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Avalanche danger level characteristics from field observations of snow instability

机译:雪崩危险水平特征从雪不稳定的现场观察

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Avalanche danger levels are described in qualitative terms that mostly are not amenable to measurements or observations. However, estimating and improving forecast consistency and accuracy require descriptors that can be observed or measured. Therefore, we aim to characterize the avalanche danger levels based on expert field observations of snow instability. We analyzed 589 field observations by experienced researchers and forecasters recorded mostly in the region of Davos (Switzerland) during 18 winter seasons (2001–2002 to 2018–2019). The data include a snow profile with a stability test (rutschblock, RB) and observations on snow surface quality, drifting snow, signs of instability and avalanche activity. In addition, observers provided their estimate of the local avalanche danger level. A snow stability class ( very poor , poor , fair , good , very good ) was assigned to each profile based on RB score, RB release type and snowpack characteristics. First, we describe some of the key snowpack characteristics of the data set. In most cases, the failure layer included persistent grain types even after a recent snowfall. We then related snow instability data to the local avalanche danger level. For the danger levels 1–Low to 4–High , we derived typical stability distributions. The proportions of profiles rated poor and very poor clearly increased with increasing danger level. For our data set, the proportions were 5?%, 13?%, 49?% and 63?% for the danger levels 1–Low to 4–High , respectively. Furthermore, we related the local avalanche danger level to the occurrence of signs of instability such as whumpfs, shooting cracks and recent avalanches. The absence of signs of instability was most closely related to 1–Low and the presence of them to 3–Considerable . Adding the snow stability class and the 3?d sum of new snow depth improved the discrimination between the lower three danger levels. Still, 2–Moderate was not well described. Nevertheless, we propose some typical situations that approximately characterize each of the danger levels. Obviously, there is no single easily observable set of parameters that would allow us to fully characterize the avalanche danger levels. One reason for this shortcoming is the fact that the snow instability data we analyzed usually lack information on spatial frequency, which is needed to reliably assess the danger level.
机译:雪崩危险水平在定性术语中描述,主要是不适合测量或观察。然而,估计和提高预测一致性和准确性要求可以观察或测量的描述符。因此,我们的目标是根据雪不稳定的专家场观察来表征雪崩危险水平。在18个冬季(2001-2002至2018-2019)中,我们分析了经验丰富的研究人员和主要在达沃斯(瑞士)地区的589名经验丰富的研究人员和预测员。这些数据包括具有稳定性测试(Rutschblock,RB)的雪崩曲线和雪表面质量的观察,漂流雪,不稳定性和雪崩活动的迹象。此外,观察者还提供了对当地雪崩危险水平的估计。基于RB得分,RB释放类型和积雪特性,将分配给每个档案的雪稳定性类(非常差,差,公平,非常好的)。首先,我们描述了数据集的一些关键积雪特性。在大多数情况下,即使在最近的降雪之后,故障层也包括持久的谷物类型。然后我们将雪不稳定数据相关联到本地雪崩危险水平。对于危险水平为1低至4高,我们导出了典型的稳定性分布。随着危险水平的增加,差异差异差的比例明显增加。对于我们的数据集,对于4-低至4高的危险水平,比例分别为5?%,13℃,49倍。此外,我们与当地的雪崩危险水平相关,以发生不稳定性的迹象,例如鞭打,射击裂缝和最近的雪崩。没有不稳定的迹象与1低的迹象和它们的存在与3相当相当。添加雪稳定性等级和3?D总和的新雪深度改善了较低的三个危险水平之间的歧视。仍然,2-中等没有很好地描述。尽管如此,我们提出了一些典型的情况,大致表征每个危险程度。显然,没有单一容易观察到的参数集,允许我们充分表征雪崩危险程度。这种缺点的一个原因是我们分析的雪不稳定数据通常缺乏关于空间频率的信息,这是可靠地评估危险水平所需的空间频率。

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