Uncertainties in the SNOWPACK multilayer snow model for a Canadian avalanche context: sensitivity to climatic forcing data

Côté, Kevin; Madore, Jean-Benoît; Langlois, Alexandre

doi:10.1080/02723646.2016.1277935

Cited by 8 publications

(5 citation statements)

References 18 publications

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“…For precipitation, in most cases, bias is corrected but MAE increases. Indeed, even though the model on average underestimates precipitation, major precipitation events are overestimated (Côté et al, 2017). The "one-directional" lapse-rate correction reduces the overall bias by accurately correcting the small and common underestimation errors but accentuates the overall larger and rarer overestimation errors, thus increasing the MAE.…”

Section: Discussionmentioning

confidence: 99%

“…These constraints are essential to keep the algorithm from degenerating and generate irrelevant alignments. However, atmospheric models tend to strongly underestimate precipitations in mountain environments (Bellaire et al, , 2013Côté et al, 2017). Hence, profiles generated from atmospheric models and ground truth profiles can have a significant difference in HS.…”

Section: Snow Modeling Evaluationmentioning

confidence: 99%

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Subgridding High Resolution Numerical Weather Forecast in the Canadian Selkirk range for local snow modelling in a remote sensing perspective

Billecocq

Langlois

Montpetit

2023

Preprint

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Abstract. Snow Water Equivalent (SWE) is a key variable in climate and hydrology studies. Current SWE products mask out high topography areas due to the coarse resolution of the satellite sensors used. The snow remote sensing community is hence pushing towards active microwaves approaches for global SWE monitoring. However, designing a SWE retrieval algorithm is not trivial, as multiple combinations of snow microstructure representations and SWE can yield the same radar signal. The community is converging towards forward modeling approaches using an educated first guess on the snowpack structure. Yet, snow highly varies in space and time, especially in mountain environments where the complex topography affects atmospheric and snowpack state variables in numerous ways. Automatic Weather Stations (AWS) are too sparse, and high-resolution Numerical Weather Predictions systems have a maximal resolution of 2.5 km × 2.5 km, which is too coarse to capture snow spatial variability in a complex topography. In this study, we designed a subgridding framework for the Canadian High Resolution Deterministic Prediction System. The native 2.5 km × 2.5 km resolution forecast was subgridded to a 100 m × 100 m resolution and used as the input for snow modeling over two winters in Glacier National Park, British Columbia, Canada. Air temperature, relative humidity, precipitation and wind speed were first parameterized regarding elevation using six Automatic Weather Stations. Alpine3D was then used to spatialize atmospheric parameters and radiation input accounting for terrain reflections and perform the snow simulations. Modeled snowpack state variables relevant for microwave remote sensing were evaluated against profiles generated with Automatic Weather Stations data and compared to raw HRDPS driven profiles. Overall, the subgridding framework improves the optical grain size (OGS) bias by 0.04 mm, the density bias by 2.7 kg · m−3 and the modelled SWE by 17 % (up to 41 % in the best case scenario). Overall, this work provides the necessary basis for SWE retrieval algorithms using forward modeling in a Bayesian framework.

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Section: Discussionmentioning

confidence: 99%

Section: Snow Modeling Evaluationmentioning

confidence: 99%

Subgridding High Resolution Numerical Weather Forecast in the Canadian Selkirk range for local snow modelling in a remote sensing perspective

Billecocq

Langlois

Montpetit

2023

Preprint

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“…Previous snow sensitivity studies typically focused on snow depth or snow water equivalent (SWE). Uncertainties in modeled snow depth or SWE were estimated from meteorological input uncertainty (e.g., Bellaire et al, 2011;Côté et al, 2017;Lapo et al, 2015;Raleigh et al, 2015;Sauter and Obleitner, 2015), different model setups (Günther et al, 2019;Schlögl et al, 2016) or different physical model assumptions (Günther et al, 2019;Lafaysse et al, 2017). Uncertainties from meteorological input had the highest impact on SWE (Günther et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of modeled snow stability data to meteorological input uncertainty

Herwijnen¹,

Rotach

Schweizer³

2020

Nat. Hazards Earth Syst. Sci.

