Dr. Frank Techel

During winter, public avalanche forecasts provide crucial information for professional decision-makers as well as recreational backcountry users. While avalanche forecasting has traditionally relied exclusively on human expertise, avalanche warning services increasingly integrate data-driven models to support the forecasting process. This study assesses a random-forest classifier trained with weather data and physical snow-cover simulations as input for predicting dry-snow avalanche danger levels during the initial live testing in the winter season of 2020–2021 in Switzerland. The model achieved ∼ 70 % agreement with published danger levels, performing equally well in nowcast and forecast mode. Using model-predicted probabilities, continuous expected danger values were computed, showing a high correlation with the sub-levels as published in the Swiss forecast. The model effectively captured temporal dynamics and variations across different slope aspects and elevations but showed lower performance during periods with persistent weak layers in the snowpack. SHapley Additive exPlanations (SHAP) were employed to make the model's decision process more transparent, reducing its "black-box" nature. Beyond increasing the explainability of model predictions, the model encapsulates 20 years of forecasters' experience in aligning weather and snowpack conditions with danger levels. Therefore, the presented approach and visualization could also be employed as a training tool for new forecasters, highlighting relevant parameters and thresholds. In summary, machine-learning models like the danger-level model, often considered black-box models, can provide reliable, high-resolution, and comparably transparent “second opinions” that complement human forecasters' danger assessments.

Im vorliegenden Bericht werden die Schnee- und Lawinensituation und Lawinen mit Personen- und Sachschaden beschrieben.

Wetter, Schneedecke und Lawinengefahr
Der erste Teil (Kapitel 2, ab Seite 8) umfasst einen Ruckblick auf die Schnee- und Lawinensituation in den Schweizer Alpen und im Schweizer Jura. Dabei wird auf den Schneehohenverlauf und die wichtigsten Niederschlagsereignisse sowie die bedeutendsten Lawinenperioden mit der Entwicklung des Schneedeckenaufbaus und der Schneedeckenstabilitat eingegangen. Weitere Informationen zu einzelnen Perioden, Gefahren- und Schneehohenkarten konnen im AvaBlog, die vollstandigen Bulletins im Archiv im Internet unter www.slf.ch abgerufen werden.

Lawinen mit Personen- und Sachschaden
Der zweite Teil des Berichtes beinhaltet die Lawinen mit Personen- und Sachschaden (Kapitel 3, ab Seite 38). Nebst einer Zusammenfassung aller uns bekannten Lawinen mit Personen- und Sachschaden des Winters 2023/24 werden auch einige ausgewahlte Unfalle detailliert beschrieben (ab Seite 57).

Information on snow stratigraphy and stability is crucial in avalanche forecasting. Traditionally, this information has been obtained by manually observing and recording snow profiles, a time-consuming process. However, in recent years, the use of physical snow cover simulations driven by meteorological measurements from automated weather stations (AWS) or a numerical weather prediction (NWP) model has increased  n avalanche forecasting, allowing the simulation of future conditions. We combined  hese two approaches by initiating physical snow-cover simulations with manual snow  rofile observations. To quantitatively assess the quality of these simulations, we utilized  now profile observations conducted at regular intervals at the Weissfluhjoch test site in  Switzerland for three consecutive winter seasons from 2020/2021-2022/2023. These profiles are used in two ways: (1) to initiate AWS-driven and NWP-driven snow cover simulations and (2) as a reference for evaluating snow cover simulations initiated earlier in the season. To evaluate the quality of the simulations, we utilized an established profile alignment and similarity assessment algorithm. The comparison of modeled profiles to observed profiles showed that NWP- and AWS-driven simulations perform equally well for shorter simulation periods of about 2 to 4 weeks. Similarities decline with increasing simulation time, and the decline is about a factor of two larger for NWP-driven simulations than for AWS-driven simulations, so AWS-driven simulations outperform NWP-driven ones for longer simulation periods. The decline in similarity over simulation time further implies that AWS- and NWP-driven simulations initialized with an observed profile during the season generally have higher similarities than the reference simulations initialized at the beginning of the season. Even NWP-driven simulations initiated with a profile are better than the AWS-driven reference simulation for short simulation periods of up to 6 weeks. It shows that initiating simulations using observed snow profiles can provide forecasters with reliable snowpack information, which is particularly valuable in remote areas where no weather stations exist. Similarity assessments between two modeled profiles generally resulted in higher similarity scores and suggest that inconsistencies between the model and the real world still impact the evaluation.

