Avalanche danger level characteristics from field observations of snow instability

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

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Avalanche danger level characteristics from field observations of snow instability

10.5194/tc-2020-350 ◽

2020 ◽

Author(s):

Jürg Schweizer ◽

Christoph Mitterer ◽

Benjamin Reuter ◽

Frank Techel

Keyword(s):

Spatial Frequency ◽

Surface Quality ◽

Snow Depth ◽

Field Observations ◽

Snow Surface ◽

Stability Class ◽

Data Set ◽

Drifting Snow ◽

Danger Level ◽

Avalanche Danger

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

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

The Cryosphere ◽

10.5194/tc-14-3503-2020 ◽

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.

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On the relation between avalanche occurrence and avalanche danger level

10.5194/tc-2019-218 ◽

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.

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

10.5194/nhess-2021-341 ◽

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.

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

10.5194/tc-2020-42 ◽

2020 ◽

Cited By ~ 1

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.

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On the relation between avalanche occurrence and avalanche danger level

The Cryosphere ◽

10.5194/tc-14-737-2020 ◽

2020 ◽

Vol 14 (2) ◽

pp. 737-750 ◽

Cited By ~ 1

Author(s):

Jürg Schweizer ◽

Christoph Mitterer ◽

Frank Techel ◽

Andreas Stoffel ◽

Benjamin Reuter

Keyword(s):

Large Data ◽

Swiss Alps ◽

Data Sets ◽

Snow Avalanches ◽

Secondary Importance ◽

Data Set ◽

Avalanche Size ◽

Wet Snow ◽

Danger Level ◽

Avalanche Danger

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

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In-Situ LED-Based Observation of Snow Surface and Depth Transects

Sensors ◽

10.3390/s20082292 ◽

2020 ◽

Vol 20 (8) ◽

pp. 2292

Author(s):

Celeste Barnes ◽

Chris Hopkinson ◽

Thomas Porter ◽

Zhouxin Xi

Keyword(s):

Snow Depth ◽

Surface Albedo ◽

Surface Condition ◽

Field Of View ◽

Snow Surface ◽

Depth Measurement ◽

Individual Distance ◽

Strong Linear Relationship ◽

Better Than

As part of a new snowpack monitoring framework, this study evaluated the feasibility of using an LED LIDAR (Leddar) time of flight sensor for snowpack depth measurement. The Leddar sensor has two additional features over simple sonic ranging sensors: (i) the return signal is divided into 16 segments across a 48° field of view, each recording individual distance-to-target (DTT) measurements; (ii) an index of reflectance or intensity signal is recorded for each segment. These two features provide information describing snowpack morphology and surface condition. The accuracy of Leddar sensor DTT measurements for snow depth monitoring was found to be < 20 mm, which was better than the 50 mm quoted by the manufacturer, and the precision was < 5 mm. Leddar and independent sonic ranger snow depth measurement showed strong linear agreement (r2 = 0.98). There was also a strong linear relationship (r2 = 0.98) between Leddar and manual field snow depth measurements. The intensity signal response was found to correlate with snow surface albedo and inversely with air temperature (r = 0.77 and −0.77, respectively).

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Spatial Phase and the Temporal Structure of the Response to Gratings in V1

Journal of Neurophysiology ◽

10.1152/jn.1998.80.2.554 ◽

1998 ◽

Vol 80 (2) ◽

pp. 554-571 ◽

Cited By ~ 30

Author(s):

Jonathan D. Victor ◽

Keith P. Purpura

Keyword(s):

