeneous Data ◽  
Swiss Alps ◽  
Data Driven ◽  
Weather Data ◽  
Data Set ◽  
Snow Conditions ◽  
Danger Level ◽  
Avalanche Danger

Abstract. Even today, the assessment of avalanche danger is by large a subjective, yet data-based decision-making process. Human experts analyze heterogeneous data volumes, diverse in scale, and conclude on the avalanche scenario based on their experience. Nowadays, modern machine learning methods and the rise in computing power in combination with physical snow cover modelling open up new possibilities for developing decision support tools for operational avalanche forecasting. Therefore, we developed a fully data-driven approach to predict the regional avalanche danger level, the key component in public avalanche forecasts, for dry-snow conditions in the Swiss Alps. Using a large data set of more than 20 years of meteorological data measured by a network of automated weather stations, which are located at the elevation of potential avalanche starting zones, and snow cover simulations driven with these input weather data, we trained two random forest (RF) classifiers. The first classifier (RF #1) was trained relying on the forecast danger levels published in the avalanche bulletin. Given the uncertainty related to a forecast danger level as a target variable, we trained a second classifier (RF #2), relying on a quality-controlled subset of danger level labels. We optimized the RF classifiers by selecting the best set of input features combining meteorological variables and features extracted from the simulated profiles. The accuracy of the danger level predictions ranged between 74 % and 76 % for RF #1, and between 72 % and 78 % for RF #2, with both models achieving better performance than previously developed methods. To assess the accuracy of the forecast, and thus the quality of our labels, we relied on nowcast assessments of avalanche danger by well-trained observers. The performance of both models was similar to the accuracy of the current experience-based Swiss avalanche forecasts (which is estimated to 76 %). The models performed consistently well throughout the Swiss Alps, thus in different climatic regions, albeit with some regional differences. A prototype model with the RF classifiers was already tested in a semi-operational setting by the Swiss avalanche warning service during the winter 2020-2021. The promising results suggest that the model may well have potential to become a valuable, supplementary decision support tool for avalanche forecasters when assessing avalanche hazard.

SKT

2021 ◽  
Vol 14 (11) ◽  
pp. 2369-2382
Author(s):  
Monica Chiosa ◽  
Thomas B. Preußer ◽  
Gustavo Alonso
Keyword(s):  
Frequency Distribution ◽  
Empirical Evaluation ◽  
Large Data ◽  
Cloud Service ◽  
Data Sets ◽  
Data Set ◽  
Single Pass ◽  
Trade Offs ◽  
Significant Performance ◽  
Spatial Architecture

Data analysts often need to characterize a data stream as a first step to its further processing. Some of the initial insights to be gained include, e.g., the cardinality of the data set and its frequency distribution. Such information is typically extracted by using sketch algorithms, now widely employed to process very large data sets in manageable space and in a single pass over the data. Often, analysts need more than one parameter to characterize the stream. However, computing multiple sketches becomes expensive even when using high-end CPUs. Exploiting the increasing adoption of hardware accelerators, this paper proposes SKT , an FPGA-based accelerator that can compute several sketches along with basic statistics (average, max, min, etc.) in a single pass over the data. SKT has been designed to characterize a data set by calculating its cardinality, its second frequency moment, and its frequency distribution. The design processes data streams coming either from PCIe or TCP/IP, and it is built to fit emerging cloud service architectures, such as Microsoft's Catapult or Amazon's AQUA. The paper explores the trade-offs of designing sketch algorithms on a spatial architecture and how to combine several sketch algorithms into a single design. The empirical evaluation shows how SKT on an FPGA offers a significant performance gain over high-end, server-class CPUs.

scholarly journals Exploring the relationship between avalanche hazard and large-scale terrain choices at a helicopter skiing operation – Insight from run list ratings

2019 ◽  
Author(s):  
Reto Sterchi ◽  
Pascal Haegeli ◽  
Patrick Mair
Keyword(s):  
Large Scale ◽  
Selection Process ◽  
Decision Aids ◽  
Large Data ◽  
Limited Attention ◽  
Data Set ◽  
Linear Mixed Effects Model ◽  
Linear Mixed Effects ◽  
Avalanche Hazard ◽  
The Relationship