Systematic mapping of avalanches using remote-sensing technologies has become a reliable and – when combined with machine-learning algorithms – also time-effective way to document avalanche activity over large areas. Making use of these technological advances, we compared the avalanche activity for three periods with widespread activity of very large and extremely large avalanches, when the highest danger level 5 (very high) was forecast for large parts of the Swiss Alps. In these three periods (in 1999, 2018, 2019), avalanches were captured with different remote-sensing instruments: in 1999, with airplane imagery, in 2018 and 2019 with multispectral satellite data. Comparing the activity of all three periods to Large Scale Hazard Indication Modelling (LSHIM) of avalanches with a 100-year return period allowed to compare how much of the potential avalanche terrain was active and to quantitatively compare activity between these periods. We found that 19% of the avalanche release areas showed activity in 1999 compared to 16% in 2018 and 8% in 2019. Considering the avalanche area delimited using the 100-year return period, 17% were active in 1999, 11% in 2018 and 6% in 2019. By comparing the three avalanche periods, we contribute to better understand the extent and return periods of very large and extremely large avalanches (size 4 and 5) and demonstrate, how avalanche mapping from remotely sensed data can make avalanche databases more complete.

Despite the increasing use of physical snow cover simulations in regional avalanche forecasting, avalanche forecasting is still an expert-based decision-making process. However, recently, it has become possible to obtain fully automated avalanche danger level predictions with satisfying accuracy by combining physically based snow cover models with machine learning approaches. These predictions are made at the location of automated weather stations close to avalanche starting zones. To bridge the gap between these local predictions and fully data- and model-driven regional avalanche danger maps, we developed and evaluated a three-stage model pipeline (RAvaFcast v1.0.0), involving the steps classification, interpolation, and aggregation. More specifically, we evaluated the impact of various terrain features on the performance of a Gaussian-process-based model for interpolation of local predictions to unobserved locations on a dense grid. Aggregating these predictions using an elevation-based strategy, we estimated the regional danger level and the corresponding elevation range for predefined warning regions, resulting in a forecast similar to the human-made public avalanche forecast in Switzerland. The best-performing model matched the human-made forecasts with a mean day accuracy of approximately 66 % for the entire forecast domain and 70 % specifically for the Alps. However, the performance depended strongly on the classifier's accuracy (i.e., a mean day accuracy of 68 %) and the density of local predictions available for the interpolation task. Despite these limitations, we believe that the proposed three-stage model pipeline has the potential to improve the interpretability of machine-made danger level predictions and has, thus, the potential to assist avalanche forecasters during forecast preparation, for instance, by being integrated in the forecast process in the form of an independent virtual forecaster.

The European Avalanche Danger Scale (EADS) is the governing document for public avalanche warning services in Europe. It has been the common ground to assess and communicate avalanche danger, with literally no changes since its introduction in 1993. However, since then, more concepts and tools have been added to standardize the production and communication of public avalanche warnings. The most prominent are the introduction of typical avalanche problems and the European Avalanche Warning Service (EAWS) Matrix, revised definitions of key terms used during the forecasting process, and a workflow tying all these elements together. However, these developments have led to differences between the terminology used in the EADS and these newer concepts. Therefore, an updated danger scale is needed, which integrates these developments, and which focuses on communicating avalanche danger to the public. Thus, language should be simple and clear while unambiguously connected to the technical definitions. Moreover, as the danger scale is the standard used by all avalanche warning services in Europe, it needs to work well in many languages. As a first step working towards a new danger scale, we analyzed data on how the governing factors of frequency of snowpack stability and avalanche size are used in connection to the avalanche danger levels in an operational forecasting context. From these data, obtained from 15 warning services during the forecasting season 2022/2023, we extracted typical descriptions for the danger levels. It showed that these align well with the EADS at the higher danger levels. However, it also indicates that forecaster tend to emphasize instability over stability at lower danger levels. We present a brief danger scale based on usage data and discuss choices and challenges that must be tackled to bring the EADS up to date.

