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 How is avalanche danger described in public avalanche forecasts? Analyzing textual descriptions of avalanche forecasts in Switzerland

2021 ◽  
Author(s):  
Veronika Hutter ◽  
Frank Techel ◽  
Ross S. Purves
Keyword(s):  
Complete Agreement ◽  
Avalanche Size ◽  
The Public ◽  
Starting Point ◽  
Efficient Communication ◽  
Official Definitions ◽  
Level 2 ◽  
Danger Level ◽  
Do So ◽  
Avalanche Danger

Abstract. Efficient communication in public avalanche forecasts is of great importance to clearly inform and warn the public about expected avalanche conditions. In Europe, avalanche danger is communicated using a pyramid, starting with ordinal categories 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 locations, and the expected avalanche size, are described exclusively in the 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 firstly which text symbols are used to describe these elements, and secondly how these descriptions relate to the forecast danger level. We do so 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 analyzed almost 6000 danger descriptions in avalanche forecasts published in Switzerland and written using a structured catalog of phrases with a limited number of words. Text symbols 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 0.67 to 0.87. Nonetheless, even experts were not in complete agreement about the labeling of terms and were less likely to agree on terms not used in official definitions. Avalanche danger levels were categorized relatively consistently using words and phrases, with for example avalanche size classes increasingly monotonically with avalanche danger. However, especially for danger level 2-Moderate, information about key elements was often missing in danger descriptions. In general, the analysis of the danger descriptions showed that extreme conditions are more frequently described in 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 and should be communicated, especially to recreationalists, and provide a starting point for future studies of how users interpret avalanche forecasts.

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.

Data-driven automatic predictions of avalanche danger in Switzerland

2021 ◽  
Author(s):  
Cristina Pérez-Guillén ◽  
Martin Hendrick ◽  
Frank Techel ◽  
Alec van Herwijnen ◽  
Michele Volpi ◽  
...  
Keyword(s):  
Real Time ◽  
Winter Season ◽  
Data Driven ◽  
Target Variable ◽  
Test Set ◽  
Avalanche Forecasting ◽  
The Public ◽  
Data Driven Approach ◽  
Danger Level ◽  
Avalanche Danger

<p>Avalanche forecasting implies predicting current and future snow instability in time and space. In Switzerland, avalanche bulletins are issued daily during the winter season to warn the public about the avalanche hazard, described by region with one of five danger levels. Assessing avalanche danger is by large a data-driven, yet experience-based decision-making process. It involves analysing a multitude of data diverse in scale – time and space, and concluding by expert judgment on the avalanche scenario. Numerous statistical models were developed in the past, but rarely applied due to limited usefulness in operational forecasting. Modern machine learning techniques open up new possibilities for developing support tools for operational avalanche forecasting. With this aim, we developed a data-driven approach based on the supervised Random Forest (RF) classifier to automatically predict the danger level for dry-snow avalanche conditions in the Swiss Alps. A large database of more than 20 years of meteorological data and modelled snow stratigraphy data obtained with the numerical snow cover model SNOWPACK were used to train the RF algorithm. We optimized the model and selected the best set of input features that combine meteorological variables and features extracted from the simulated profiles, resampled at the same daily resolution as the forecasts. Our target variable was the regional danger level forecast in the public bulletin. We evaluated the predictive performance of the RF model with an independent test set with data of two winter seasons (2018-2019 and 2019-2020). The test set accuracy was 72 %, which is slightly lower than the accuracy estimate of the public forecasts (about 76 %). Given this uncertainty in our target variable, we trained an optimized RF model on a subset containing so-called verified avalanche danger levels. The test set accuracy then increased to 80 %. During the winter season 2020-2021, both RF models were tested in operational setting and automatically predicted a ‘nowcast’ and a ‘forecast’ in real-time.  In parallel, we also tested a deep recurrent neural network model, which used a 7-days time series with 3-hours time resolution as input and also predicted the avalanche danger level. We present a comparison of the performance of the three models. This is one of the first times that a data-driven approach is tested in real-time as a feasible tool for operational avalanche forecasting.</p>

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 Making EHRs Reusable: A Common Framework of Data Operations

2021 ◽  
Author(s):  
Miguel Pedrera ◽  
Noelia Garcia ◽  
Paula Rubio ◽  
Juan Luis Cruz ◽  
José Luis Bernal ◽  
...  
Keyword(s):  
Data Extraction ◽  
Tertiary Hospital ◽  
Data Driven ◽  
Future Studies ◽  
Secondary Use ◽  
Starting Point ◽  
Common Framework ◽  
Transformation Processes ◽  
Iso 13606

Reuse of EHRs requires data extraction and transformation processes are based on homogeneous and formalized operations in order to make them understandable, reproducible and auditable. This work aims to define a common framework of data operations for obtaining EHR-derived datasets for secondary use. Thus, 21 operations were identified from different data-driven projects of a 1,300-beds tertiary Hospital. Then, ISO 13606 standard was used to formalize them. This work is the starting point to homogenize ETL processes for the reuse of EHRs, applicable to any condition and organization. In future studies, defined data operations will be implemented and validated in projects of different purposes.

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.

scholarly journals Spatial consistency and bias in avalanche forecasts – a case study in the European Alps

2018 ◽  
Vol 18 (10) ◽  
pp. 2697-2716 ◽  
Author(s):  
Frank Techel ◽  
Christoph Mitterer ◽  
Elisabetta Ceaglio ◽  
Cécile Coléou ◽  
Samuel Morin ◽  
...  
Keyword(s):  
European Alps ◽  
The Public ◽  
Spatial Consistency ◽  
The Individual ◽  
Operational Constraints ◽  
Region Size ◽  
Danger Level ◽  
Avalanche Danger

Abstract. In the European Alps, the public is provided with regional avalanche forecasts, issued by about 30 forecast centers throughout the winter, covering a spatially contiguous area. A key element in these forecasts is the communication of avalanche danger according to the five-level, ordinal European Avalanche Danger Scale (EADS). Consistency in the application of the avalanche danger levels by the individual forecast centers is essential to avoid misunderstandings or misinterpretations by users, particularly those utilizing bulletins issued by different forecast centers. As the quality of avalanche forecasts is difficult to verify, due to the categorical nature of the EADS, we investigated forecast goodness by focusing on spatial consistency and bias, exploring real forecast danger levels from four winter seasons (477 forecast days). We describe the operational constraints associated with the production and communication of the avalanche bulletins, and we propose a methodology to quantitatively explore spatial consistency and bias. We note that the forecast danger level agreed significantly less often when compared across national and forecast center boundaries (about 60 %) than within forecast center boundaries (about 90 %). Furthermore, several forecast centers showed significant systematic differences in terms of more frequently using lower (or higher) danger levels than their neighbors. Discrepancies seemed to be greatest when analyzing the proportion of forecasts with danger level 4 – high and 5 – very high. The size of the warning regions, the smallest geographically clearly specified areas underlying the forecast products, differed considerably between forecast centers. Region size also had a significant impact on all summary statistics and is a key parameter influencing the issued danger level, but it also limits the communication of spatial variations in the danger level. Operational constraints in the production and communication of avalanche forecasts and variation in the ways the EADS is interpreted locally may contribute to inconsistencies and may be potential sources for misinterpretation by forecast users. All these issues highlight the need to further harmonize the forecast production process and the way avalanche hazard is communicated to increase consistency and hence facilitate cross-border forecast interpretation by traveling users.

Supplementary material to "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):  
Data Driven ◽  
Supplementary Material ◽  
Snow Conditions ◽  
Danger Level ◽  
Avalanche Danger
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