Comparing simulated and manual snow profiles to derive thresholds for modeled snow instability metrics

2020 ◽  
Author(s):  
Stephanie Mayer ◽  
Alec van Herwijnen ◽  
Mathias Bavay ◽  
Bettina Richter ◽  
Jürg Schweizer
Keyword(s):  
Snow Cover ◽  
Input Data ◽  
Climate Models ◽  
Weather Prediction ◽  
Mechanical Instability ◽  
Critical Crack ◽  
Critical Crack Length ◽  
Snow Stratigraphy ◽  
Meteorological Input ◽  
Weather Stations

<p>Numerical snow cover models enable simulating present or future snow stratigraphy based on meteorological input data from automatic weather stations, numerical weather prediction or climate models. To assess avalanche danger for short-term forecasts or with respect to long-term trends induced by a warming climate, the modeled vertical layering of the snowpack has to be interpreted in terms of mechanical instability. In recent years, improvements in our understanding of dry-snow slab avalanche formation have led to the introduction of new metrics describing the fracture processes leading to avalanche release. Even though these instability metrics have been implemented into the detailed snow cover model SNOWPACK, validated threshold values that discriminate rather stable from rather unstable snow conditions are not readily available. To overcome this issue, we compared a comprehensive dataset of almost 600 manual snow profiles with simulations. The manual profiles were observed in the region of Davos over 17 different winters and include stability tests such as the Rutschblock test as well as observations of signs of instability. To simulate snow stratigraphy at the locations of the manual profiles, we obtained meteorological input data by interpolating measurements from a network of automatic weather stations. By matching simulated snow layers with the layers from traditional snow profiles, we established a method to detect potential weak layers in the simulated profiles and determine the degree of instability. To this end, thresholds for failure initiation (skier stability index) and crack propagation criteria (critical crack length) were calibrated using the observed stability test results and signs of instability incorporated in the manual observations. The resulting instability criteria are an important step towards exploiting numerical snow cover models for snow instability assessment.</p>

A random forest model to assess snow instability from simulated snow stratigraphy

2021 ◽  
Author(s):  
Stephanie Mayer ◽  
Alec van Herwijnen ◽  
Jürg Schweizer
Keyword(s):  
Random Forest ◽  
Snow Cover ◽  
Input Data ◽  
Weather Prediction ◽  
Classification Model ◽  
Weak Layer ◽  
Snow Stratigraphy ◽  
Meteorological Input ◽  
Weather Stations ◽  
Avalanche Danger

<p>Numerical snow cover models enable simulating present or future snow stratigraphy based on meteorological input data from automatic weather stations, numerical weather prediction or climate models. To assess avalanche danger for short-term forecasts or with respect to long-term trends induced by a warming climate, modeled snow stratigraphy has to be interpreted in terms of mechanical instability. Several instability metrics describing the mechanical processes of avalanche release have been implemented into the detailed snow cover model SNOWPACK. However, there exists no readily available method that combines these metrics to predict snow instability.</p><p>To overcome this issue, we compared a comprehensive dataset of almost 600 manual snow profiles with SNOWPACK simulations. The manual profiles were observed in the region of Davos over 17 different winter seasons and include a Rutschblock stability test as well as a local assessment of avalanche danger. To simulate snow stratigraphy at the locations of the manual profiles, we interpolated meteorological input data from a network of automatic weather stations. For each simulated profile, we manually determined the layer corresponding to the weakest layer indicated by the Rutschblock test in the corresponding observed snow profile. We then used the subgroups of the most unstable and the most stable profiles to train a random forest (RF) classification model on the observed stability described by a binary target variable (unstable vs. stable).</p><p>As potential explanatory variables, we considered all implemented stability indices calculated for the manually picked weak layers in the simulated profiles as well as further weak layer and slab properties (e.g. weak layer grain size or slab density).  After selecting the six most decisive features and tuning the hyper-parameters of the RF, the model was able to distinguish between unstable and stable profiles with a five-fold cross-validated accuracy of 88%.</p><p>Our RF model provides the probability of instability (POI) for any simulated snow layer given the features of this layer and the overlying slab. Applying the RF model to each layer of a complete snow profile thus enables the detection of the most unstable layers by considering the local maxima of the POI among all layers of the profile. To analyze the evolution of snow instability over a complete winter season, the RF model can provide the daily maximal POI values for a time series of snow profiles. By comparing this series of POI values with observed avalanche activity, the RF model can be validated.</p><p>The resulting statistical model is an important step towards exploiting numerical snow cover models for snow instability assessment.</p>

scholarly journals Validating modeled critical crack length for crack propagation in the snow cover model SNOWPACK

