Statistical forecasting of regional avalanche danger using simulated snow-cover data
Statistical forecasting of regional avalanche danger using simulated snow-cover data
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DOI：
10.3189/002214309790152429
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发表时间：
2009-01-01
影响因子：
3.4
通讯作者：
Schweizer, Juerg
中科院分区：
文献类型：
--
作者：
Schirmer, Michael;Lehning, Michael;Schweizer, Juerg
在过去，数值预测区域雪崩危险的统计方法与气象输入变量显示不够准确的结果，可能是由于缺乏雪地层学数据。详细的积雪数据很少使用，因为这些数据不容易获得（人工观测）。随着积雪模型的发展和越来越多地使用，这一缺陷现在可以得到纠正，模型输出可以用作预测模型的输入。我们使用基于物理的积雪模型SNOWPACK的输出与气象变量相结合，调查并建立与区域雪崩危险的联系。雪地层学模拟的位置附近的达沃斯，瑞士，超过九个冬天的自动气象站。只考虑了干雪的情况。各种选择算法被用来确定最重要的模拟雪变量。对数据挖掘和统计方法，包括分类树、人工神经网络、支持向量机、隐马尔可夫模型和最近邻法进行了关于预测区域雪崩危险（欧洲雪崩危险等级）的培训。最佳结果是采用最近邻方法，该方法使用前一天的雪崩危险级别作为额外输入。交叉验证的准确性（命中率）为73%。这项研究表明，模拟雪地层学变量，提供SNOWPACK，能够提高数值雪崩预报。
In the past, numerical prediction of regional avalanche danger using statistical methods with meteorological input variables has shown insufficiently accurate results, possibly due to the lack of snow-stratigraphy data. Detailed snow-cover data were rarely used because they were not readily available (manual observations). With the development and increasing use of snow-cover models this deficiency can now be rectified and model output can be used as input for forecasting models. We used the output of the physically based snow-cover model SNOWPACK combined with meteorological variables to investigate and establish a link to regional avalanche danger. Snow stratigraphy was simulated for the location of an automatic weather station near Davos, Switzerland, over nine winters. Only dry-snow situations were considered. A variety of selection algorithms was used to identify the most important simulated snow variables. Data mining and statistical methods, including classification trees, artificial neural networks, support vector machines, hidden Markov models and nearest-neighbour methods were trained on the forecasted regional avalanche danger (European avalanche danger scale). The best results were achieved with a nearest-neighbour method which used the avalanche danger level of the previous day as additional input. A cross-validated accuracy (hit rate) of 73% was obtained. This study suggests that modelled snow-stratigraphy variables, as provided by SNOWPACK, are able to improve numerical avalanche forecasting.