Avalanche Bulletins Research Articles

Применение социологического опроса для оценки качества прогнозов лавинной опасности в Иле Алатау

The results of assessing the quality of avalanche danger forecasts based on data from a sociological survey are considered. The avalanche danger forecast was compiled using a method developed at the Institute of Geography and Water Security, using artificial intelligence. The level of avalanche danger was assessed on an international 5-point scale. The avalanche danger forecast was distributed on social networks in the form of an avalanche bulletin. To assess the quality of avalanche forecasts, a sociological survey was conducted among users. The survey involved 214 people - tourists, climbers and skiers. It turned out that 93% of respondents regularly watch avalanche danger forecasts, and 71% of respondents are ready to refuse to go to the mountains if the level of avalanche danger is high. The majority of consumers (82%) positively assess the work of the avalanche service in Kazakhstan.

Experience of release of an avalanche bulletin for the mountain region of Ile Alatau

Experience of release of an avalanche bulletin for the mountain region of Ile Alatau

Snowpack and avalanche characterization over the 2021–2022 winter season in Sisimiut, West Greenland

In Greenland, snow avalanches have been recognized as a significant hazard and a risk to communities for decades. However, prior to this study, no formal avalanche database nor avalanche forecasting service existed in Greenland. This study was conducted over the 2021–2022 snow season to advance snow science research in Greenland and provide the town of Sisimiut with an avalanche bulletin service based on snowpack stability, weather conditions, and avalanche activity monitoring data within the Sisimiut backcountry. Snowpack stratigraphies were assessed and stability tests were performed periodically using standardized methods, and the results were linked with daily weather monitoring and avalanche event characterization. The observed avalanche activity was dominated by slab avalanches, accounting for 96% of the registered events. Instabilities were mainly driven by specific temperature patterns, strong winds, and rain episodes. During cold and calm periods, slow kinetic growth of snow crystals in low-density layers was observed. Abrupt thermal increases were recurrent, resulting in softening of superficial slabs with consequent reactivation of buried weak layers, thus reducing snowpack stability. On the other hand, prolonged warmer temperatures lowered the thermal gradient within the snowpack, leading to rounding and bonding of weak layer grains, thus recovering the snowpack stability. As observed on three occasions, rain events caused the formation of persistent weak layers and should be considered a warning for future snowpack instability. As a result of this study, we retroactively linked local weather patterns with snowpack instability and provided the first periodical avalanche bulletin service for Sisimiut.

Application of Artificial Intelligence in the Assessment and Forecast of Avalanche Danger in the Ile Alatau Ridge

The assessment and forecast of avalanche danger are very important means of preventing avalanche fatalities, especially in recreational areas. The use of artificial intelligence methods for these purposes significantly increases the accuracy of avalanche forecasts. The purpose of this re-search was to improve the methods for assessing and forecasting avalanche danger in the Ile Alatau Ridge. To create a training sample, the data from three meteorological and two avalanche stations for the period from 2002 to 2022 were used. The following predictors were chosen: air temperature, snow cover depth, precipitation, and snowpack stability index. The subject of the assessment and forecasts was the level of avalanche danger, assessed on a five-point scale. The program Statistica StatSoft was used as a neurosimulator. When forecasting avalanche danger, the predictive values of air temperature and precipitation, obtained from numerical weather forecast models, were used. The model correctly assessed the current level of avalanche danger in 90% of cases. The forecast of avalanche danger was justified in 80% of cases. The artificial intelligence program helped the avalanche forecaster to improve the forecast quality. This method is currently being used for compiling an avalanche bulletin for two river basins in the Ile Alatau.

