Avalanche Forecasting Research Articles

Snow–Pack Structure: Stability Analyzed by Pattern–Recognition Techniques

AbstractInternal snow–cover structure depicted by conventional snow profiles is commonly held to be important for avalanche forecasting but it is largely omitted from numerical forecasting schemes owing to quantification difficulties. A subjective test given to practising forecasters illustrated the complex roles of measured profile parameters in describing instability. A clustering technique of pattern recognition, after introducing a snow–profile training set, has demonstrated an objective ability to sort correctly 86% of the layer boundaries associated with slab–avalanche bed surfaces or lubricating layers. Further improvement in stability recognition comes from a newly devised shear test driven by a wedge which quickly locates failure planes within the snow cover. The cluster analysis is then able to sort with similar accuracy between stable and unstable (avalanching) failure planes so located.

Using Linear Discriminant Analysis to Classify Snowfall Situations into Avalanching and Non-Avalanching Ones

AbstractThe existing methods of predicting avalanche danger often do not meet users’ demands because of the empiric character of the insufficient volume of information used. In such forecasts the contribution of each individual parameter into the prognostic information is unknown, and this is very important when studying such an event as avalanche formation, which is conditioned by a complex interaction of numerous factors, including snow accumulation, snow thickness, and the conditions of its development. It is obvious that such problems can be successfully solved by statistical methods, and that explains the growing interest in numerical methods of avalanche forecasting. Problems of multi-dimensional observations arises in many scientific fields. The method suited for this problem is discriminant analysis, the purpose of which is to divide a multi-dimensional observation vector into predetermined classes.This study considers the prognostic (diagnostic) problems of fresh-snow avalanches released during snowfall or in the two days after it has ceased. The theoretical basis is a complex of statistical methods: correlation and dispersion analysis, “sifting” for the choice of predictors’ informative groups, construction of linear parametric discriminant functions, predictions based on training sample, and verification of discriminant functions based on independent material.The archive used in the study consisted of 500 avalanching cases and 1 300 non-avalanching ones. All situations were grouped according to geomorphological characteristics. Each situation is described by eight meteorological characteristics. The results of classification of snowfall situations into avalanching and non-avalanching ones are as follows: reliability ofpis from 75% to 91%,Hfrom 0.15 to 0.51; based on independent material the reliability ofpis from 63% to 85%,Hfrom 0. 10 to 0.56.

Using Linear Discriminant Analysis to Classify Snowfall Situations into Avalanching and Non-Avalanching Ones

AbstractThe existing methods of predicting avalanche danger often do not meet users’ demands because of the empiric character of the insufficient volume of information used. In such forecasts the contribution of each individual parameter into the prognostic information is unknown, and this is very important when studying such an event as avalanche formation, which is conditioned by a complex interaction of numerous factors, including snow accumulation, snow thickness, and the conditions of its development. It is obvious that such problems can be successfully solved by statistical methods, and that explains the growing interest in numerical methods of avalanche forecasting. Problems of multi-dimensional observations arises in many scientific fields. The method suited for this problem is discriminant analysis, the purpose of which is to divide a multi-dimensional observation vector into predetermined classes.This study considers the prognostic (diagnostic) problems of fresh-snow avalanches released during snowfall or in the two days after it has ceased. The theoretical basis is a complex of statistical methods: correlation and dispersion analysis, “sifting” for the choice of predictors’ informative groups, construction of linear parametric discriminant functions, predictions based on training sample, and verification of discriminant functions based on independent material.The archive used in the study consisted of 500 avalanching cases and 1 300 non-avalanching ones. All situations were grouped according to geomorphological characteristics. Each situation is described by eight meteorological characteristics. The results of classification of snowfall situations into avalanching and non-avalanching ones are as follows: reliability of p is from 75% to 91%, H from 0.15 to 0.51; based on independent material the reliability of p is from 63% to 85%, H from 0. 10 to 0.56.

