Using snow depth observations to provide insight into the quality of snowpack simulations for regional-scale avalanche forecasting. Horton, S. & Haegeli, P. The Cryosphere, 16(8):3393–3411, August, 2022. ![Using snow depth observations to provide insight into the quality of snowpack simulations for regional-scale avalanche forecasting [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Abstract. The combination of numerical weather prediction and snowpack models has potential to provide valuable information about snow avalanche conditions in remote areas. However, the output of snowpack models is sensitive to precipitation inputs, which can be difficult to verify in mountainous regions. To examine how existing observation networks can help interpret the accuracy of snowpack models, we compared snow depths predicted by a weather–snowpack model chain with data from automated weather stations and manual observations. Data from the 2020–2021 winter were compiled for 21 avalanche forecast regions across western Canada covering a range of climates and observation networks. To perform regional-scale comparisons, SNOWPACK model simulations were run at select grid points from the High-Resolution Deterministic Prediction System (HRDPS) numerical weather prediction model to represent conditions at treeline elevations, and observed snow depths were upscaled to the same locations. Snow depths in the Coast Mountain range were systematically overpredicted by the model, while snow depths in many parts of the interior Rocky Mountain range were underpredicted. These discrepancies had a greater impact on simulated snowpack conditions in the interior ranges, where faceting was more sensitive to snow depth. To put the comparisons in context, the quality of the upscaled observations was assessed by checking whether snow depth changes during stormy periods were consistent with the forecast avalanche hazard. While some regions had high-quality observations, other regions were poorly represented by available observations, suggesting in some situations modelled snow depths could be more reliable than observations. The analysis provides insights into the potential for validating weather and snowpack models with readily available observations, as well as for how avalanche forecasters can better interpret the accuracy of snowpack simulations.
@article{horton_using_2022,
title = {Using snow depth observations to provide insight into the quality of snowpack simulations for regional-scale avalanche forecasting},
volume = {16},
issn = {1994-0424},
url = {https://tc.copernicus.org/articles/16/3393/2022/},
doi = {10.5194/tc-16-3393-2022},
abstract = {Abstract. The combination of numerical weather prediction and snowpack models has
potential to provide valuable information about snow avalanche
conditions in remote areas. However, the output of snowpack models is
sensitive to precipitation inputs, which can be difficult to verify in
mountainous regions. To examine how existing observation networks can
help interpret the accuracy of snowpack models, we compared snow depths
predicted by a weather–snowpack model chain with data from automated
weather stations and manual observations. Data from the 2020–2021 winter
were compiled for 21 avalanche forecast regions across western Canada
covering a range of climates and observation networks. To perform
regional-scale comparisons, SNOWPACK model simulations were run at
select grid points from the High-Resolution Deterministic
Prediction System (HRDPS) numerical weather prediction model to
represent conditions at treeline elevations, and observed snow depths
were upscaled to the same locations. Snow depths in the Coast Mountain
range were systematically overpredicted by the model, while snow depths
in many parts of the interior Rocky Mountain range were underpredicted.
These discrepancies had a greater impact on simulated snowpack
conditions in the interior ranges, where faceting was more sensitive to
snow depth. To put the comparisons in context, the quality of the
upscaled observations was assessed by checking whether snow depth
changes during stormy periods were consistent with the forecast
avalanche hazard. While some regions had high-quality observations,
other regions were poorly represented by available observations,
suggesting in some situations modelled snow depths could be more
reliable than observations. The analysis provides insights into the
potential for validating weather and snowpack models with readily
available observations, as well as for how avalanche forecasters can better
interpret the accuracy of snowpack simulations.},
language = {en},
number = {8},
urldate = {2023-06-01},
journal = {The Cryosphere},
author = {Horton, Simon and Haegeli, Pascal},
month = aug,
year = {2022},
keywords = {NALCMS},
pages = {3393--3411},
}
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To examine how existing observation networks can help interpret the accuracy of snowpack models, we compared snow depths predicted by a weather–snowpack model chain with data from automated weather stations and manual observations. Data from the 2020–2021 winter were compiled for 21 avalanche forecast regions across western Canada covering a range of climates and observation networks. To perform regional-scale comparisons, SNOWPACK model simulations were run at select grid points from the High-Resolution Deterministic Prediction System (HRDPS) numerical weather prediction model to represent conditions at treeline elevations, and observed snow depths were upscaled to the same locations. Snow depths in the Coast Mountain range were systematically overpredicted by the model, while snow depths in many parts of the interior Rocky Mountain range were underpredicted. These discrepancies had a greater impact on simulated snowpack conditions in the interior ranges, where faceting was more sensitive to snow depth. To put the comparisons in context, the quality of the upscaled observations was assessed by checking whether snow depth changes during stormy periods were consistent with the forecast avalanche hazard. While some regions had high-quality observations, other regions were poorly represented by available observations, suggesting in some situations modelled snow depths could be more reliable than observations. 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The combination of numerical weather prediction and snowpack models has\npotential to provide valuable information about snow avalanche\nconditions in remote areas. However, the output of snowpack models is\nsensitive to precipitation inputs, which can be difficult to verify in\nmountainous regions. To examine how existing observation networks can\nhelp interpret the accuracy of snowpack models, we compared snow depths\npredicted by a weather–snowpack model chain with data from automated\nweather stations and manual observations. Data from the 2020–2021 winter\nwere compiled for 21 avalanche forecast regions across western Canada\ncovering a range of climates and observation networks. To perform\nregional-scale comparisons, SNOWPACK model simulations were run at\nselect grid points from the High-Resolution Deterministic\nPrediction System (HRDPS) numerical weather prediction model to\nrepresent conditions at treeline elevations, and observed snow depths\nwere upscaled to the same locations. Snow depths in the Coast Mountain\nrange were systematically overpredicted by the model, while snow depths\nin many parts of the interior Rocky Mountain range were underpredicted.\nThese discrepancies had a greater impact on simulated snowpack\nconditions in the interior ranges, where faceting was more sensitive to\nsnow depth. To put the comparisons in context, the quality of the\nupscaled observations was assessed by checking whether snow depth\nchanges during stormy periods were consistent with the forecast\navalanche hazard. 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