Statistical Classification of Self-Organized Snow Surfaces

Journal Article · Thu Jun 07 00:00:00 EDT 2018 · Geophysical Research Letters

DOI:https://doi.org/10.1029/2018gl077616· OSTI ID:1539740

Kochanski, K. ^[1]; Anderson, R. S. ^[2]; Tucker, G. E. ^[2]

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1. Univ. of Colorado, Boulder, CO (United States); DOE/OSTI
2. Univ. of Colorado, Boulder, CO (United States)

Wind-swept snow self-organizes into bedforms. These bedforms affect local and global energy fluxes but have not been incorporated into Earth system models because the conditions governing their development are not well understood. To address this difficulty, we created statistical classifiers, drawn from 736 hr of time-lapse footage in the Colorado Front Range, that predict bedform presence as a function of wind speed and time since snowfall. These classifiers provide the first quantitative predictions of bedform and sastrugi presence in varying weather conditions. We find that the likelihood that a snow surface is covered by bedforms increases with time since snowfall and with wind speed and that the likelihood that a surface is covered by sastrugi increases with time and with the highest wind speeds. Our observations will be useful to Earth system modelers and represent a new step toward understanding self-organized processes that ornament 8% of the surface of the planet.

• Research Organization: Krell Institute, Ames, IA (United States)
• Sponsoring Organization: USDOE Office of Science (SC)
• Grant/Contract Number: FG02-97ER25308
• OSTI ID: 1539740
• Journal Information: Geophysical Research Letters, Journal Name: Geophysical Research Letters Journal Issue: 13 Vol. 45; ISSN 0094-8276
• Publisher: American Geophysical Union
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
Geology