March Wet Avalanche Prediction at Bridger Bowl Ski Area, Montana (original) (raw)
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Computer Assisted Avalanche Forecasting: Skier-Triggered Avalanches
2004
Computer assisted avalanche forecasting has become a valuable tool in some forecasting operations in Canada. Avalanche forecasting models are based on statistical, numerical and/or rule-based methods, but usually predict natural rather than skier-triggered avalanches. A nearest neighbour forecasting model for skier-triggered avalanches on post storm weak layers was improved by adding snowpack properties including a stability index to meteorological variables. However, a skier stability index did not improve the forecasting success on storm snow instabilities. The best forecast could be achieved by using meteorological parameters.
Probabilistic analysis of recent snow avalanche activity and weather in the French Alps
The characterization of statistical relationships between snow avalanche occurrence and climate can be useful for avalanche prediction. We investigated the relationship between avalanche occurrences between 1978 and 2003 and meteorological parameters for 576 avalanche events from 12 avalanche tracks in the Valloire valley in the French Alps. Probabilities of avalanche occurrence based on logistic regression analyses were calculated at a daily and yearly time scale, by differentiating high-and low-frequency avalanche tracks. For high-frequency avalanche tracks, the daily probability of avalanche depends on the precipitation (water equivalent, i.e. WE) the day before a given avalanche event, along with the mean air temperature the day of the event. For low-frequency avalanche tracks, on the other hand, it depends on precipitation (WE) the day preceding a given event, only. We also tested the relationship between various meteorological parameters and the type of avalanche. The occurrence of dry snow avalanches is related to total precipitation (WE) on the day of and the day before a given event, whereas that of wet snow avalanches depends on precipitation (WE) the day of a given event, and maximum air temperature during the event. Our results show that for high-frequency avalanche tracks, annual probabilities of high avalanche activity depend on the occurrences of successive days (≥3 days) with high precipitation in winter and above-average air temperature (mean ± 1 S.D.). For low-frequency avalanche tracks, probabilities of high avalanche activity depend on the occurrences of successive days (≥ 3 days) with high precipitation in winter. The sensitivity of these models was tested through bootstrap analyses. We also discuss the role of meteorological parameters highlighted in these models.
Avalanche forecasting in a heavy snowfall area using the snowpack model
Cold Regions Science and Technology, 2008
We describe the use of SNOWPACK, a snow cover model, for areas with heavy snowfall. Record-breaking snowfall was recorded over the Sea of Japan and northwest coast of Honshu in the winter of 2005/2006. Avalanche forecasting was conducted at Tsunan, Niigata Prefecture, where the snow depth exceeded 4 m. Measurements from an Automated Meteorological Data Acquisition System (AMEDAS) operated by the Japan Meteorological Agency were used as the model input data. To verify the model output, snow pit observations were carried out at 10-day intervals. Simulated snow profiles were verified by applying a comparative method developed by Lehning et al. [Lehning, M., Fierz, C., Lundy, C., 2001. An objective snow profile comparison method and its application to SNOWPACK. Cold Reg, Sci. Technol. 33, 253–261.] and were in reasonable agreement with the observed results, with an agreement score of 0.74. However, the equations for the stability index (SI) were unsuitable for the study area considered....
Probabilistic Analysis of Recent Snow Avalanche Activity and Climate in the French Alps
Proceedings of the 2004 International Snow Science Workshop Jackson Hole Wyoming, 2004
The characterization of statistical relationships between snow avalanche occurrence and climate can be useful for avalanche prediction. We investigated the relationship between avalanche occurrences between 1978 and 2003 and meteorological parameters for 576 avalanche events from 12 avalanche tracks in the Valloire valley in the French Alps. Probabilities of avalanche occurrence based on logistic regression analyses were calculated at a daily and yearly time scale, by differentiating high-and low-frequency avalanche tracks. For high-frequency avalanche tracks, the daily probability of avalanche depends on the precipitation (water equivalent, i.e. WE) the day before a given avalanche event, along with the mean air temperature the day of the event. For low-frequency avalanche tracks, on the other hand, it depends on precipitation (WE) the day preceding a given event. We also tested the relationship between various meteorological parameters and the type of avalanche. The occurrence of dry snow avalanches is related to total precipitation (WE) on the day of and the day before a given event, whereas that of wet snow avalanches depends on precipitation (WE) the day of a given event, and maximum air temperature during the event. Our results show that for high-frequency avalanche tracks, annual probabilities of high avalanche activity depend on the occurrences of successive days (≥3 days) with high rainfall in winter and above-average air temperature (mean + 1 SD). For low-frequency avalanche tracks, probabilities of high avalanche activity depend on the occurrences of successive days (≥3 days) with high rainfall in winter.
