Density, specific surface area, and correlation length of snow measured by high‐resolution penetrometry

Abstract

Key Points:• Statistical model relating micro-CT structure to SMP force for many snow data • New method to retrieve density, correlation length, and SSA in the field • Efficient retrieval of spatial variability and 2-D stratigraphy of snow Abstract 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.

Nordic Snow Radar Experiment

“…As snow pit observations were not made directly in the footprint of microwave instruments, with the exception of specific tests (see Proksch et al, 2015b), the snow profile information should be treated with caution. In addition to temporal variability, also the spatial variability of snow should be considered.…”

“…The objective of the IOPs was to complement the collected time series of basic observations, in particular by employing advanced methods for characterization of snow microstructure. The measurements consisted of -SSA analyses using near-infrared (NIR) photography (Matzl et al, 2006), -snow micropenetrometry using the Snow MicroPen, (SMP; Schneebeli et al, 1998;Proksch et al, 2015a),…”

“…To demonstrate the use of the NoSREx data in evaluation of microwave emission and backscattering models, the Microwave Emission Model for Layered Snowpacks adapted to include backscattering (MEMLS3&a) consisting of simulation of both active and passive microwave response (Wiesmann and Mätzler and Wiesmann, 1999;Proksch et al, 2015b) was applied.…”

“…. Simulations in a one-layer configuration using MEMLS3&a (Proksch et al, 2015b) for 50 • incidence angle, applying measured parameters depicted in Fig. 2.…”

“…Data collected during NoSREx have found use in numerous recent studies exploring the modelling of microwave signatures of snow-covered terrain (Proksch et al, 2015b;Tan et al, 2015;Chang et al, 2015;Pan et al, 2016). Here, we provide an overview of the instrumentation and data acquisition protocols used and of the collected microwave signatures.…”