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Rescal-snow: A model of dunes and snow-waves

Authors: Kochanski, Kelly; Gian-Carlo Defazio; Green, Eric; Barnes, Richard; Downie, Carlos; Rubin, Adam; Rountree, Barry;

Rescal-snow: A model of dunes and snow-waves

Abstract

When wind blows over snow, it self-organizes. This forms surface features, such as ripples and dunes, that alter the reflectivity and thermal conductivity of the snow. Studying these features in the field is cold and challenging (we've tried), so we created rescal-snow to enable snow scientists to study snow features in controlled numerical experiments. We hope that this model will be useful to researchers in snow science, geomorphology, and polar climate. Rescal-snow is able to simulate: Snow/sand grain erosion and deposition by wind Snowfall Time-dependent cohesion (snow sintering) Avalanches of loose grains Rescal-snow is also designed for robust, reproducible science, and contains tools for high-performance computing, data management, and data analysis, including: Workflow tools for generating and running many simulations in parallel A python-based workflow that manages data and analysis at runtime

Keywords

dune, aeolian sediment transport, cellular automata, snow, sediment transport

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
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