Preprints
https://doi.org/10.5194/hess-2021-119
https://doi.org/10.5194/hess-2021-119

  11 Mar 2021

11 Mar 2021

Review status: this preprint is currently under review for the journal HESS.

Decision tree-based detection of blowing snow events in the European Alps

Zhipeng Xie1, Weiqiang Ma1,3, Yaoming Ma1,3, Zeyong Hu2,3, Genhou Sun4, Yizhe Han1,5, Wei Hu1,5, Rongmingzhu Su1,5, and Yixi Fan1,5 Zhipeng Xie et al.
  • 1Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, China
  • 2Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China
  • 3CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing, 100101, China
  • 4School of Atmospheric Sciences, Sun Yat-sen University, 135 Xingang Xi Road, Guangzhou, 510275, China
  • 5University of Chinese Academy of Sciences, Beijing, 100049, China

Abstract. Blowing snow processes are crucial in shaping the strongly heterogeneous spatiotemporal distribution of snow, and in regulating subsequent snowpack evolution in mountainous terrain. Although empirical formulae and a constant threshold wind speed have been widely used to estimate the occurrence of blowing snow in regions with sparse observations, the scarcity of in-situ observations in mountainous regions contrasts with the demands of models for reliable observations at high spatiotemporal resolution. Therefore, these methods struggle to accurately capture the high local variability of blowing snow. This study investigated the potential capability of the decision tree model (DTM) to detect blowing snow in the European Alps. The DTMs were constructed based on routine meteorological observations (mean wind speed, maximum wind speed, air temperature and relative humidity). Twenty repetitions of random sub-sampling validation test with an optimal size ratio (0.8) between the training and validation subset were applied to train and assess the DTMs. Results show that the maximum wind speed contributes most to the classification accuracy, and the inclusion of more predictor variables improves the overall accuracy. However, the spatiotemporal transferability of the DTM might be limited if divergent distributions exist between stations. Although both the site-specific DTMs and site-independent DTM show strong performance for accurately detecting blowing snow, specific assessment indicators varied between stations and surface conditions. Events for which blowing snow and snowfall occurred simultaneously were detected the most reliably. Although models failed to fully reproduce the high frequency of local blowing snow events, they have been demonstrated a promising approach requiring limited meteorological variables and have the potential to scale to multiple stations across different regions.

Zhipeng Xie et al.

Status: open (until 06 May 2021)

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  • RC1: 'Comment on hess-2021-119', Anonymous Referee #1, 06 Apr 2021 reply

Zhipeng Xie et al.

Zhipeng Xie et al.

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Short summary
Ground information on the occurrence of blowing snow has been sorely lacking because direct observations of blowing snow are sparse in time and space. In this paper, we investigated the potential capability of the decision tree model to detect blowing snow events in the European Alps. Trained with routine meteorological observations, the decision tree model can be used as an efficient tool to detect blowing snow occurrences across different regions requiring limited meteorological variables.