2019 ◽  
Author(s):  
Bettina Richter ◽  
Jürg Schweizer ◽  
Mathias W. Rotach ◽  
Alec van Herwijnen
Keyword(s):  
Crack Length ◽  
Snow Cover ◽  
Field Experiments ◽  
Detection Method ◽  
Probability Of Detection ◽  
Critical Crack ◽  
Critical Crack Length ◽  
Avalanche Forecasting ◽  
Snow Stratigraphy ◽  
Abstract Data

Abstract. Data on snow stratigraphy and snow instability are of key importance for avalanche forecasting. Snow cover models can improve the spatial and temporal resolution of such data, especially if they also provide information on snow instability. Recently, a new stability criterion, namely a parameterization for the critical crack length, was implemented into the snow cover model SNOWPACK. To validate and improve this parameterization, we therefore used data from three years of field experiments performed close to two automatic weather station above Davos, Switzerland. Monitoring the snowpack on a weekly basis allowed to investigate limitations of the model. Based on 145 experiments we replaced two variables of the original parameterization, which were not sufficiently well modeled, with a fit factor thereby decreasing the normalized root mean square error from 1.80 to 0.28. With this fit factor, the improved parameterization accounts for the grain size resulting in lower critical crack lengths for snow layers with larger grains. This also improved an automatic weak layer detection method using a simple local minimum by increasing the probability of detection from 0.26 to 0.91 and decreased the false alarm ratio from 0.89 to 0.47.

scholarly journals Validating modeled critical crack length for crack propagation in the snow cover model SNOWPACK

2019 ◽  
Vol 13 (12) ◽  
pp. 3353-3366 ◽  
Author(s):  
Bettina Richter ◽  
Jürg Schweizer ◽  
Mathias W. Rotach ◽  
Alec van Herwijnen
Keyword(s):  
Grain Size ◽  
Crack Propagation ◽  
Crack Length ◽  
Snow Cover ◽  
Field Experiments ◽  
Critical Crack ◽  
Critical Crack Length ◽  
Avalanche Forecasting ◽  
Weak Layer ◽  
Snow Stratigraphy

Abstract. Observed snow stratigraphy and snow stability are of key importance for avalanche forecasting. Such observations are rare and snow cover models can improve the spatial and temporal resolution. To evaluate snow stability, failure initiation and crack propagation have to be considered. Recently, a new stability criterion relating to crack propagation, namely the critical crack length, was implemented into the snow cover model SNOWPACK. The critical crack length can also be measured in the field with a propagation saw test, which allows for an unambiguous comparison. To validate and improve the parameterization for the critical crack length, we used data from 3 years of field experiments performed close to two automatic weather stations above Davos, Switzerland. We monitored seven distinct weak layers and performed in total 157 propagation saw tests on a weekly basis. Comparing modeled to measured critical crack length showed some discrepancies stemming from model assumption. Hence, we replaced two variables of the original parameterization, namely the weak layer shear modulus and thickness, with a fit factor depending on weak layer density and grain size. With these adjustments, the normalized root-mean-square error between modeled and observed critical crack lengths decreased from 1.80 to 0.28. As the improved parameterization accounts for grain size, values of critical crack lengths for snow layers consisting of small grains, which in general are not weak layers, become larger. In turn, critical weak layers appear more prominently in the vertical profile of critical crack length simulated with SNOWPACK. Hence, minimal values in modeled critical crack length better match observed weak layers. The improved parameterization of critical crack length may be useful for both weak layer detection in simulated snow stratigraphy and also providing more realistic snow stability information – and hence may improve avalanche forecasting.

scholarly journals Sensitivity of modeled snow stability data to meteorological input uncertainty

2020 ◽  
Author(s):  
Bettina Richter ◽  
Alec van Herwijnen ◽  
Mathias W. Rotach ◽  
Jürg Schweizer
Keyword(s):  
Sensitivity Analysis ◽  
Stability Index ◽  
High Sensitivity ◽  
Layer Formation ◽  
Critical Crack ◽  
Meteorological Forcing ◽  
Critical Crack Length ◽  
Weak Layer ◽  
Meteorological Input ◽  
Increasing Precipitation

