Mapping starting zone snow depth with a ground-based lidar to assist avalanche control and forecasting

2015 ◽  
Vol 120 ◽  
pp. 197-204 ◽  
Author(s):  
Jeffrey S. Deems ◽  
Peter J. Gadomski ◽  
Dominic Vellone ◽  
Ryan Evanczyk ◽  
Adam L. LeWinter ◽  
...  
Keyword(s):  
Snow Depth ◽  
Ground Based Lidar ◽  
Avalanche Control

scholarly journals The spatial variability of snow resistance on potential avalanche slopes

1995 ◽  
Vol 41 (137) ◽  
pp. 183-190 ◽  
Author(s):  
K. W. Birkeland ◽  
K.J. Hansen ◽  
R. L. Brown
Keyword(s):  
Slope Stability ◽  
Spatial Variability ◽  
Linear Relationship ◽  
Complex Terrain ◽  
Snow Depth ◽  
Control Measures ◽  
Snow Avalanches ◽  
Short Time ◽  
Terrain Features ◽  
Avalanche Control

AbstractSince snow avalanches are believed to release from zones of localized weakness, knowledge of snow-strength patterns is important for determining slope stability and for applying effective avalanche-control measures. In this study, the spatial variability of snow resistance (an index of snow strength) and depth were measured and compared with terrain features on two inclined slopes. A refined instrument allowed the strength of an entire snow slab to be characterized in a short time. The spatial pattern of trees appeared to affect the pattern of snow depth at one site, where a significant linear relationship was found between snow depth and average snow resistance. These results suggest that localized snow-depth variations may be important in snow-strength genesis. Although a linear relationship existed at that site, additional factors may be critically relevant. A second site with more complex terrain features and less localized wind drifting did not show a linear relationship between depth and average resistance. Instead, complex patterns of resistance demonstrated that many factors contribute to snow resistance. In particular, die snow overlying rocks was found to have significantly weaker resistance than that in adjacent areas not over rocks.

scholarly journals The spatial variability of snow resistance on potential avalanche slopes

1995 ◽  
Vol 41 (137) ◽  
pp. 183-190 ◽  
Author(s):  
K. W. Birkeland ◽  
K.J. Hansen ◽  
R. L. Brown
Keyword(s):  
Slope Stability ◽  
Spatial Variability ◽  
Linear Relationship ◽  
Complex Terrain ◽  
Snow Depth ◽  
Control Measures ◽  
Snow Avalanches ◽  
Short Time ◽  
Terrain Features ◽  
Avalanche Control

AbstractSince snow avalanches are believed to release from zones of localized weakness, knowledge of snow-strength patterns is important for determining slope stability and for applying effective avalanche-control measures. In this study, the spatial variability of snow resistance (an index of snow strength) and depth were measured and compared with terrain features on two inclined slopes. A refined instrument allowed the strength of an entire snow slab to be characterized in a short time. The spatial pattern of trees appeared to affect the pattern of snow depth at one site, where a significant linear relationship was found between snow depth and average snow resistance. These results suggest that localized snow-depth variations may be important in snow-strength genesis. Although a linear relationship existed at that site, additional factors may be critically relevant. A second site with more complex terrain features and less localized wind drifting did not show a linear relationship between depth and average resistance. Instead, complex patterns of resistance demonstrated that many factors contribute to snow resistance. In particular, die snow overlying rocks was found to have significantly weaker resistance than that in adjacent areas not over rocks.

LONG-TERM CHANGES IN SNOWFALL AND SNOW DEPTH IN TOYAMA PREFECTURE DURING 1974-2015

2018 ◽  
Vol 74 (5) ◽  
pp. I_73-I_78
Author(s):  
Shigehiko ODA ◽  
Takuya MATSUURA ◽  
Masashi SHIMOSAKA ◽  
Taichi TEBAKARI
Keyword(s):  
Snow Depth ◽  
Long Term Changes

scholarly journals Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network

2019 ◽  
Vol 11 (23) ◽  
pp. 2864 ◽  
Author(s):  
Jiping Liu ◽  
Yuanyuan Zhang ◽  
Xiao Cheng ◽  
Yongyun Hu
Keyword(s):  
Neural Network ◽  
Sea Ice ◽  
Snow Depth ◽  
Deep Neural Network ◽  
Arctic Basin ◽  
Temporal Variations ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Thickness ◽  
Arctic Sea

The accurate knowledge of spatial and temporal variations of snow depth over sea ice in the Arctic basin is important for understanding the Arctic energy budget and retrieving sea ice thickness from satellite altimetry. In this study, we develop and validate a new method for retrieving snow depth over Arctic sea ice from brightness temperatures at different frequencies measured by passive microwave radiometers. We construct an ensemble-based deep neural network and use snow depth measured by sea ice mass balance buoys to train the network. First, the accuracy of the retrieved snow depth is validated with observations. The results show the derived snow depth is in good agreement with the observations, in terms of correlation, bias, root mean square error, and probability distribution. Our ensemble-based deep neural network can be used to extend the snow depth retrieval from first-year sea ice (FYI) to multi-year sea ice (MYI), as well as during the melting period. Second, the consistency and discrepancy of snow depth in the Arctic basin between our retrieval using the ensemble-based deep neural network and two other available retrievals using the empirical regression are examined. The results suggest that our snow depth retrieval outperforms these data sets.

Sign in / Sign up

Export Citation Format

Share Document