Forecasting the formation of critical snow layers using a coupled snow cover and weather model

Bellaire, Sascha; Jamieson, Bruce

doi:10.1016/j.coldregions.2013.06.007

Cited by 43 publications

(27 citation statements)

References 18 publications

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“…Snow cover modeling offers an alternative way to obtain snow stratigraphy information (Bartelt & Lehning, 2002;Brun et al, 1992;Vionnet et al, 2012) with high temporal and spatial resolution. Coupling of such snow cover models with numerical weather prediction models allows predicting snowpack properties in time and space (Bellaire & Jamieson, 2013;Quéno et al, 2016;Vionnet et al, 2016). By adding a mechanical model to this model chain we can reach out for predicting snow instability in time and space (Vernay et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Describing Snow Instability by Failure Initiation, Crack Propagation, and Slab Tensile Support

Reuter

Schweizer²

2018

Geophysical Research Letters

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Snow instability is a generic term describing the propensity of a snow slope to avalanche. In need of a concise mechanics‐based concept we suggest a framework based on failure initiation, crack propagation, and slab tensile support. Following these three steps we modeled three metrics from mechanical data, which we derived from snow micropenetrometer signals. Verifying the metrics with field measurements confirmed that slab thickness and weak layer strength typically influence failure initiation, elastic modulus and weak layer fracture energy largely control crack propagation, and slab thickness and tensile strength provide the required tensile support. For all three metrics, considering slab layering was essential. Validation with signs of instability showed that the most accurate model includes all three steps – suggesting that snow instability can be described by failure initiation, crack propagation, and slab tensile support. Further validation is needed to assess the framework's potential for operational use.

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Section: Introductionmentioning

confidence: 99%

Describing Snow Instability by Failure Initiation, Crack Propagation, and Slab Tensile Support

Reuter

Schweizer²

2018

Geophysical Research Letters

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“…The other implemented option, I02, is the default formulation of the ISBA-ES snow model (Boone, 2002;Sun et al, 1999):…”

Section: Thermal Conductivitymentioning

confidence: 99%

“…We included the ISBA-ES formulation (B02, Boone, 2002) in ESCROC for which this threshold is set to r min = 0.03 for snow densities above ρ r = 200 kg m −3 and to higher values up to 0.05 for very low density, following…”

Section: Liquid Water Contentmentioning

confidence: 99%

“…Several organisations in different countries have implemented snowpack-modelling approaches for this purpose (Durand et al, 1999;Lehning et al, 1999;VikhamarSchuler et al, 2011;Bellaire and Jamieson, 2013). The goal is to provide a description of the space and time variability of the current snowpack, which is out of reach using observations only and to anticipate its evolution according to the expected meteorological conditions a few hours to days ahead.…”

Section: Introductionmentioning

confidence: 99%

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A multiphysical ensemble system of numerical snow modelling

Lafaysse¹,

Cluzet²,

Dumont³

et al. 2017

The Cryosphere

103

156

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Abstract. Physically based multilayer snowpack models suffer from various modelling errors. To represent these errors, we built the new multiphysical ensemble system ES-CROC (Ensemble System Crocus) by implementing new representations of different physical processes in the deterministic coupled multilayer ground/snowpack model SUR-FEX/ISBA/Crocus. This ensemble was driven and evaluated at Col de Porte (1325 m a.s.l., French alps) over 18 years with a high-quality meteorological and snow data set. A total number of 7776 simulations were evaluated separately, accounting for the uncertainties of evaluation data. The ability of the ensemble to capture the uncertainty associated to modelling errors is assessed for snow depth, snow water equivalent, bulk density, albedo and surface temperature. Different sub-ensembles of the ESCROC system were studied with probabilistic tools to compare their performance. Results show that optimal members of the ESCROC system are able to explain more than half of the total simulation errors. Integrating members with biases exceeding the range corresponding to observational uncertainty is necessary to obtain an optimal dispersion, but this issue can also be a consequence of the fact that meteorological forcing uncertainties were not accounted for. The ESCROC system promises the integration of numerical snow-modelling errors in ensemble forecasting and ensemble assimilation systems in support of avalanche hazard forecasting and other snowpack-modelling applications.

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“…Truthful identification of the precipitation phase (rain/snow) is of course crucial for the functioning of meteorological models that forecast the precipitation phase itself [2] but also for accurate correction of gauge measured winter precipitation [3] and for land surface models (LSM) predicting snow accumulation and melt [4], glacier and polar ice water balance models [5], models for lake and sea ice growth [6], and climate change models [7]. It is also important for models predicting avalanche hazards [8], sublimation of snow in forests [9], urban snowmelt quality [10], winter road safety [11], infiltration into frozen soils [12], survival of mammals and plants under snow cover [13], flooding from rain on snow events [14] etc. Precipitation phase determination is a modeling challenge for both hydrology and meteorology; therefore, a cross discipline approach combining methods and knowledge from both sciences could lead to new insight for both.…”

Section: Introductionmentioning

confidence: 99%

Meteorological Knowledge Useful for the Improvement of Snow Rain Separation in Surface Based Models

et al. 2015

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An accurate precipitation phase determination-i.e., solid versus liquid-is of paramount importance in a number of hydrological, ecological, safety and climatic applications. Precipitation phase can be determined by hydrological, meteorological or combined approaches. Meteorological approaches require atmospheric data that is not often utilized in the primarily surface based hydrological or ecological models. Many surface based models assign precipitation phase from surface temperature dependent snow fractions, which assume that atmospheric conditions acting on hydrometeors falling through the lower atmosphere are invariant. This ignores differences in phase change probability caused by air mass boundaries which can introduce a warm air layer over cold air leading to more atmospheric melt energy than expected for a given surface temperature, differences in snow grain-size or precipitation rate which increases the magnitude of latent heat exchange between the hydrometers and atmosphere required to melt the snow resulting in snow at warmer temperatures, or earth surface properties near a surface observation point heating or cooling a shallow layer of air allowing rain at cooler temperatures or snow at warmer OPEN ACCESSHydrology 2015, 2 267 temperatures. These and other conditions can be observed or inferred from surface observations, and should therefore be used to improve precipitation phase determination in surface models.

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Forecasting the formation of critical snow layers using a coupled snow cover and weather model

Cited by 43 publications

References 18 publications

Describing Snow Instability by Failure Initiation, Crack Propagation, and Slab Tensile Support

Describing Snow Instability by Failure Initiation, Crack Propagation, and Slab Tensile Support

A multiphysical ensemble system of numerical snow modelling

Meteorological Knowledge Useful for the Improvement of Snow Rain Separation in Surface Based Models

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