Using Linear Discriminant Analysis to Classify Snowfall Situations into Avalanching and Non-Avalanching Ones

AbstractThe existing methods of predicting avalanche danger often do not meet users’ demands because of the empiric character of the insufficient volume of information used. In such forecasts the contribution of each individual parameter into the prognostic information is unknown, and this is very important when studying such an event as avalanche formation, which is conditioned by a complex interaction of numerous factors, including snow accumulation, the state of snow thickness, and the conditions of its development.It is obvious that such problems can be successfully solved by statistical methods, and that explains the growing interest in numerical methods of avalanche forecasting. Problems of multi-dimensional observations arises in many scientific fields. The method suited for this problem is discriminant analysis, the purpose of which is to divide a multi-dimensional observation vector into predetermined classes.This study considers the prognostic (diagnostic) problems of fresh-snow avalanches released during snowfall or in the two days after it has ceased. The theoretical basis is a complex of statistical methods: correlation and dispersion analysis, “sifting" for the choice of predictors’ informative groups, construction of linear parametric discriminant functions, predictions based on training sample, and verification of discriminant functions based on independent material.The archive used in the study consisted of 500 avalanching cases and 1 300 non-avalanching ones. All situations were grouped according to geomorphological characteristics. Each situation is described by eight meteorological characteristics. The results of classification of snowfall situations into avalanching and non-avalanching ones are as follows: reliability of ρ is from 75% to 91%, H from 0.15 to 0.51; based on independent material the reliability of ρ is from 63% to 85%, H from 0.10 to 0.56.This paper has been accepted in revised form for publication in a later issue of the Journal of Glaciology.

Using Linear Discriminant Analysis to Classify Snowfall Situations into Avalanching and Non-Avalanching Ones

Abstract The existing methods of predicting avalanche danger often do not meet users’ demands because of the empiric character of the insufficient volume of information used. In such forecasts the contribution of each individual parameter into the prognostic information is unknown, and this is very important when studying such an event as avalanche formation, which is conditioned by a complex interaction of numerous factors, including snow accumulation, the state of snow thickness, and the conditions of its development. It is obvious that such problems can be successfully solved by statistical methods, and that explains the growing interest in numerical methods of avalanche forecasting. Problems of multi-dimensional observations arises in many scientific fields. The method suited for this problem is discriminant analysis, the purpose of which is to divide a multi-dimensional observation vector into predetermined classes. This study considers the prognostic (diagnostic) problems of fresh-snow avalanches released during snowfall or in the two days after it has ceased. The theoretical basis is a complex of statistical methods: correlation and dispersion analysis, “sifting" for the choice of predictors’ informative groups, construction of linear parametric discriminant functions, predictions based on training sample, and verification of discriminant functions based on independent material. The archive used in the study consisted of 500 avalanching cases and 1 300 non-avalanching ones. All situations were grouped according to geomorphological characteristics. Each situation is described by eight meteorological characteristics. The results of classification of snowfall situations into avalanching and non-avalanching ones are as follows: reliability of ρ is from 75% to 91%, H from 0.15 to 0.51; based on independent material the reliability of ρ is from 63% to 85%, H from 0.10 to 0.56. This paper has been accepted in revised form for publication in a later issue of the Journal of Glaciology.

Snow Avalanches: A review of Current Research and Applications

AbstractSnow avalanches have long been an active area of applied glaciology. Empirical and traditional methods of forecasting and controlling avalanches are gradually giving way to the application of modern science and engineering. There are four areas of this science where progress is being made through active interaction among scientists, engineers, and practical men. The artificial, release of avalanches as a control measure has seen a large body of research regarding effects of explosives on snow but still is handicapped by inadequate basic knowledge about avalanche release mechanisms. There is currently a surge of interest in testing numerical methods of avalanche forecasting and several sophisticated statistical techniques have been introduced, but operational forecasting still depends largely on empirical experience. The pressure of development on alpine lands has brought to the fore a number of problems associated with mapping avalanches, determining their return intervals and deducing their behavior from both observations of terrain and vegetation and calculating their behavior from theory. The ability to predict the characteristics of moving avalanches is advancing through a combination of theoretical insights and field observations, but is inhibited by the difficulty of the latter.

Recent Progress in North American Avalanche Forecasting and Control

AbstractThe techniques of avalanche forecasting and control have found increasingly wide adoption in North America in the past decade. Numerous government and private agencies are now engaged in work to protect ski areas, highways, railroads and power lines. Practical control measures such as artillery fire have been developed to a high degree of effectiveness, but the construction of permanent defense structures other than snow sheds is still in its infancy. Experimental work on instrumentation, forecasting, defense structures and the use of explosives, artillery and jet aircraft is currently in progress.

Recent Progress in North American Avalanche Forecasting and Control

Abstract The techniques of avalanche forecasting and control have found increasingly wide adoption in North America in the past decade. Numerous government and private agencies are now engaged in work to protect ski areas, highways, railroads and power lines. Practical control measures such as artillery fire have been developed to a high degree of effectiveness, but the construction of permanent defense structures other than snow sheds is still in its infancy. Experimental work on instrumentation, forecasting, defense structures and the use of explosives, artillery and jet aircraft is currently in progress.