Instruments and Methods Simulating complex snow distributions in windy environments using SnowTran-3D
Glen E. Liston,Robert B. Haehnel,Matthew Sturm,Christopher A. Hiemstra,Svetlana Berezovskaya,Ronald D. Tabler +5 more
TL;DR: In this article, a generalized version of the original Liston and Sturm (1998) model, called SnowTran-3D (version 2.0), is presented to simulate wind-related snow distributions over the range of topographic and climatic environments found globally.
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Abstract: We present a generalized version of SnowTran-3D (version 2.0), that simulates wind- related snow distributions over the range of topographic and climatic environments found globally. This version includes three primary enhancements to the original Liston and Sturm (1998) model: (1) an improved wind sub-model, (2) a two-layer sub-model describing the spatial and temporal evolution of friction velocity that must be exceeded to transport snow (the threshold friction velocity) and (3) implementation of a three-dimensional, equilibrium-drift profile sub-model that forces SnowTran-3D snow accumulations to duplicate observed drift profiles. These three sub-models allow SnowTran-3D to simulate snow-transport processes in variable topography and different snow climates. In addition, SnowTran-3D has been coupled to a high-resolution, spatially distributed meteorological model (MicroMet) to provide more realistic atmospheric forcing data. MicroMet distributes data (precipi- tation, wind speed and direction, air temperature and relative humidity) obtained from meteorological stations and/or atmospheric models located within or near the simulation domain. SnowTran-3D has also been coupled to a spatially distributed energy- and mass-balance snow-evolution modeling system (SnowModel) designed for application in any landscape and climate where snow is found. SnowTran-3D is typically run using temporal increments ranging from 1 hour to 1 day, horizontal grid increments ranging from 1 to 100 m and time-spans ranging from individual storms to entire snow seasons.
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Citations
Estimating Snow Water Equivalent Using Snow Depth Data and Climate Classes
TL;DR: In this article, a method of estimating snow bulk density is presented and then used to convert snow depth to snow water equivalent (SWE), which is grounded in the fact that depth varies over a range that is many times greater than that of bulk density, and estimates derived from measured depths and modeled densities generally fall close to measured values of SWE.
A Distributed Snow Evolution Modeling System (SnowModel)
Glen E. Liston,Kelly Elder +1 more
- 01 Dec 2004
TL;DR: In this paper, a spatially distributed snow-evolution modeling system called SnowModel is proposed for application in landscapes, climates, and conditions where snow occurs, which is an aggregation of four submodels: MicroMet defines meteorological forcing conditions, EnBal calculates surface energy exchanges, SnowPack simulates snow depth and water-equivalent evolution, and SnowTran-3D accounts for snow redistribution by wind.
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Inhomogeneous precipitation distribution and snow transport in steep terrain
TL;DR: In this paper, a numerical model is developed, describing the relevant processes of saltation, suspension, and preferential deposition, which is used to simulate a 120 h snow storm period over a steep alpine ridge, for which snow distribution measurements are available.
The Changing Cryosphere: Pan-Arctic Snow Trends (1979–2009)
TL;DR: Using MicroMet and SnowModel in conjunction with land cover, topography, and 30 years of the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) atmospheric reanalysis data, a distributed snow-related dataset was created including air temperature, snow precipitation, snow season timing and length, maximum snow water equivalent (SWE) depth, average snow density, snow sublimation, and rain-on-snow events.
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Mapping snow depth from manned aircraft on landscape scales at centimeter resolution using structure-from-motion photogrammetry
TL;DR: In this article, the same authors applied the structure from motion (SfM) algorithm to the measurement of snow depth from a single-person unmanned aerial vehicle (UAV).
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