Le rapport comprend une rétrospective de la situation nivologique et avalancheuse dans les Alpes suisses et le Jura suisse. L’évolution de la hauteur de neige et les principaux épisodes de précipitations ainsi que les périodes avalancheuses les plus importantes avec l’évolution de la structure et de la stabilité du manteau neigeux y sont abordés. De plus amples informations sur les différentes pé-riodes, les cartes de danger et de hauteur de neige peuvent être consultées sur l’AvaBlog (anciennement rapport hebdomadaire), les bulletins complets dans les archives sur Internet à l’adresse www.slf.ch.

Der Winter 2022/23 war ausserordentlich schneearm und warm. Während im Vorjahr der Winter bereits als «extrem schneearm im Süden» beschrieben wurde, war der Winter 2022/23 durch rekordtiefe Schneehöhen in der ganzen Schweiz charakterisiert. Vor allem zwischen Mitte Februar und Mitte März waren die Schneehöhen im Schweizer Alpenraum so tief wie noch nie seit Messbeginn. Die Schneearmut ergab sich aus der Kombination von unterdurchschnittlichen Niederschlagsmengen und zu warmen Temperaturen.

Snow avalanches are the deadliest natural hazards in Switzerland and cause significant economic losses annually. Therefore, avalanche detection and prediction are crucial in ensuring safety and mobility in the Swiss Alps. Operationally, avalanche forecasts are issued daily during the winter season to inform the public and local authorities about the avalanche hazard. Whereas traditional avalanche forecasting has been a human-expert decision-making process entirely, machine-learning models have become increasingly used in recent years. In this context, the Swiss avalanche warning service has been at the forefront of using these innovative data-driven tools to supplement current forecasting practices. These data-driven approaches have shown the potential to provide objective decision tools with higher spatiotemporal resolution than human-based forecasts. Nevertheless, the predictions generated by these machine learning models are opaque, usually referred to as 'black boxes', making it challenging for avalanche forecasters to interpret how variables are combined to make predictions. To address this, we employed an explanation method to interpret predictions from a machine learning model developed to predict danger levels for dry snow conditions in Switzerland. Moreover, precise avalanche data collected from an automated seismic detection system at the Vallée de la Sionne test site (Switzerland) are used to validate these danger level forecasts during the winter season 2020-2021. A novelty in future model implementations would be integrating these timely avalanche detections into forecasting models.

In recent years, the integration of physical snowpack models coupled with machine-learning techniques has become more prevalent in public avalanche forecasting. When combined with spatial interpolation methods, these approaches enable fully data- and model-driven predictions of snowpack stability or avalanche danger at any given location. This prompts the question: Are such highly detailed spatial model predictions sufficiently accurate for operational use? To explore this, we assess the performance of interpolated, model-based predictions of snowpack stability and avalanche danger, comparing them to human-generated public avalanche forecasts during the 2023/2024 winter season in Switzerland. To do so, we compare human forecasts and model predictions for locations in avalanche terrain (considering coordinates, aspect, elevation) where skiers triggered avalanches (244 events) or which were skied but where no avalanche was triggered (non-events, 3177 data points from GPX tracks). While this data reflects human behavior to some extent, we consider the event ratio as a proxy for the probability of avalanche release due to human load. We observed that with increasing model-predicted danger level or decreasing model-predicted snowpack stability, the event ratio increased. Comparing model predictions with human-made forecasts showed that the predictive performance of two operationally used models was similar to the performance of human avalanche forecasts: both predicted a strong increase in the probability of human-triggered avalanches. In summary, our results indicate that models capture regional patterns of snowpack (in)stability or avalanche danger well, and that these model chains should therefore be systematically integrated in the forecasting process.