Abstract. While guides in mechanized skiing operations use a well-established terrain selection process to limit their exposure to avalanche hazard and keep the residual risk at an acceptable level, the relationship between the open/closed status of runs and environmental factors is complex and has so far only received limited attention from research. Using a large data set of over 25 000 operational run list codes from a mechanized skiing operation, we applied a general linear mixed effects model to explore the relationship between acceptable skiing terrain (i.e., status open) and avalanche hazard conditions. Our results show that the magnitude of the effect of avalanche hazard on run list codes depends on the type of terrain that is being assessed by the guiding team. Ski runs in severe alpine terrain with steep lines through large avalanche slopes are much more susceptible to increases in avalanche hazard than less severe terrain. However, our results also highlight the strong effects of recent skiing on the run coding and thus the importance of prior first-hand experience. Expressing these relationships numerically provides an important step towards the development of meaningful decision aids, which can assist commercial operations to manage their avalanche risk more effectively and efficiently.

scholarly journals Car-Following Described by Blending Data-Driven and Analytical Models: A Gaussian Process Regression Approach

2021 ◽  
pp. 036119812110326
Author(s):  
Ignasi Echaniz Soldevila ◽  
Victor L. Knoop ◽  
Serge Hoogendoorn
Keyword(s):  
Gaussian Process Regression ◽  
Large Data ◽  
Driving Behavior ◽  
Large Data Sets ◽  
Training Data ◽  
Data Driven ◽  
Data Sets ◽  
Data Set ◽  
Car Following ◽  
New Variables

Traffic engineers rely on microscopic traffic models to design, plan, and operate a wide range of traffic applications. Recently, large data sets, yet incomplete and from small space regions, are becoming available thanks to technology improvements and governmental efforts. With this study we aim to gain new empirical insights into longitudinal driving behavior and to formulate a model which can benefit from these new challenging data sources. This paper proposes an application of an existing formulation, Gaussian process regression (GPR), to describe individual longitudinal driving behavior of drivers. The method integrates a parametric and a non-parametric mathematical formulation. The model predicts individual driver’s acceleration given a set of variables. It uses the GPR to make predictions when there exists correlation between new input and the training data set. The data-driven model benefits from a large training data set to capture all driver longitudinal behavior, which would be difficult to fit in fixed parametric equation(s). The methodology allows us to train models with new variables without the need of altering the model formulation. And importantly, the model also uses existing traditional parametric car-following models to predict acceleration when no similar situations are found in the training data set. A case study using radar data in an urban environment shows that a hybrid model performs better than parametric model alone and suggests that traffic light status over time influences drivers’ acceleration. This methodology can help engineers to use large data sets and to find new variables to describe traffic behavior.

scholarly journals Avalanche danger level characteristics from field observations of snow instability

2021 ◽  
Vol 15 (7) ◽  
pp. 3293-3315
Author(s):  
Jürg Schweizer ◽  
Christoph Mitterer ◽  
Benjamin Reuter ◽  
Frank Techel
Keyword(s):  
Spatial Frequency ◽  
Surface Quality ◽  
Snow Depth ◽  
Field Observations ◽  
Snow Surface ◽  
Stability Class ◽  
Data Set ◽  
Drifting Snow ◽  
Danger Level ◽  
Avalanche Danger

Abstract. Avalanche danger levels are described in qualitative terms that mostly are not amenable to measurements or observations. However, estimating and improving forecast consistency and accuracy require descriptors that can be observed or measured. Therefore, we aim to characterize the avalanche danger levels based on expert field observations of snow instability. We analyzed 589 field observations by experienced researchers and forecasters recorded mostly in the region of Davos (Switzerland) during 18 winter seasons (2001–2002 to 2018–2019). The data include a snow profile with a stability test (rutschblock, RB) and observations on snow surface quality, drifting snow, signs of instability and avalanche activity. In addition, observers provided their estimate of the local avalanche danger level. A snow stability class (very poor, poor, fair, good, very good) was assigned to each profile based on RB score, RB release type and snowpack characteristics. First, we describe some of the key snowpack characteristics of the data set. In most cases, the failure layer included persistent grain types even after a recent snowfall. We then related snow instability data to the local avalanche danger level. For the danger levels 1–Low to 4–High, we derived typical stability distributions. The proportions of profiles rated poor and very poor clearly increased with increasing danger level. For our data set, the proportions were 5 %, 13 %, 49 % and 63 % for the danger levels 1–Low to 4–High, respectively. Furthermore, we related the local avalanche danger level to the occurrence of signs of instability such as whumpfs, shooting cracks and recent avalanches. The absence of signs of instability was most closely related to 1–Low and the presence of them to 3–Considerable. Adding the snow stability class and the 3 d sum of new snow depth improved the discrimination between the lower three danger levels. Still, 2–Moderate was not well described. Nevertheless, we propose some typical situations that approximately characterize each of the danger levels. Obviously, there is no single easily observable set of parameters that would allow us to fully characterize the avalanche danger levels. One reason for this shortcoming is the fact that the snow instability data we analyzed usually lack information on spatial frequency, which is needed to reliably assess the danger level.

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