Abstract. To perform spatial snow cover simulations for numerical avalanche forecasting, interpolation and downscaling of meteorological data are required, which introduce uncertainties. The repercussions of these uncertainties on modeled snow stability remain mostly unknown. We therefore assessed the contribution of meteorological input uncertainty on modeled snow stability by performing a global sensitivity analysis. We used the numerical snow cover model SNOWPACK to simulate two snow instability metrics, i.e. the skier stability index and the critical crack length, for a field site equipped with an automatic weather station providing the necessary input for the model. Uncertainty ranges for meteorological forcing covered typical differences observed within a distance of 2 km and an elevation change of 200 m. Three different scenarios were investigated to better assess the influence of meteorological forcing on snow stability during a) the weak layer formation period, b) the slab formation period, and c) the weak layer and slab formation period. For each scenario, 14 000 simulations were performed, by introducing quasi-random uncertainties to the meteorological input. Results showed that a weak layer formed in 99.7 % of the simulations, indicating that the weak layer formation was very robust due to the prolonged dry period. For scenario a), modeled grain size of the weak layer was mainly sensitive to precipitation, while the shear strength of the weak layer was sensitive to most input variables, especially air temperature. Once the weak layer existed (case b), precipitation was the most prominent driver for snow stability. The sensitivity analysis highlighted that for all scenarios, the two stability metrics were mostly sensitive precipitation. Precipitation determined the load of the slab, which in turn influenced weak layer properties. For case b) and c), the two stability metrics showed contradicting behaviors. With increasing precipitation, i.e. deep snowpacks, the skier stability index decreased (less stable). In contrast, the critical crack length increased with increasing precipitation. With regard to spatial simulations of snow stability, the high sensitivity on precipitation suggests that accurate precipitation patterns are necessary to obtain realistic snow stability patterns. With regard to spatial simulations of snow stability, the high sensitivity on precipitation suggests that accurate precipitation patterns are necessary to obtain realistic snow stability patterns.

scholarly journals Sensitivity of modeled snow stability data to meteorological input uncertainty

2020 ◽  
Vol 20 (11) ◽  
pp. 2873-2888 ◽  
Author(s):  
Bettina Richter ◽  
Alec van Herwijnen ◽  
Mathias W. Rotach ◽  
Jürg Schweizer
Keyword(s):  
Stability Index ◽  
Layer Formation ◽  
Dry Period ◽  
Critical Crack ◽  
Meteorological Forcing ◽  
Critical Crack Length ◽  
Input Uncertainty ◽  
Weak Layer ◽  
Meteorological Input ◽  
Increasing Precipitation

Abstract. To perform spatial snow cover simulations for numerical avalanche forecasting, interpolation and downscaling of meteorological data are required, which introduce uncertainties. The repercussions of these uncertainties on modeled snow stability remain mostly unknown. We therefore assessed the contribution of meteorological input uncertainty to modeled snow stability by performing a global sensitivity analysis. We used the numerical snow cover model SNOWPACK to simulate two snow instability metrics, i.e., the skier stability index and the critical crack length, for a field site equipped with an automatic weather station providing the necessary input for the model. Simulations were performed for a winter season, which was marked by a prolonged dry period at the beginning of the season. During this period, the snow surface layers transformed into layers of faceted and depth hoar crystals, which were subsequently buried by snow. The early-season snow surface was likely the weak layer of many avalanches later in the season. Three different scenarios were investigated to better assess the influence of meteorological forcing on snow stability during (a) the weak layer formation period, (b) the slab formation period, and (c) the weak layer and slab formation period. For each scenario, 14 000 simulations were performed, by introducing quasi-random uncertainties to the meteorological input. Uncertainty ranges for meteorological forcing covered typical differences observed within a distance of 2 km or an elevation change of 200 m. Results showed that a weak layer formed in 99.7 % of the simulations, indicating that the weak layer formation was very robust due to the prolonged dry period. For scenario a, modeled grain size of the weak layer was mainly sensitive to precipitation, while the shear strength of the weak layer was sensitive to most input variables, especially air temperature. Once the weak layer existed (case b), precipitation was the most prominent driver for snow stability. The sensitivity analysis highlighted that for all scenarios, the two stability metrics were mostly sensitive to precipitation. Precipitation determined the load of the slab, which in turn influenced weak layer properties. For cases b and c, the two stability metrics showed contradicting behaviors. With increasing precipitation, i.e., deep snowpacks, the skier stability index decreased (became less stable). In contrast, the critical crack length increased with increasing precipitation (became more stable). With regard to spatial simulations of snow stability, the high sensitivity to precipitation suggests that accurate precipitation patterns are necessary to obtain realistic snow stability patterns.

scholarly journals MetOp/AVHRR Snow Detection Method for Meteorological Applications

2020 ◽  
Vol 59 (12) ◽  
pp. 2001-2019
Author(s):  
Niilo Siljamo ◽  
Otto Hyvärinen ◽  
Aku Riihelä ◽  
Markku Suomalainen
Keyword(s):  
Snow Cover ◽  
Hydrological Modeling ◽  
Direct Detection ◽  
Weather Prediction ◽  
Training Data ◽  
Two Phase ◽  
European Organization ◽  
Variable Surface ◽  
Weather And Climate ◽  
Weather Stations

