The Prairie Blowing Snow Model: characteristics, validation, operation

Pomeroy, John W.; Gray, David B.; Landine, P. G.

doi:10.1016/0022-1694(93)90171-5

Cited by 287 publications

(240 citation statements)

References 8 publications

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“…Considerable variability in the spatial snow distribution can be introduced through the interaction between wind and snow with terrain and vegetation (Elder et al, 1991;Blöschl, 1999;Liston et al, 2007). In fact, wind has been shown to be the dominant influence on spatial variability of snow in complex terrain (Pomeroy et al, 1993;Winstral et al, 2002;Sturm and Wagner, 2010). Without prior knowledge of the spatial snow distribution in a given area, arbitrary manual snow measurements will not provide accurate estimates of snow depth over large alpine regions (Elder et al, 1991;Anderton et al, 2004;Erickson et al, 2005).…”

Section: A Hedrick Et Al: Lidar Validation Of Snodasmentioning

confidence: 99%

Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements

et al. 2015

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Abstract. Repeated light detection and ranging (lidar) surveys are quickly becoming the de facto method for measuring spatial variability of montane snowpacks at high resolution. This study examines the potential of a 750 km 2 lidar-derived data set of snow depths, collected during the 2007 northern Colorado Cold Lands Processes Experiment (CLPX-2), as a validation source for an operational hydrologic snow model. The SNOw Data Assimilation System (SNODAS) model framework, operated by the US National Weather Service, combines a physically based energy-and-mass-balance snow model with satellite, airborne and automated groundbased observations to provide daily estimates of snowpack properties at nominally 1 km resolution over the conterminous United States. Independent validation data are scarce due to the assimilating nature of SNODAS, compelling the need for an independent validation data set with substantial geographic coverage.Within 12 distinctive 500 × 500 m study areas located throughout the survey swath, ground crews performed approximately 600 manual snow depth measurements during each of the CLPX-2 lidar acquisitions. This supplied a data set for constraining the uncertainty of upscaled lidar estimates of snow depth at the 1 km SNODAS resolution, resulting in a root-mean-square difference of 13 cm. Upscaled lidar snow depths were then compared to the SNODAS estimates over the entire study area for the dates of the lidar flights. The remotely sensed snow depths provided a more spatially continuous comparison data set and agreed more closely to the model estimates than that of the in situ measurements alone. Finally, the results revealed three distinct areas where the differences between lidar observations and SNODAS estimates were most drastic, providing insight into the causal influences of natural processes on model uncertainty.

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Section: A Hedrick Et Al: Lidar Validation Of Snodasmentioning

confidence: 99%

Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements

et al. 2015

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“…Because this module uses irradiance as an estimate for transmittance, the self-shading irradiance was used as input for the longVt module. The pbsmSnobal module calculates snow transport and sublimation following Pomeroy et al (1993). However, it was set to inhibit blowing snow, resulting in a point-scale accumulation and ablation of a snow-pack.…”

Section: Crhm and Snobalmentioning

confidence: 99%

Implications of mountain shading on calculating energy for snowmelt using unstructured triangular meshes

Marsh

Pomeroy

Spiteri

2012

Hydrological Processes

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In many parts of the world, the snowmelt energy balance is dominated by net solar shortwave radiation. This is the case in the Canadian Rocky Mountains, where clear skies dominate the winter and spring. In mountainous regions, solar irradiance at the snow surface is not only affected by solar angles, atmospheric transmittance, and the slope and aspect of immediate topography, but also by shadows from surrounding terrain. Many hydrological models do not consider such horizon-shadows. The accumulation of errors in estimating solar irradiance by neglecting horizon-shadows can lead to significant errors in calculating the timing and rate of snowmelt due to the seasonal storage of internal energy in the snowpack.A common approach to representing the landscape is through structured meshes. iii

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“…The sublimation calculation for a column of blowing snow over a unit area of land is based on a vertical integration of the sublimation rate of a single ice particle having mean mass m at height z, the mean particle mass being determined using a two-parameter gamma distribution of particle size and the change in this distribution with height [Pomeroy et al, 1993]:…”

Section: Fully Developed Blowing Snow Sublimation Ebmentioning

confidence: 99%

“…The prairie blowing snow model (PBSM) was first developed in 1987 as a single column mass and energy balance that calculates blowing snow transport and sublimation rates [Pomeroy, 1988[Pomeroy, , 1989 and later extended to include a snow cover mass balance for the case of two dimensions [Pomeroy et al, 1993]. The model presented here uses a modified, single column calculation with new methods to calculate the inputs to this scheme and to scale the fluxes from a point to a landscape in a snow mass balance calculation.…”

mentioning

confidence: 99%

Prairie and arctic areal snow cover mass balance using a blowing snow model

Pomeroy¹,

Li²

2000

J. Geophys. Res.

168

156

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Abstract. Algorithms to calculate the threshold wind speed and the effect of exposed vegetation on saltation and to describe vertical profiles of humidity in blowing snow, permit the calculation of point blowing snow transport and sublimation fluxes using standard meteorological and landcover data or simple interfaces with climate models. Blowing snow Saskatchewan prairie and an arctic site. Field measurements at these sites indicated that from 48% to 58% of snowfall was removed by blowing snow before melt began. Simulations suggest that the ratios of snow removed and sublimated by blowing snow to that transported were 2:1 and 1:1 at the prairie and arctic sites respectively. The resulting methodology was capable of estimating winter season mass balances for these snowpacks that compared well with snowfall and snow accumulation measurements.

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The Prairie Blowing Snow Model: characteristics, validation, operation

Cited by 287 publications

References 8 publications

Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements

Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements

Implications of mountain shading on calculating energy for snowmelt using unstructured triangular meshes

Prairie and arctic areal snow cover mass balance using a blowing snow model

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