Travel and terrain advice statements in public avalanche bulletins: a quantitative analysis of who uses this information, what makes it useful, and how it can be improved for users

Abstract. Recreationists are responsible for developing their own risk management plans for travelling in avalanche terrain. To help recreationists mitigate their exposure to avalanche hazard, many avalanche warning services include explicit travel and terrain advice (TTA) statements in their daily avalanche bulletins where forecasters offer guidance about what specific terrain to avoid and what to favour under the existing conditions. However, the use and effectiveness of this advice has never been tested to ensure it meets the needs of recreationists developing their risk management approach for backcountry winter travel. We conducted an online survey in Canada and the United States to determine which user groups are paying attention to the TTA in avalanche bulletins, what makes these statements useful, and if modifications to the phrasing of the statements would improve their usefulness for users. Our analysis reveals that the core audience of the TTA is users with introductory-level avalanche awareness training who integrate slope-scale terrain considerations into their avalanche safety decisions. Using a series of proportional-odds ordinal mixed-effect models, we show that reducing the jargon used in the advice helped users with no or only introductory-level avalanche awareness training understand the advice significantly better and adding an additional explanation made the advice more useful for them. These results provide avalanche warning services with critical perspectives and recommendations for improving their TTA so that they can better support recreationists who are at earlier stages of developing their avalanche risk management approach and therefore need the support the most.

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

Abstract. Even today, the assessment of avalanche danger is by and large a subjective yet data-based decision-making process. Human experts analyse 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 assess 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 official Swiss avalanche bulletin. To reduce the uncertainty resulting from using the forecast danger level as target variable, we trained a second classifier (RF 2) that relies 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 models, i.e. the percentage of correct danger level predictions, ranged between 74 % and 76 % for RF 1 and between 72 % and 78 % for RF 2. We assessed the accuracy of forecasts with nowcast assessments of avalanche danger by well-trained observers. The performance of both models was similar to the agreement rate between forecast and nowcast assessments of the current experience-based Swiss avalanche forecasts (which is estimated to be 76 %). The models performed consistently well throughout the Swiss Alps, thus in different climatic regions, albeit with some regional differences. Our results suggest that the models may well have potential to become a valuable supplementary decision support tool for avalanche forecasters when assessing avalanche hazard.

Exploring the avalanche bulletin as an avenue for continuing education by including learning interventions

Snow avalanches pose a serious threat to people recreating in the mountainous backcountry during the wintertime. To help recreationists manage their risk from avalanches, local avalanche warning services publish daily bulletins to inform the public about the existing hazard conditions. While the correct application of this information is crucial for avoiding potentially deadly accidents, recreationists’ ability to develop their skills through practical experience alone is limited due to the wicked nature of the backcountry learning environment where feedback is not always reliable. The present study explores the idea of improving recreationists’ ability to apply the hazard information to terrain by adding interactive exercises with feedback directly into the daily avalanche bulletins. To examine this idea, we conducted an online survey that included a route ranking exercise. Our analysis dataset included responses from 2278 backcountry recreationists with a variety of backgrounds and avalanche safety training levels. Using a series of generalized linear mixed effects models and conditional inference tree analyses, our results highlight that including interactive self-assessment exercises in avalanche bulletins has potential for enhancing their effectiveness and education value, especially for individuals who might not have the skills to properly understand the hazard information well enough to make informed decisions about personal risk but are willing to learn. Avalanche warning services should consider integrating application exercises with feedback into their bulletins to give users opportunities to assess their understanding and practice their information processing skills. Integrating such exercises directly into the bulletin takes advantage of recreationists' frequent interaction with the product and provides them with just-in-time education when they use the bulletin for personal trip planning. Enhancing bulletins this way will turn them from pure condition reports into a critical component of the overall avalanche awareness education system. Recreationists’ performance in these exercises can provide warning services with valuable insights into skill levels of their users. • Managing avalanche risk requires applying hazard information to terrain. • Bulletin information processing skills vary among recreationists. • Well-designed exercises with feedback can improve information application skills. • Recreationists welcome safe learning opportunities.

Mapping the avalanche risk: from survey to cartographic production. The avalanche bulletin of the Meteomont Service of the Alpine Troops Command

Abstract. During the last decades, the process of explaining life-threatening natural hazards to the public has become a major public issue from the point of view of effective prevention policies. The avalanche risk and the communication methods aimed at its forecasting and prevention constitute the focus of this paper. Among the strategies for an effective communication of environmental risks, cartography plays a pivotal role. It has proved to be essential not only for communication purposes, but also for the planning of prompt and efficient preventive interventions; in so doing, it contributes to the reduction of avalanche-caused damages and deaths. The paper investigates prevention and forecasting activities of the Meteomont Service of the Alpine Troops Command (COMTA) of Bolzano (capital city of the province of South Tyrol - North Italy), resulting in the daily publication of avalanche bulletins (Bollettini valanghe), which also include hazard maps. Specifically, the phases that contribute to the production of the avalanche bulletin and the embedded avalanche risk maps will be firstly examined; secondly, such maps will be analysed in order to assess their communicative potential for the purpose of a correct interpretation aimed at the effective prevention of snow-related risks in mountain areas. Possible improvement will be proposed on the basis of the experience of several avalanche warning services worldwide.