AbstractSnow cover plays a significant role in the weather and climate system by affecting the energy and mass transfer between the surface and the atmosphere. It also has far-reaching effects on ecosystems of snow-covered areas. Therefore, global snow-cover observations in a timely manner are needed. Satellite-based instruments can be utilized to produce snow-cover information that is suitable for these needs. Highly variable surface and snow-cover features suggest that operational snow extent algorithms may benefit from at least a partly empirical approach that is based on carefully analyzed training data. Here, a new two-phase snow-cover algorithm utilizing data from the Advanced Very High Resolution Radiometer (AVHRR) on board the MetOp satellites of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) is introduced and evaluated. This algorithm is used to produce the MetOp/AVHRR H32 snow extent product for the Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF). The algorithm aims at direct detection of snow-covered and snow-free pixels without preceding cloud masking. Pixels that cannot be classified reliably to snow or snow-free, because of clouds or other reasons, are set as unclassified. This reduces the coverage but increases the accuracy of the algorithm. More than four years of snow-depth and state-of-the-ground observations from weather stations were used to validate the product. Validation results show that the algorithm produces high-quality snow coverage data that may be suitable for numerical weather prediction, hydrological modeling, and other applications.

scholarly journals Snow fracture in relation to slab avalanche release: critical state for the onset of crack propagation

2017 ◽  
Vol 11 (1) ◽  
pp. 217-228 ◽  
Author(s):  
Johan Gaume ◽  
Alec van Herwijnen ◽  
Guillaume Chambon ◽  
Nander Wever ◽  
Jürg Schweizer
Keyword(s):  
Crack Propagation ◽  
Slope Angle ◽  
Critical Length ◽  
Snow Layer ◽  
Critical Crack ◽  
Critical Crack Length ◽  
New Model ◽  
Avalanche Forecasting ◽  
Weak Layer ◽  
Recent Developments

Abstract. The failure of a weak snow layer buried below cohesive slab layers is a necessary, but insufficient, condition for the release of a dry-snow slab avalanche. The size of the crack in the weak layer must also exceed a critical length to propagate across a slope. In contrast to pioneering shear-based approaches, recent developments account for weak layer collapse and allow for better explaining typical observations of remote triggering from low-angle terrain. However, these new models predict a critical length for crack propagation that is almost independent of slope angle, a rather surprising and counterintuitive result. Based on discrete element simulations we propose a new analytical expression for the critical crack length. This new model reconciles past approaches by considering for the first time the complex interplay between slab elasticity and the mechanical behavior of the weak layer including its structural collapse. The crack begins to propagate when the stress induced by slab loading and deformation at the crack tip exceeds the limit given by the failure envelope of the weak layer. The model can reproduce crack propagation on low-angle terrain and the decrease in critical length with increasing slope angle as modeled in numerical experiments. The good agreement of our new model with extensive field data and the ease of implementation in the snow cover model SNOWPACK opens a promising prospect for improving avalanche forecasting.

Mapping and verification of the snow cover timing in the Baikal region by remote sensing data MODIS “snow cover”

2021 ◽  
Vol 973 (7) ◽  
pp. 21-31
Author(s):  
Е.А. Rasputina ◽  
A.S. Korepova
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Spatial Resolution ◽  
Baikal Region ◽  
Remote Sensing Data ◽  
Meteorological Station ◽  
Sensing Data ◽  
Modis Data ◽  
Weather Stations ◽  
The Difference

The mapping and analysis of the dates of onset and melting the snow cover in the Baikal region for 2000–2010 based on eight-day MODIS “snow cover” composites with a spatial resolution of 500 m, as well as their verification based on the data of 17 meteorological stations was carried out. For each year of the decennary under study, for each meteorological station, the difference in dates determined from the MODIS data and that of weather stations was calculated. Modulus of deviations vary from 0 to 36 days for onset dates and from 0 to 47 days – for those of stable snow cover melting, the average of the deviation modules for all meteorological stations and years is 9–10 days. It is assumed that 83 % of the cases for the onset dates can be considered admissible (with deviations up to 16 days), and 79 % of them for the end dates. Possible causes of deviations are analyzed. It was revealed that the largest deviations correspond to coastal meteorological stations and are associated with the inhomogeneity of the characteristics of the snow cover inside the pixels containing water and land. The dates of onset and melting of a stable snow cover from the images turned out to be later than those of weather stations for about 10 days. First of all (from the end of August to the middle of September), the snow is established on the tops of the ranges Barguzinsky, Baikalsky, Khamar-Daban, and later (in late November–December) a stable cover appears in the Barguzin valley, in the Selenga lowland, and in Priolkhonye. The predominant part of the Baikal region territory is covered with snow in October, and is released from it in the end of April till the middle of May.

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