Statistical avalanche forecating using meteorological data from Bear Pass, British Columbia, Canada

• Main Author: Floyer, James Antony
• Language: English
• Published: 2009
• Online Access: http://hdl.handle.net/2429/14259

A framework is presented for a numerical prediction scheme with a prediction accuracy of greater than 70% at Bear Pass, British Columbia, Canada. One way analysis of variance and canonical discriminant analysis are used to identify the principal variables that allow discrimination between avalanche and non-avalanche time periods. The optimum variable set is then used in a Mahalanobis-metric nearest neighbours model to determine the observations in the historical database that most closely represent conditions of the forecast period. The information about these neighbours can be used by the forecaster to aid in making an avalanche forecast. In this study, a univariate analysis of the meteorological and snowpack data from The Pass is used to assess the contribution of each of the variables for use in avalanche prediction. Using these data, the snow-climate of the area is assessed and it is shown that Bear Pass may be classified as a maritime snow climate, although some elements of a transitional snow-climate are evident. A canonical discriminant analysis is performed for both a two-group discrimination into avalanche and non-avalanche periods and for a three-group discrimination into wet, dry and non-avalanche periods. For the three-group discrimination, a two-stage discrimination is preferred, involving first discriminating into wet-avalanche or dry-avalanche periods and then discriminating between wet or dry-avalanche and non-avalanche periods. The analysis is performed for all areas in a combined analysis and also for individual sub-areas defined within The Pass. Improvements on classification rates to three out of the four sub-areas are found compared to the analysis for the whole Pass. Various parameters that affect the nearest neighbour model performance are analyzed. The effect of group size distribution is assessed, as well as the number and threshold number of neighbours that form the basis for the forecast decision. The effect of dimensionality on the model is also analyzed. The Mahalanobis-metric nearest neighbours model is compared to Cornice, a nearest neighbours model developed for avalanche forecasting in Scotland. Classification rates for the two models are found to be very similar. Finally, preliminary findings from a new method using time patterns of predictor variables is presented. The method makes use of time series data from remote weather stations. Sections of the time series are similar to present conditions are identified through a deconvolution process. It is hoped that this method may offer improvements to the conventional nearest neighbours method by implicitly assuming that meteorological conditions are correlated in time.