Spatial Frequency ◽

Spike Train ◽

Temporal Structure ◽

Spike Timing ◽

Dependent Manner ◽

Data Set ◽

Simple Cells ◽

Complex Cells ◽

Spatial Phase ◽

Contrast Information

Victor, Jonathan D. and Keith P. Purpura. Spatial phase and the temporal structure of the response to gratings in V1. J. Neurophysiol. 80: 554–571, 1998. We recorded single-unit activity of 25 units in the parafoveal representation of macaque V1 to transient appearance of sinusoidal gratings. Gratings were systematically varied in spatial phase and in one or two of the following: contrast, spatial frequency, and orientation. Individual responses were compared based on spike counts, and also according to metrics sensitive to spike timing. For each metric, the extent of stimulus-dependent clustering of individual responses was assessed via the transmitted information, H. In nearly all data sets, stimulus-dependent clustering was maximal for metrics sensitive to the temporal pattern of spikes, typically with a precision of 25–50 ms. To focus on the interaction of spatial phase with other stimulus attributes, each data set was analyzed in two ways. In the “pooled phases” approach, the phase of the stimulus was ignored in the assessment of clustering, to yield an index H pooled. In the “individual phases” approach, clustering was calculated separately for each spatial phase and then averaged across spatial phases to yield an index H indiv. H pooled expresses the extent to which a spike train represents contrast, spatial frequency, or orientation in a manner which is not confounded by spatial phase (phase-independent representation), whereas H indiv expresses the extent to which a spike train represents one of these attributes, provided spatial phase is fixed (phase-dependent representation). Here, representation means that a stimulus attribute has a reproducible and systematic influence on individual responses, not a neural mechanism for decoding this influence. During the initial 100 ms of the response, contrast was represented in a phase-dependent manner by simple cells but primarily in a phase-independent manner by complex cells. As the response evolved, simple cell responses acquired phase-independent contrast information, whereas complex cells acquired phase-dependent contrast information. Simple cells represented orientation and spatial frequency in a primarily phase-dependent manner, but also they contained some phase-independent information in their initial response segment. Complex cells showed primarily phase-independent representation of orientation but primarily phase-dependent representation of spatial frequency. Joint representation of two attributes (contrast and spatial frequency, contrast and orientation, spatial frequency and orientation) was primarily phase dependent for simple cells, and primarily phase independent for complex cells. In simple and complex cells, the variability in the number of spikes elicited on each response was substantially greater than the expectations of a Poisson process. Although some of this variation could be attributed to the dependence of the response on the spatial phase of the grating, variability was still markedly greater than Poisson when the contribution of spatial phase to response variance was removed.

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Spatio-Chromatic Information Content of Natural Scenes

Perception ◽

10.1068/v96l1009 ◽

1996 ◽

Vol 25 (1_suppl) ◽

pp. 162-162 ◽

Cited By ~ 1

Author(s):

T Troscianko ◽

C A Parraga ◽

G Brelstaff ◽

D Carr ◽

K Nelson

Keyword(s):

Spatial Frequency ◽

Human Vision ◽

Natural Scenes ◽

Data Set ◽

Frequency Space ◽

Sensitivity Functions ◽

Visual Encoding ◽

Spatial Frequencies ◽

Hyper Spectral ◽

Fourier Spectra

A common assumption in the study of the relationship between human vision and the visual environment is that human vision has developed in order to encode the incident information in an optimal manner. Such arguments have been used to support the 1/f dependence of scene content as a function of spatial frequency. In keeping with this assumption, we ask whether there are any important differences between the luminance and (r/g) chrominance Fourier spectra of natural scenes, the simple expectation being that the chrominance spectrum should be relatively richer in low spatial frequencies than the luminance spectrum, to correspond with the different shape of luminance and chrominance contrast sensitivity functions. We analysed a data set of 29 images of natural scenes (predominantly of vegetation at different distances) which were obtained with a hyper-spectral camera (measuring the scene through a set of 31 wavelength bands in the range 400 – 700 nm). The images were transformed to the three Smith — Pokorny cone fundamentals, and further transformed into ‘luminance’ (r+g) and ‘chrominance’ (r-g) images, with various assumptions being made about the relative weighting of the r and g components, and the form of the chrominance response. We then analysed the Fourier spectra of these images using logarithmic intervals in spatial frequency space. This allowed a determination of the total energy within each Fourier band for each of the luminance and chrominance representations. The results strongly indicate that, for the set of scenes studied here, there was no evidence of a predominance of low-spatial-frequency chrominance information. Two classes of explanation are possible: (a) that raw Fourier content may not be the main organising principle determining visual encoding of colour, and/or (b) that our scenes were atypical of what may have driven visual evolution. We present arguments in favour of both of these propositions.

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Measurement of snow cover depth using 100 × 100 meters sampling plot and Structure from Motion method in Adventdalen, Svalbard

Czech Polar Reports ◽

10.5817/cpr2016-2-14 ◽

2016 ◽

Vol 6 (2) ◽

pp. 155-168

Author(s):

Radim Stuchlík ◽

Jan Russnák ◽

Tomáš Plojhar ◽

Zdeněk Stachoň

Keyword(s):

Spatial Distribution ◽

Snow Cover ◽

Structure From Motion ◽

Snow Depth ◽

Surface Model ◽

Snow Surface ◽

Reference Volume ◽

Snow Cover Depth ◽

Sampling Plot ◽

Motion Method

We tried to verify the concept of Structure from Motion method for measuring the volume of snow cover in a grid of 100×100 m located in Adventdalen, Central Svalbard. As referencing method we utilized 121 depth measurements in one hectare area. Using avalanche probe a snow depth was measured in mentioned 121 nodes of the grid. We detected maximum snow depth of 2.05 m but snowless parts as well. From gathered depths’ data we geostatistically (ordinary kriging) interpolated snow surface model which we used to determine reference volume of snow at research plot (5 569 m3). As a result, we were able to calculate important metrics and analyze topography and spatial distribution of snow cover at the plot. For taking photos for Structure from Motion method, bare pole in hands with a camera mounted was used. We constructed orthomosaic of research plot.

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