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Abstract. To perform spatial snow cover simulations for numerical avalanche forecasting, interpolation and downscaling of meteorological data are required, which introduce uncertainties. The repercussions of these uncertainties on modeled snow stability remain mostly unknown. We therefore assessed the contribution of meteorological input uncertainty to modeled snow stability by performing a global sensitivity analysis. We used the numerical snow cover model SNOWPACK to simulate two snow instability metrics, i.e., the skier stability index and the critical crack length, for a field site equipped with an automatic weather station providing the necessary input for the model. Simulations were performed for a winter season, which was marked by a prolonged dry period at the beginning of the season. During this period, the snow surface layers transformed into layers of faceted and depth hoar crystals, which were subsequently buried by snow. The early-season snow surface was likely the weak layer of many avalanches later in the season. Three different scenarios were investigated to better assess the influence of meteorological forcing on snow stability during (a) the weak layer formation period, (b) the slab formation period, and (c) the weak layer and slab formation period. For each scenario, 14 000 simulations were performed, by introducing quasi-random uncertainties to the meteorological input. Uncertainty ranges for meteorological forcing covered typical differences observed within a distance of 2 km or an elevation change of 200 m. Results showed that a weak layer formed in 99.7 % of the simulations, indicating that the weak layer formation was very robust due to the prolonged dry period. For scenario a, modeled grain size of the weak layer was mainly sensitive to precipitation, while the shear strength of the weak layer was sensitive to most input variables, especially air temperature. Once the weak layer existed (case b), precipitation was the most prominent driver for snow stability. The sensitivity analysis highlighted that for all scenarios, the two stability metrics were mostly sensitive to precipitation. Precipitation determined the load of the slab, which in turn influenced weak layer properties. For cases b and c, the two stability metrics showed contradicting behaviors. With increasing precipitation, i.e., deep snowpacks, the skier stability index decreased (became less stable). In contrast, the critical crack length increased with increasing precipitation (became more stable). With regard to spatial simulations of snow stability, the high sensitivity to precipitation suggests that accurate precipitation patterns are necessary to obtain realistic snow stability patterns.

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“…However, only a few studies have so far assessed the uncertainty of snow cover models. Côté et al (2017) investigated the sensitivity of modeled snow height to three different weather models for five different automatic weather stations. They found that differences in forecast precipitation influenced modeled snow height.…”

mentioning

confidence: 99%

Sensitivity of modeled snow stability data to meteorological input uncertainty

Herwijnen¹,

Rotach²,

Schweizer³

2020

Preprint

View full text Add to dashboard Cite

Abstract. To perform spatial snow cover simulations for numerical avalanche forecasting, interpolation and downscaling of meteorological data are required, which introduce uncertainties. The repercussions of these uncertainties on modeled snow stability remain mostly unknown. We therefore assessed the contribution of meteorological input uncertainty on modeled snow stability by performing a global sensitivity analysis. We used the numerical snow cover model SNOWPACK to simulate two snow instability metrics, i.e. the skier stability index and the critical crack length, for a field site equipped with an automatic weather station providing the necessary input for the model. Uncertainty ranges for meteorological forcing covered typical differences observed within a distance of 2 km and an elevation change of 200 m. Three different scenarios were investigated to better assess the influence of meteorological forcing on snow stability during a) the weak layer formation period, b) the slab formation period, and c) the weak layer and slab formation period. For each scenario, 14 000 simulations were performed, by introducing quasi-random uncertainties to the meteorological input. Results showed that a weak layer formed in 99.7 % of the simulations, indicating that the weak layer formation was very robust due to the prolonged dry period. For scenario a), modeled grain size of the weak layer was mainly sensitive to precipitation, while the shear strength of the weak layer was sensitive to most input variables, especially air temperature. Once the weak layer existed (case b), precipitation was the most prominent driver for snow stability. The sensitivity analysis highlighted that for all scenarios, the two stability metrics were mostly sensitive precipitation. Precipitation determined the load of the slab, which in turn influenced weak layer properties. For case b) and c), the two stability metrics showed contradicting behaviors. With increasing precipitation, i.e. deep snowpacks, the skier stability index decreased (less stable). In contrast, the critical crack length increased with increasing precipitation. With regard to spatial simulations of snow stability, the high sensitivity on precipitation suggests that accurate precipitation patterns are necessary to obtain realistic snow stability patterns. With regard to spatial simulations of snow stability, the high sensitivity on precipitation suggests that accurate precipitation patterns are necessary to obtain realistic snow stability patterns.

show abstract

Uncertainties in the SNOWPACK multilayer snow model for a Canadian avalanche context: sensitivity to climatic forcing data

Cited by 8 publications

References 18 publications

Subgridding High Resolution Numerical Weather Forecast in the Canadian Selkirk range for local snow modelling in a remote sensing perspective

Subgridding High Resolution Numerical Weather Forecast in the Canadian Selkirk range for local snow modelling in a remote sensing perspective

Sensitivity of modeled snow stability data to meteorological input uncertainty

Sensitivity of modeled snow stability data to meteorological input uncertainty

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