Impact of information presentation on interpretability of spatial hazard information: lessons from a study in avalanche safety

Abstract. Avalanche warning services publish avalanche condition reports, often called avalanche bulletins, to help backcountry recreationists make informed risk management choices regarding when and where to travel in avalanche terrain. To be successful, these bulletins must be interpreted and applied by users prior to entering avalanche terrain. However, few avalanche bulletin elements have been empirically tested for their efficacy in communicating hazard information. The objective of this study is to explicitly test the effectiveness of three different graphics representing the aspect and elevation of avalanche problems on users' ability to apply the information. To address this question, we conducted an online survey in the spring of 2020 that presented participants with one of three graphic renderings of avalanche problem information and asked them to rank a series of route options in order of their exposure to the described hazard. After the route ranking tasks, users were presented with all three graphics and asked to rate how effective they thought the graphics were. Our analysis data set included responses from 3056 backcountry recreationists with a variety of backgrounds and avalanche safety training levels. Using a series of generalized linear mixed effects models, our analysis shows that a graphic format that combines the aspect and elevation information for each avalanche problem is the most effective graphic for helping users understand the avalanche hazard conditions because it resulted in higher success in picking the correct exposure ranking, faster completion times, and was rated by users to be the most effective. These results are consistent with existing research on the impact of graphics on cognitive load and can be applied by avalanche warning services to improve the communication of avalanche hazard to readers of their avalanche bulletins.

Where the rubber of the RISP model meets the road: Contextualizing risk information seeking and processing with an avalanche bulletin user typology

In risk communication research, the Risk Information Seeking and Processing (RISP) model has established itself as a valuable tool for explaining differences in how people reason with risk information. However, the generic nature of its dependent variables, information seeking (routine or nonroutine) and processing (heuristic or systematic), seems to limit its capacity to produce tangible directives for improving risk communication product design. Studying avalanche risk communication provides an opportunity to conduct empirical research to better understand how information seeking and processing behaviors explicitly manifest themselves in a specific context and with respect to a particular product. The primary tool that avalanche warning services use to inform winter backcountry recreationists about avalanche hazard is the public avalanche bulletin, which summarizes conditions for a mountain region over a given time. Addressing an industry need to evaluate avalanche bulletin effectiveness for the range of backcountry recreationists in Canada, we conducted 46 semi-structured interviews with an inclusive sample to shed light onto how recreationists incorporate bulletin information into backcountry travel decisions. We combined applied thematic analysis with correspondence analysis to detect and classify patterns in recreationists' bulletin use into an Avalanche Bulletin User Typology that explicitly describes users’ information seeking and processing behaviors. The identified patterns reveal a distinct hierarchy in the complexity of information reasoning, which parallels the Structure of Observed Learning Outcomes (SOLO) taxonomy, a well-established education theory. The results illustrate new opportunities for designing risk communication products with evidence-based and context-specific guidance to reach and resonate with audiences more effectively.

Innovative technologies for assessing and forecasting the avalanche danger level in the Ile Alatau mountains

Annotation. Avalanche hazard forecasts are of great importance for ensuring the safety of residents and tourists in mountainous areas. All snow avalanches’ forecasting methods used in the work of the snow avalanche service in Kazakhstan were developed in the 80s of the XX century. They need a significant modernization. The Natural Hazards Laboratory of the Institute of Geography and Water Safety studies the advanced scientific methods for assessing and forecasting avalanche danger. In the winter period from December 2020 to April 2021, an experiment was carried out to assess the avalanche danger level in the vicinity of Almaty city and to issue a weekly avalanche bulletin. The main objective of the project is to adapt the experience of the Swiss Institute for Snow and Avalanches (SLF) for the conditions of Kazakhstan. In the course of the work, the meteorological information received from the meteorological stations of international exchange was analyzed. The avalanche danger level was assessed on an international five-point scale. The expert assessment was carried out taking into account the recommendations of the SLF Institute. In order to facilitate the forecaster's work, an automatic expert system using neural network technologies was developed. The neural network was trained on the long-term data on avalanche conditions in the Kishi Almaty River basin. The European center weather forecasts were used to increase the lead time of avalanche warnings. The results of avalanche danger level’s assessing were published weekly on the website of the Institute of Geography and Water Safety, as well as on the Telegram social network. This method is recommended to be used in the operational work of the Kazakhstan Hydrometeorological Service and the Kazakhstan Mudflow Protection Service.