Forecasting Factors for Skier-Triggered Avalanches at a Helicopter Skiing Operation
2000
To forecast skier-triggered avalanches, stability, snowpack and meteorological variables, and records of previous avalanche activity are typically used. The relative importance of, and interaction between, variables used to forecast skier-triggered avalanches have received little attention. This study analyzes the influence of forecasting variables at a heli-skiing operation in the Columbia Mountains of British Columbia, Canada. Forecasting variables are individually assessed
Remote Sensing, 2019
Although snow avalanches are among the most destructive natural disasters, and result in losses of life and economic damages in mountainous regions, far too little attention has been paid to the prediction of the snow avalanche hazard using advanced machine learning (ML) models. In this study, the applicability and efficiency of four ML models: support vector machine (SVM), random forest (RF), naïve Bayes (NB) and generalized additive model (GAM), for snow avalanche hazard mapping, were evaluated. Fourteen geomorphometric, topographic and hydrologic factors were selected as predictor variables in the modeling. This study was conducted in the Darvan and Zarrinehroud watersheds of Iran. The goodness-of-fit and predictive performance of the models was evaluated using two statistical measures: the area under the receiver operating characteristic curve (AUROC) and the true skill statistic (TSS). Finally, an ensemble model was developed based upon the results of the individual models. Results show that, among individual models, RF was best, performing well in both the Darvan (AUROC = 0.964, TSS = 0.862) and Zarrinehroud (AUROC = 0.956, TSS = 0.881) watersheds. The accuracy of the ensemble model was slightly better than all individual models for generating the snow avalanche hazard map, as validation analyses showed an AUROC = 0.966 and a TSS = 0.865 in the Darvan watershed, and an AUROC value of 0.958 and a TSS value of 0.877 for the Zarrinehroud watershed. The results indicate that slope length, lithology and relative slope position (RSP) are the most important factors controlling snow avalanche distribution. The methodology developed in this study can improve risk-based decision making, increases the credibility and reliability of snow avalanche hazard predictions and can provide critical information for hazard managers.
Cold Regions Science and Technology, 1999
Using classification and regression tree models, we evaluated 31 factors in terms of their importance to explaining avalanche activity indices at two ski areas: Alta, UT and Mammoth Mountain, CA. This study derived new empirical factors that combined wind velocity with new snow amount, air temperatures with time, and total snow depth with time. The analyses created over-fit tree models in exploring structures inherent in the data to obtain the relative ranking and scores of various combinations of the 31 factors. Avalanche activity indices included maximum size, number of releases and sum of sizes of released avalanches. Results showed that time lagged conventional factors describing snowfall and derived wind-drift parameters ranked highest in all tests. Snow drift factors segregated into prominent wind directions showed only moderate importance. Among the non-storm factors, the starting snow depth of a particular year ranked highest showing the importance of interannual variability. This was followed by the accumulated vapor pressure difference, which we formulated to better describe the conditioning of old snow with age. The average snow depth increase and other factors followed in importance.
Evaluation and Comparison of Statistical and Conventional Methods of Forecasting Avalanche Hazard
Journal of Glaciology, 1977
Principal problems concerning the raw data and methodological limitations of statistical and conventional avalanche forecasting methods are summarized. The concepts of four statistical models based on multivariate data analysis, are outlined in a few words. In order to give an idea of the potential and quality of the different methods, test runs over two winters are discussed and a tentative store is established. Statistical models I and IV, together with the conventional forecast, attain a score of 70-80%, whereas statistical models II and III show a slightly poorer performance.
Cold Regions Science and Technology
The application of physically-based numerical modeling of the snowpack in support of avalanche hazard prediction is increasing. Modeling , in complement to direct observations and weather forecasting , provides information otherwise unavailable on the present and future state of the snowpack and its mechanical stability. However, there is a significant mismatch between the capabilities of modeling tools developed by research organizations and implemented by some operational services, and the actual operational use of those by avalanche forecasters, thereby causing frustration on both sides. By summarizing currently implemented modeling tools specifically designed for avalanche forecasting, we intend to diminish and contribute to bridge this gap. We highlight specific features and potential added value, as well as challenges preventing a more widespread use of these modeling tools. Lessons learned from currently used methods are explored and provided, as well as prospects for the future, including a list of the most critical issues to be addressed.