Evaluation of the SNOWPACK model’s metamorphism and microstructure in Canada: a case study

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

doi:10.1080/02723646.2018.1472984

Cited by 3 publications

(6 citation statements)

References 64 publications

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“…Finally, percolation is also affected by snow grains, which is hard to evaluate. A previous work in our group (Madore et al, 2018) suggested that the optical snow grain size bias is a function of the dominant metamorphic process in place. In our case, an overestimation of the optical grain size was measured.…”

Section: Discussionmentioning

confidence: 86%

“…Overall, this paper proposes a configuration of the SNOWPACK model well adapted to the conditions of Glacier National Park, leading to accurate melt and percolation timing, as well as a good correlation with wet snow avalanche cycles. Future work will include further empirical development of new snow density parameterization as well as further investigation into grain size and type biases in the model following the approach of Madore et al (2018), in order to propose a snow grain correction function adapted to our study region. These results are relevant for the ongoing development of the SNOWPACK model as well as for the snow modeling community that makes use of similar thermodynamic snow models for climate scenarios, snow mass balance studies, surface-atmosphere feedback, hydrological processes, etc.…”

Section: Discussionmentioning

confidence: 99%

“…Further uncertainties arise from the snow grain size simulations for which the bias depends on the metamorphism equation in place. More specifically, Madore et al (2018) have demonstrated that while density can be reproduced with reasonable accuracy over the range of 100-500 kg m −3 in Canada, the snow grain size overestimation is more important in an equilibrium metamorphism environment than a kinetic growth environment. Given that porosity will increase with the increasing grain size and decreasing density (Clifton et al, 2008), this is of primary importance in water flow (i.e., percolation) and ultimately stability computation, where overestimated grain size will overestimate porosity.…”

Section: Snowpack and Water Percolationmentioning

confidence: 99%

“…Frontiers in Earth Science frontiersin.org 4.5.2.3 Grain size and type Finally, grain size and type were also evaluated. Madore et al (2018) have shown that biases between the observed and simulated snow grain size can be attributed to the metamorphic process in place. Given that the metamorphic processes are mainly driven by the temperature gradient, it can be expected that the biases between the observed and simulated grain size can vary with climatic conditions.…”

Section: Figure 10mentioning

confidence: 99%

“…Further improvement could arise from a modification to the grain size, given that grain size is one of the parameters driving percolation (Yamaguchi et al, 2010). The previous work from Madore et al (2018) suggested that SNOWPACK overestimated snow grain size, an overestimation that increases as the grain size increases, which could lead to biases in the percolation. They suggested the biases to be a function of the metamorphic process in place (i.e., kinetic growth vs. equilibrium) so that an empirical correction to grain size adapted to Rogers Pass could further improve the percolation simulation.…”

Section: Improvements From Empirical Optimization Of Snowpack Processesmentioning

confidence: 99%

See 4 more Smart Citations

Investigation into percolation and liquid water content in a multi-layered snow model for wet snow instabilities in Glacier National Park, Canada

Madore

Fierz²,

Langlois

2022

Front. Earth Sci.

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Water percolation in snow plays a crucial role in the avalanche risk assessment. Liquid water content and wetting front are hard to measure in the field; hence, accurate simulation of the phenomena can be of great help to forecasters. This study was the first to evaluate water percolation simulations with the SNOWPACK model using Richards’ scheme on Mount Fidelity, Glacier National Park, Canada. The study highlights that, at this site, an updated configuration on precipitation phase transition and new snow density can significantly improve simulations of the snow cover, and water percolation in particular, which can be relevant in an era of an increased occurrence of rain-on-snow (ROS) events. More specifically, emphasis was put on the quality of the input data and parameters. The analysis of the precipitation phase temperature threshold showed that a value of 1.4°C was the best suited to track the rain/snow transition on site. A 10-year analysis of 24-h precipitation measured using the rain gauge and 24-h new snow water equivalent showed an excellent correlation. New snow density sub-models were evaluated using the 24-h new snow density values taken by the park technicians. The BELLAIRE model performed best and was used to drive the snow simulations. Two SNOWPACK snow simulations were evaluated using 1) rain gauge precipitation amount (PCPM) and 2) automatic snow height measurement (HS) at the same site. Both runs simulated the main snowpack layers observed during the dry season (i.e., before spring percolation was observed), and both simulated the snow properties with good accuracy. The water equivalent of snow cover, used as a proxy for a first-order characterization of the simulations generated by both simulations, was slightly underestimated compared with four manual measurements taken on-site during the winter. Nevertheless, the comparison of both measured density and modeled bulk density showed great correspondence. The percolation timing and wetting front depth were evaluated using field measurements from field campaigns and continuous observations from on-site instruments. The main percolation events were correctly simulated and were coincident with the observed wet avalanche cycles. The results highlight the need for accurate input data on valid simulation of the wetting front and percolation timing on site. Good percolation information generated using the SNOWPACK model and Richards’ scheme could be used to assess the snowpack stability by forecasters in areas where such data are available.

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Section: Discussionmentioning

confidence: 86%

Section: Discussionmentioning

confidence: 99%

Section: Snowpack and Water Percolationmentioning

confidence: 99%

Section: Figure 10mentioning

confidence: 99%

Section: Improvements From Empirical Optimization Of Snowpack Processesmentioning

confidence: 99%

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Investigation into percolation and liquid water content in a multi-layered snow model for wet snow instabilities in Glacier National Park, Canada

Madore

Fierz²,

Langlois

2022

Front. Earth Sci.

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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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Subgridding high-resolution numerical weather forecast in the Canadian Selkirk mountain range for local snow modeling in a remote sensing perspective

Billecocq,

Langlois,

Montpetit

2024

The Cryosphere

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Abstract. Snow water equivalent (SWE) is a key variable in climate and hydrology studies. Yet, 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-microwave approaches for global SWE monitoring. Designing a SWE retrieval algorithm is not trivial, as multiple combinations of snow microstructure representations and SWE can yield the same radar signal. Retrieval algorithm designs are converging towards forward modeling approaches using an educated first guess on the snowpack structure. Snow highly varies in space and time, especially in mountain environments where the complex topography affects atmospheric and snowpack state variables in numerous ways. In Canada, automatic weather stations are too sparse, and high-resolution numerical weather prediction 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 (HRDPS). 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. We then used Alpine3D to spatialize atmospheric parameters and radiation input accounting for terrain reflections, and we performed the snow simulations. We evaluated modeled snowpack state variables relevant for microwave remote sensing against simulated profiles generated with automatic weather station data and compared them to simulated profiles driven by raw HRDPS data. The subgridding framework improves the optical grain size bias by 18 % on average and the modeled SWE by 16 % compared to simulations driven with raw HRDPS forecasts. This work could improve the snowpack radar backscattering modeling by up to 7 dB and serves as a basis for SWE retrieval algorithms using forward modeling in a Bayesian framework.

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Evaluation of the SNOWPACK model’s metamorphism and microstructure in Canada: a case study

Cited by 3 publications

References 64 publications

Investigation into percolation and liquid water content in a multi-layered snow model for wet snow instabilities in Glacier National Park, Canada

Investigation into percolation and liquid water content in a multi-layered snow model for wet snow instabilities in Glacier National Park, Canada

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 mountain range for local snow modeling in a remote sensing perspective

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