Using avalanche problems to examine the effect of large-scale atmosphere–ocean oscillations on avalanche hazard in western Canada

Abstract. Numerous large-scale atmosphere–ocean oscillations including the El Niño–Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), the Pacific North American Teleconnection Pattern (PNA), and the Arctic Oscillation (AO) are known to substantially affect winter weather patterns in western Canada. Several studies have examined the effect of these oscillations on avalanche hazard using long-term avalanche activity records from highway avalanche safety programmes. We present a new approach for gaining additional insight into these relationships that uses avalanche problem information published in public avalanche bulletins during the winters of 2010 to 2019. For each avalanche problem type, we calculate seasonal prevalence values for each forecast area, elevation band, and season, which are then included in a series of beta mixed-effects regression models to explore both the overall and regional effects of the Pacific-centered oscillations (POs; including ENSO, PDO, and PNA) and AO on the nature of avalanche hazard in the study area. We find significant negative effects of PO on the prevalence of storm slab avalanche problems, wind slab avalanche problems, and dry loose avalanche problems, which agree reasonably well with the known impacts of PO on winter weather in western Canada. The analysis also reveals a positive relationship between AO and the prevalence of deep persistent slab avalanche problems, particularly in the Rocky Mountains. In addition, we find several smaller-scale patterns that highlight that the avalanche hazard response to these oscillations varies regionally. Even though our study period is short, our study shows that the forecaster judgement included in avalanche problem assessments can add considerable value for these types of analyses. Since the predictability of the most important atmosphere–ocean oscillations is continuously improving, a better understanding of their effect on avalanche hazard can contribute to the development of informative seasonal avalanche forecasts in a relatively simple way.

Snow avalanche detection and mapping in multitemporal and multiorbital radar images from TerraSAR-X and Sentinel-1

Abstract. Snow avalanches can endanger people and infrastructure, especially in densely populated mountainous regions. In Switzerland, the public is informed by an avalanche bulletin issued twice a day during winter which is based on weather information and snow and avalanche reports from a network of observers. During bad weather, however, information about avalanches that have occurred can be scarce or even be missing completely. To assess the potential of weather-independent radar satellites, we compared manual and automatic change detection avalanche mapping results from high-resolution TerraSAR-X (TSX) stripmap images and medium-resolution Sentinel-1 (S1) interferometric wide-swath images for a study site in central Switzerland. The TSX results were also compared to available mapping results from high-resolution SPOT-6 optical satellite images. We found that avalanche outlines from TSX and S1 agree well with each other. Cutoff thresholds of mapped avalanche areas were found with 500 m2 for TSX and 2000 m2 for S1. S1 provides a much higher spatial and temporal coverage and allows for mapping of the entire Alps at least every 6 d with freely available acquisitions. With costly SPOT-6 images the Alps can even be covered in a single day at meter resolution, at least for clear-sky conditions. For the SPOT-6 and TSX mapping results, we found a fair agreement, but the temporal information from radar change detection allows for a better separation of overlapping avalanches. Still, the total mapped avalanche area differed by at least a factor of 3 because with radar mainly the avalanche deposition zone was detected, whereas the release zone was very visible already in SPOT-6 data. With automatic avalanche mapping we detected around 70 % of manually mapped new avalanches, at least when the number of old avalanches is low. To further improve the radar mapping capabilities, we combined S1 images from multiple orbits and polarizations and obtained a notable enhancement of resolution and speckle reduction such that the obtained mapping results are almost comparable to the single-orbit TSX change detection results. In a multiorbital S1 mosaic covering all of Switzerland, we manually counted 7361 new avalanches which occurred during an extreme avalanche period around 4 January 2018.

Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps

Abstract. Accurate and timely information on avalanche occurrence is key for avalanche warning, crisis management and avalanche documentation. Today such information is mainly available at isolated locations provided by observers in the field. The achieved reliability, considering accuracy, completeness and reliability of the reported avalanche events, is limited. In this study we present the spatially continuous mapping of a large avalanche period in January 2018 covering the majority of the Swiss Alps (12 500 km2). We tested different satellite sensors available for rapid mapping during the first avalanche period. Based on these experiences, we tasked SPOT6 and SPOT7 for data acquisition to cover the second, much larger avalanche period. We manually mapped the outlines of 18 737 individual avalanche events, applying image enhancement techniques to analyze regions in the shade as well as in brightly illuminated ones. The resulting dataset of mapped avalanche outlines, having unique completeness and reliability, is evaluated to produce maps of avalanche occurrence and avalanche size. We validated the mapping of the avalanche outlines using photographs acquired from helicopters just after the avalanche period. This study demonstrates the applicability of optical, very high spatial resolution satellite data to map an exceptional avalanche period with very high completeness, accuracy and reliability over a large region. The generated avalanche data are of great value in validating avalanche bulletins, in completing existing avalanche databases and for research applications by enabling meaningful statistics on important avalanche parameters.

ALBINA The White Goddess – Mapping and Communicating Avalanche Risk in the European Alps

Abstract. With the increasing importance of communication in the context of risk management and disaster prevention in mountainous environments, the demand for adequate communication channels and cartographic representations is constantly rising. In particular, the presentation of a broad spectrum of geospatial topics such as avalanche awareness requires innovative cartographic methods and approaches that go beyond standard cartographic depiction procedures.ALBINA is such a project that addresses this proposition. It embraces risk management with cartographic communication methods and stands for “The White Goddess” an allusion to snow avalanches. This cooperation project has the goal to publish a joint, multilingual avalanche bulletin in the entire European region of Tyrol, South Tyrol and Trentino. The aim is to inform the public daily about the avalanche situation as well as to communicate avalanche related information in an efficient and profound way. An online portal is currently being developed as part of an Interreg V-A Italy-Austria project in collaboration between the University of Vienna, the EVTZ Europaregion, the Austrian Avalanche Warning Service of Tyrol as well as the Italian Avalanche Warning Services of South Tyrol and Trentino. The developed communication structures promote and facilitate the exchange of spatial-temporal information between experts of neighboring regions as well as the public in a multi-lingual environment. The framework is supported by a software system that handles and visualizes meteorological data, observations, snow profiles and avalanche events of the entire region with a strong focus on cartographic communication. It furthermore offers the possibility to enter and manipulate the avalanche bulletin in a standardized way in order to optimize the exchange of information between the avalanche experts on duty.In order to foster the efforts in avalanche awareness and communication three conceptual cornerstones have been identified according to international avalanche warning standards: (1) avalanche danger assessment and forecasting production, (2) timing and validity of publication and (3) effective geo-communication. Based on this alignment the international ALBINA project was launched to showcase the ability and strength of such an approach. This presentation will primarily focus on effective geo-communication clarifying the general framework as well as the communication structures and workflow within the overall system. Thereby explaining the methods and interaction between the available real-time data, the technical infrastructure, the human resources as well as the geo-communicational aspects of the system. Thereafter the individual cornerstones of the system will be discussed. These consist of various services dealing with the input and administration of avalanche relevant information, geodata processing and provision, map production and dissemination, meteorological map and diagram manipulation and creation as well as the design and conception of the frontend web-interface. Finally, the current state of the system will be presented exemplifying the geo-communicational procedures and methods.

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

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.