Recent advances in automated snowpack modelling notwithstanding, public avalanche forecasting relies heavily on human estimates of avalanche conditions. Like any human judgments, these estimates differ to some extent from one forecaster to the next. We assess inter-rater reliability (IRR) — the proportion of pairwise agreement among forecasters — both for estimates of factors determining avalanche danger and for the danger level (D) itself. Comparing forecast drafts prepared independently by forecasters for the same forecast task, both in Switzerland (SWI) and in Norway (NOR), we find that IRR is lower for the factors determining avalanche danger (SWI: 0.6 − 0.66, NOR: 0.46 − 0.75) than for the danger level (SWI: 0.87, NOR: 0.76). The EAWS Matrix is a tool for estimating D that is supposed to secure IRR. It is not a part of the daily workflow in Switzerland, and one might wonder whether introducing it there would help. We find that the IRR for D would on the contrary have been much lower were the primary D chosen in the Matrix (a mere 0.54)! Where IRR is low, individual forecasts must often be incorrect, since two different forecasts cannot both be right. We calculate on the basis of the observed IRR that when estimating the factors in avalanche danger the average forecaster is incorrect in 32% (NOR: frequency) and 13% (NOR: avalanche size) of cases. Forecasting D without the Matrix, a forecaster is incorrect 7% of the time (in SWI). With the Matrix, a forecaster is incorrect about D in 13% (NOR) and 27% of cases (SWI). We conclude that while the EAWS workflow does give structure to the forecasting process, using the Matrix can be counterproductive. It can inadvertently compound variability in parameter inputs. The IRR of Swiss D forecasts leaves some room for improvement, but introducing the Matrix into the Swiss workflow cannot be expected to increase it while the IRR of Swiss factor forecasts remains as low as it currently is. We recommend that avalanche warning services assess the IRR of forecasters and consider the effects of low input IRR on the IRR of their Matrix outputs. Moreover, they should consider the effect of unreliability on forecast correctness. Where IRR is low, one intervention to boost forecast correctness is training forecasters with a view to increasing IRR. Another is to aggregate several individual forecasts for the same task, as is standard practice in Switzerland.

Predicting natural avalanche activity and verifying avalanche forecasts have long been major challenges. However, the advent of new technologies, such as radar-based avalanche detection systems and prediction models, are beginning to fill these critical gaps. In this study, we describe the integration of these technologies into our operational workflow, assess their perceived value by forecasters, and provide a quantitative comparison between model predictions and radar detections in southern Valais. A recently developed natural avalanche model (NAM), which predicts natural avalanche activity, was used operationally by the Swiss Avalanche Warning Service in the 2023/24 winter season. The model was displayed next to avalanche observations reported by the observer network and avalanches detected by automated radar systems. This visualization was crucial in helping forecasters develop a better appreciation of the new model. The approach also provided immediate feedback when obvious deviations from predicted and actual conditions became apparent. In this work, after one winter of operational use, we quantitatively evaluated model predictions by comparing these with avalanche activity data from two radar avalanche detection systems in Southern Valais. The analysis confirmed that the natural avalanche model was related to regional avalanche activity, but also highlighted areas for improvement. In particular, after reaching a peak in predicted probability, the model often maintained a high probability while measured avalanche activity had ceased. Our findings suggest that incorporating a trend of predicted probability could improve the model’s accuracy. For further model development, we recommend integrating detection data from multiple sites and over multiple winters. In addition, we recommend the development of automated methods to classify detected avalanches as dry or wet and to link these events to explosives logs. This would greatly enhance the real-time utility of detection data for operational avalanche forecasting services and support ongoing analysis and model refinement.

Machine learning (ML) models are revolutionizing avalanche forecasting, providing increasingly reliable predictions. At present, however, such model output can hardly be integrated into the traditional operational forecast workflow. To close this gap, the Swiss Avalanche Warning Service has developed the Snow Avalanche Forecast Editor (SAFE), allowing a flexible integration of both human forecasts and model predictions. In SAFE, each hazard source (e.g., an avalanche problem) is assessed and described as a separate layer. This allows an entirely independent characterization of the severity of avalanche problems including their spatial extent, in line with the European Avalanche Warning Services standards. Once the assessment has been completed, all existing layers are automatically combined and published as regional avalanche forecasts. Forecast accuracy can be increased by minimizing noise. Decision theory advises to calculate an expected value from multiple independent assessments. For this reason, each of the two or three forecasters on duty assesses the regional avalanche danger in the forecast domain independently of each other. Another estimate comes from the danger-level model. To combine these, forecasters and models must assess the same hazard source using the same target variable. SAFE merges these independent assessments to expected values of the danger level including sub-levels, and the particularly affected elevations and aspects. This includes clustering into a limited number of danger regions, characterized by similar expected avalanche conditions. Following the discussion between forecasters, clusters can be refined. Lastly, the avalanche danger of each cluster is manually described. SAFE is conceived as open-source platform and a first version has been operational within the Swiss avalanche warning since winter 2023/24.

