10.16904/envidat.330
Cristina Pérez-Guillén
Cristina
Pérez-Guillén
0000-0003-2596-1046
WSL Institute for Snow and Avalanche Research SLF
Frank Techel
Frank
Techel
WSL Institute for Snow and Avalanche Research SLF
Martin Hendrick
Martin
Hendrick
WSL Institute for Snow and Avalanche Research SLF
Michele Volpi
Michele
Volpi
Swiss Data Science Center
Alec van Herwijnen
Alec
van Herwijnen
WSL Institute for Snow and Avalanche Research SLF
Tasko Olevski
Tasko
Olevski
Swiss Data Science Center
Guillaume Obozinski
Guillaume
Obozinski
Swiss Data Science Center
Fernando Pérez-Cruz
Fernando
Pérez-Cruz
Swiss Data Science Center
ETH
Jürg Schweizer
Jürg
Schweizer
WSL Institute for Snow and Avalanche Research SLF
Weather, snowpack and danger ratings data for automated avalanche danger level predictions
2022
EnviDat
AVALANCHE
AVALANCHE DANGER LEVEL
AVALANCHE FORCASTING
MACHINE LEARNING
NUMERICAL AVALANCHE FORECASTING
Cristina Pérez-Guillén
Cristina
Pérez-Guillén
0000-0003-2596-1046
WSL Institute for Snow and Avalanche Research SLF
1997-11-11
en
dataset
https://www.envidat.ch/dataset/weather-snowpack-danger_ratings-data
https://www.envidat.ch/dataset/54d7dc19-e70c-4998-bfb6-caffe41c83e6
185690485 bytes
17378680 bytes
5.3 KB
CSV
md
1.0
WSL Data Policy
Each set includes the meteorological variables (resampled 24-hour averages) and the profile variables extracted from the simulated profiles for each of the weather stations of the IMIS network in Switzerland, and, the danger ratings for dry-snow conditions assigned to the location of the station. The data set of RF 1 contains the danger ratings published in the official Swiss avalanche bulletin, and the data set of RF 2 is a quality-controlled subset of danger ratings.
These data are the basis of the following publication: Pérez-Guillén, C., Techel, F., Hendrick, M., Volpi, M., van Herwijnen, A., Olevski, T., Obozinski, G., Pérez-Cruz, F., and Schweizer, J.: Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland, Nat. Hazards Earth Syst. Sci., 22, 2031–2056, https://doi.org/10.5194/nhess-22-2031-2022, 2022.
5.95587
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47.80838
10.49203
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Switzerland
Swiss Data Science Center
grant C18-05 “DEAPSsnow”