Density, specific surface area, and correlation length of snow measured by high‐resolution penetrometry. Issue 2 (24th February 2015)

• Record Type: Journal Article
• Authors: Proksch, Martin; Löwe, Henning; Schneebeli, Martin
• Is Part Of: Journal of geophysical research. Volume 120:Issue 2(2015:Apr.)
• Page Start: 346
• Page End: 362
• Publication Date: 2015-02-24
• Subjects: Geomorphology -- Periodicals; 551.3
• DOI: 10.1002/2014JF003266
• Languages: English
• ISSNs: 2169-9003

Abstract

<abstract abstract-type="main" id="jgrf20357-abs-0001"> <title>Abstract</title> <p id="jgrf20357-para-0001">Precise measurements of snow structural parameters are crucial to understand the formation of snowpacks by deposition and metamorphism and to characterize the stratigraphy for many applications and remote sensing in particular. The area‐wide acquisition of structural parameters at high spatial resolution from state‐of‐the‐art methods is, however, still cumbersome, since the time required for a single profile is a serious practical limitation. As a remedy we have developed a statistical model to extract three major snow structural parameters: density, correlation length, and specific surface area (SSA) solely from the SnowMicroPen (SMP), a high‐resolution penetrometer, which allows a meter profile to be measured with millimeter resolution in less than 1 min. The model was calibrated by combining SMP data with 3‐D microstructural data from microcomputed tomography which was used to reconstruct full‐depth snow profiles from different snow climates (Alpine, Arctic, and Antarctic). Density, correlation length, and SSA were derived from the SMP with a mean relative error of 10.6%, 16.4%, and 23.1%, respectively. For validation, we compared the density and SSA derived from the SMP to traditional measurements and near‐infrared profiles. We demonstrate the potential of our method by the retrieval of a two‐dimensional stratigraphy at Kohnen Station, Antarctica, from a 46 m long SMP transect. The result clearly reveals past depositional and metamorphic events, and our findings show that the SMP can be used as an objective, high‐resolution tool to retrieve essential snow structural parameters efficiently in the field.</p> </abstract>