Characterizing the nature and variability of avalanche hazard in western Canada

Abstract. The snow and avalanche climate types maritime, continental and transitional are well established and have been used extensively to characterize the general nature of avalanche hazard at a location, study inter-seasonal and large-scale spatial variabilities and provide context for the design of avalanche safety operations. While researchers and practitioners have an experience-based understanding of the avalanche hazard associated with the three climate types, no studies have described the hazard character of an avalanche climate in detail. Since the 2009/2010 winter, the consistent use of Statham et al. (2017) conceptual model of avalanche hazard in public avalanche bulletins in Canada has created a new quantitative record of avalanche hazard that offers novel opportunities for addressing this knowledge gap. We identified typical daily avalanche hazard situations using self-organizing maps (SOMs) and then calculated seasonal prevalence values of these situations. This approach produces a concise characterization that is conducive to statistical analyses, but still provides a comprehensive picture that is informative for avalanche risk management due to its link to avalanche problem types. Hazard situation prevalence values for individual seasons, elevations bands and forecast regions provide unprecedented insight into the inter-seasonal and spatial variability of avalanche hazard in western Canada.

A complete snow avalanche activity record from a Norwegian forecasting region using Sentinel-1 satellite-radar data

Collecting a complete dataset of snow avalanche activity in a given avalanche forecasting region throughout an entire winter is a seemingly simple task that is very difficult to accomplish. Traditional field-based methods are too time and cost consuming and limited to good visibility and accessible terrain. Different remote sensing methods have been proposed to assist in monitoring of snow avalanche activity, with satellite-borne radar remote sensing allowing for all-weather and all-light conditions monitoring. Here we present a complete, two-year snow avalanche activity record from a Norwegian forecasting region using Sentinel-1A radar satellite imagery. We used a change detection method to manually identify avalanche debris based on localized backscatter increase in avalanche runout zones and assess these detections using multi-temporal and multi-sensor datasets. We then used the complete dataset of snow avalanche activity to exemplify different applications from validating issued avalanche bulletins, to generating avalanche path maps, and creating a snow climate classification. Comparing manual identification in Sentinel-1A images and a very high resolution Radarsat-2 image suggest overall underestimation of avalanche activity, also when considering the non-detectability of small avalanche debris. We foresee that with the further increase in satellite-radar data availability and the improvement of automatized snow avalanche detection, the presented methodologies will be able to assist in operational avalanche forecasting worldwide in the near future.

Fully automatic multi-language translation with a catalogue of phrases: successful employment for the Swiss avalanche bulletin

The Swiss avalanche bulletin is produced twice a day in four languages. Due to the lack of time available for manual translation, a fully automated translation system is employed, based on a catalogue of predefined phrases and predetermined rules of how these phrases can be combined to produce sentences. Because this catalogue of phrases is limited to a small sublanguage, the system is able to automatically translate such sentences from German into the target languages French, Italian and English without subsequent proofreading or correction. Having been operational for two winter seasons, we assess here the quality of the produced texts based on two different surveys where participants rated texts from real avalanche bulletins from both origins, the catalogue of phrases versus manually written and translated texts. With a mean recognition rate of 55 %, users can hardly distinguish between the two types of texts, and give very similar ratings with respect to their language quality. Overall, the output from the catalogue system can be considered virtually equivalent to a text written by avalanche forecasters and then manually translated by professional translators. Furthermore, forecasters declared that all relevant situations were captured by the system with sufficient accuracy. Forecaster’s working load did not change with the introduction of the catalogue: the extra time to find matching sentences is compensated by the fact that they no longer need to double-check manually translated texts. The reduction of daily translation costs is expected to offset the initial development costs within a few years.

Spatio-temporal avalanche forecasting with Support Vector Machines

Abstract. This paper explores the use of the Support Vector Machine (SVM) as a data exploration tool and a predictive engine for spatio-temporal forecasting of snow avalanches. Based on the historical observations of avalanche activity, meteorological conditions and snowpack observations in the field, an SVM is used to build a data-driven spatio-temporal forecast for the local mountain region. It incorporates the outputs of simple physics-based and statistical approaches used to interpolate meteorological and snowpack-related data over a digital elevation model of the region. The interpretation of the produced forecast is discussed, and the quality of the model is validated using observations and avalanche bulletins of the recent years. The insight into the model behaviour is presented to highlight the interpretability of the model, its abilities to produce reliable forecasts for individual avalanche paths and sensitivity to input data. Estimates of prediction uncertainty are obtained with ensemble forecasting. The case study was carried out using data from the avalanche forecasting service in the Locaber region of Scotland, where avalanches are forecast on a daily basis during the winter months.