Comparison of varied complexity parameterizations in estimating blowing snow occurrences
Comparison of varied complexity parameterizations in estimating blowing snow occurrences
复制标题
估算吹雪发生时不同复杂性参数化的比较
DOI：
10.1016/j.jhydrol.2023.129291
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发表时间：
2023-02
影响因子：
6.4
通讯作者：
Sun Genhou
中科院分区：
文献类型：
--
作者：
Xie Zhipeng;Ma Yaoming;Ma Weiqiang;Hu Zeyong;Sun Genhou
吹雪对积雪的时空演变有重大影响。准确预测雪粒运动的起始是吹雪建模的先决条件之一。先前的研究提出了各种复杂性参数化来估计吹雪的发生。然而，仍然缺乏对不同方案在吹雪识别方面的性能的定量评估，特别是在提出经验方案的地区之外。本研究详细介绍了五种不同复杂程度的不同参数化在估计法国中部阿尔卑斯山吹雪发生情况时的性能比较研究。我们的结果表明，传统方案（例如基于恒定和温度的经验阈值风速方案、概率估计和基于物理的方法）可以在大多数站点准确检测到不到一半的吹雪事件。此外，风速的空间变异性会导致吹雪发生的相当大的空间变异性，而传统方法无法捕获这种变异性。基于决策树的模型可以更准确地检测吹雪发生，并且可以通过包含低风速站吹雪发生的信息来捕获雪输送的空间变化。然而，无论采用何种训练策略，此处采用的基于决策树的模型都估计了太多的误报。这项研究通过介绍当前检测吹雪发生的各种方案的最新技术和局限性，为吹雪建模做出了贡献，并强调了参数化修改、关键参数校准和优化的重要性。
Blowing snow has a major impact on the spatial–temporal evolution of snow cover. An accurate prediction of the initiation of snow particle movement is one of the prerequisites for blowing snow modeling. Previous studies have proposed varied complexity parameterizations to estimate the occurrence of blowing snow. However, a quantitative evaluation of the performance of different schemes in blowing snow identification remains lacking, particularly outside the regions where empirical schemes were proposed. This study details a comparative study of the performance of five distinct parameterizations with varying degrees of complexity in estimating the occurrence of blowing snow over the central French Alps. Our results show that less than half of blowing snow events can be accurately detected at most stations by traditional schemes, for example, the constant and temperature-based empirical threshold wind speed schemes, probability estimation, and physically based methods. In addition, the spatial variability of wind speed can result in considerable spatial variability in the occurrence of blowing snow, which cannot be captured by traditional methods. Decision tree-based models can detect blowing snow occurrences with higher accuracy, and the spatial variation in snow transport can be captured by including information on blowing snow occurrences at low wind speed stations. However, regardless of the training strategies employed, the decision tree-based model employed here estimated far too many false alarms. This study contributes to blowing snow modeling by presenting the current state-of-the-art and limitations of various schemes for detecting blowing snow occurrences, and highlights the importance of parameterization modification, key parameter calibration, and optimization.
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影响因子：
4.1
作者：
M. Lehning;C. Fierz
通讯作者：
M. Lehning;C. Fierz
影响因子：
3
作者：
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影响因子：
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DOI：
10.5194/tc-14-1713-2020
发表时间：
2020-05
期刊：
The Cryosphere
影响因子：
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作者：
C. Amory
通讯作者：
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影响因子：
3.8
作者：
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通讯作者：
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