L’hiver 2023/24 a été exceptionnellement arrosé et très doux. Contrairement à l’année précédente, qui avait été relativement sèche, avec donc peu de neige, les précipitations ont donc été importantes. En raison des températures supérieures à la moyenne, les hauteurs de neige en dessous de 1400 m d’altitude ont été toutefois nettement inférieures à la normale. L’hiver 2023/24 n’a donc pas été peu enneigé en raison de faibles précipitations, mais parce qu’il a plu fréquemment aux basses et moyennes altitudes. Audessus de 2200 m, en revanche, les hauteurs de neige ont été clairement supérieures aux moyennes. Il y avait donc de grandes différences en fonction de l’altitude : peu de neige en bas, beaucoup de neige en haut. Audessus de 2200 mètres, l’hiver (de novembre à avril) a en effet été l’un des plus enneigés, par exemple, 5e rang au Weissfluhjoch dans la série de mesures effectuées depuis plus de 80 ans. Le semestre d’hiver 2023/24 pourrait donc constituer une référence pour les hivers futurs. En effet, comme chacun sait, les modèles climatiques prévoient des hivers légèrement plus arrosés avec des températures nettement trop chaudes. [...]

Our paper focuses on four popular snowpack tests, the Rutschblock Test (RB), Compression Test (CT), Extended Column Test (ECT), and Propagation Saw Test (PST), reviewing past and current stability test research in the context of recent advances in our understanding of dry slab avalanche release. We found that: 1) Not all tests provide the same information with regards to the critical stages of avalanche release, namely snowpack layering, failure initiation, and crack propagation, 2) Stability test research is challenging, leading to some discrepancies between studies, 3) Each stability test has its own advantages, as well as its own limitations 4) Test accuracy should not be the only selection criteria, but instead users need to pick the appropriate test to answer the questions they have about their snowpack especially with regards to failure initiation and crack propagation. Finally, conducting stability tests is valuable beyond the actual test results. Tests provide an opportunity for slowing down our thinking, focusing on the snowpack, and increasing group communication, all of which are important for minimizing common decision-making biases.

Consistent estimates of avalanche size are crucial for communicating not only among avalanche practitioners but also between avalanche forecasters and the public, for instance in public avalanche forecasts. Moreover, applications such as risk management and numerical avalanche simulations rely on accurately mapped outlines of past avalanche events. Since there is not a widely applicable and objective way to measure avalanche size or to determine the outlines of an avalanche, we need to rely on human estimations. Therefore, knowing about the reliability of avalanche size estimates and avalanche outlines is essential as errors will impact applications relying on this kind of data. In the first of three user studies, we investigate the reliability in avalanche size estimates by comparing estimates for 10 avalanches made by 170 avalanche professionals working in Europe or North America. In the other two studies, both completed as pilot studies, we explore reliability in the mappings of six avalanches from oblique photographs from 10 participants and the mappings of avalanches visible on 2.9 km² of remotely sensed imagery in four different spatial resolutions from 5 participants. We observed an average agreement of 66 % in the most frequently given avalanche size, while agreement with the avalanche size considered “correct” was 74 %. Moreover, European avalanche practitioners rated avalanches significantly larger for 8 out of 10 avalanches, compared to North Americans. Assuming that participants are equally competent in the estimation of avalanche size, we calculated a score describing the factor required to obtain the observed agreement rate between any two size estimates. This factor was 0.72 in our dataset. It can be regarded as the certainty related to a size estimate by an individual and thus provides an indication of the reliability of a label. For the outlines mapped from oblique photographs, we noted a mean overlapping proportion of 52 % for any two avalanche mappings and 60 % compared to a reference mapping. The outlines mapped from remotely sensed imagery had a mean overlapping proportion of 46 % (image resolution of 2 m) to 68 % (25 cm) between any two mappings and 64 % (2 m) to 80 % (25 cm) when compared to the reference. The presented findings demonstrate that the reliability of size estimates and of mapped avalanche outlines is limited. As these data are often used as reference data or even ground truth to validate further applications, the identified limitations and uncertainties may influence results and should be considered.

Wet-snow avalanches are triggered by the infiltration of liquid water which weakens the snowpack. Wet-snow avalanches are among the most destructive avalanches, yet their release mechanism is not sufficiently understood for a process-based prediction model. Therefore, we followed a data-driven approach and developed a random forest model, depending on slope aspect, to predict the local wet-snow avalanche activity at the locations of 124 automated weather stations distributed throughout the Swiss Alps. The input variables were the snow and weather data recorded by the stations over the past 20 years. The target variable was based on manual observations over the same 20-year period. To filter out erroneous reports, we defined the days with wet-snow avalanches in a stringent manner, selecting only the most extreme active or inactive days, which reduced the size of the dataset but increased the reliability of the target variable. The model was trained with weather variables and variables computed from simulated snow stratigraphy in 38 slopes facing the 4 cardinal directions. While model development and validation were done in nowcast mode, we also studied model performance in 24-hour forecast mode by using input variables computed from a numerical weather prediction (NWP) model. Overall, the performance was good in both nowcast and forecast mode (f1-score around 0.8). To assess model performance beyond the stringent definition of wet-snow avalanche days, we compared model predictions to wet-snow avalanche activity over the entire Swiss Alps, based on the raw data over 8 winters. We obtained a Spearman correlation coefficient of 0.71. Hence, our model represents a step toward the application of support tools in operational wet-snow avalanche forecasting.

In public avalanche forecasts, avalanche danger is commonly summarized using one of five ordinal danger levels. This strong simplification of a complex phenomenon inevitably leads to a loss of information. To compensate for this information loss and to ease interpretation of the forecast, the Swiss avalanche warning service introduced sublevels, that indicate whether the forecast danger is in the lower (-), middle (=) or higher (+) range of the danger level. After a 6-year test period, during which it showed that forecasters can reliably assign sublevels to a danger level, the sublevels were published for the first time in the public avalanche forecast in the winter season of 2022/2023. To evaluate the general understanding and the usefulness of the sublevels, and to understand how they influence people’s backcountry behavior, we performed a user survey towards the end of the season. The survey with 3403 participants showed that participants perceived the sublevels as generally useful (85%) and that they understood them in the intended manner (94%). Sublevels also had a positive influence on the planning of backcountry tours. Faced with a forecast sublevel (+), almost half of the participants claimed they would change their plans to a less exposed tour than they had before when the same danger level was given but without sublevel information. This self-stated influence of the sublevels on the choice of a backcountry tour was especially important for participants with little or no avalanche training, showing that sublevels seem most useful for less experienced forecast users, who may find it difficult to extract similar information from the forecast text description.

Predicting the timing and size of natural snow avalanches is crucial for local and regional decision makers but remains one of the major challenges in avalanche forecasting. So far, forecasts are generally made by human experts interpreting a variety of data and drawing on their knowledge and experience. Using avalanche data from the Swiss Alps and one-dimensional physics-based snowpack simulations for virtual slopes, we developed a model predicting the probability of dry-snow avalanches occurring in the region surrounding automated weather stations based on the output of a recently developed instability model. This new avalanche day predictor was compared with benchmark models related to the amount of new snow. Evaluation on an independent data set demonstrated the importance of snow stratigraphy for natural avalanche release, as the avalanche day predictor outperformed the benchmark model based on the 3 d sum of new snow height (F1 scores: 0.71 and 0.65, respectively). The averaged predictions of both models resulted in the best performance (F1 score: 0.75). In a second step, we derived functions describing the probability for certain avalanche size classes. Using the 24 h new snow height as proxy of avalanche failure depth yielded the best estimator of typical (median) observed avalanche size, while the depth of the deepest weak layer, detected using the instability model, provided the better indicator regarding the largest observed avalanche size. Validation of the avalanche size estimator on an independent data set of avalanche observations confirmed these findings. Furthermore, comparing the predictions of the avalanche day predictors and avalanche size estimators with a 21-year data set of re-analysed regional avalanche danger levels showed increasing probabilities for natural avalanches and increasing avalanche size with increasing danger level. We conclude that these models may be valuable tools to support forecasting the occurrence of natural dry-snow avalanches.

With the motivation to increase consistency between forecasters and warning services when assessing regional avalanche danger, a working group of the European Avalanche Warning Services (EAWS) was assigned to define the factors determining regional avalanche danger, and to develop a workflow and a look-up table - referred to as EAWS Matrix - assisting forecasters with the consistent assignment of a danger level. The workflow starts by an evaluation of avalanche problems relevant for the given region. Each avalanche problem is assessed according to three factors determining regional avalanche danger: i) snowpack stability, ii) the frequency of snowpack stability, and iii) avalanche size. Combining the three factors, each avalanche problem is assigned a danger level using the EAWS Matrix. The highest resulting level is issued for the region. Workflow, definitions, and the look-up table were accepted at the EAWS general assembly in 2022. We present workflow, definitions, and the corresponding EAWS Matrix, and the process and considerations that led to this proposal.