Evaluation of the InfraSnow: a handheld device to measure snow specific surface (SSA)

Abstract

Describing the snow microstructure quantitively is crucial to investigate any physical process within the snowpack. Whereas valid and reliable laboratory measurement exists, e.g. micro-CT scanning or gas absorption methods, measuring snow structural quantities on the field is still challenging. So far, the only field-ready and market available device has been the IceCube (IC), which deduces the snows SSA from its diffuse reflectance at near-infrared (NIR) wavelengths of 1320 nm in combination with a radiative transfer model. The InfraSnow (IS) uses a similar measurement principle (at 945 nm), but was realized as a compact handheld instrument. This allows non-destructive measurements on the snow surface or along snow profiles as well as on snow surfaces, where sampling is undesirable, e.g. ski slopes. To make the advantages of the IS available, SLF and FPGA Company GmbH collaborated to launch a small volume production of 25 devices, that are available in a bundle with a dielectric sensor for density and LWC measurements.

The new IS devices were evaluated on-field and in the laboratory using micro-CT and IC reference measurements. In contrast to the prototype validation of Gergely, the conducted comparison with CT-measurements included also new snow samples with SSA up to 63 mm-1. The laboratory data revealed the IS method systematically overestimating the CT-SSA from about 2 to 15 % at SSA between 3 and 61 mm-1. In contrast, the relative random error was found to decrease with increasing SSA from about 20 to 1 %. IC-SSAs laid in between the IS and CT-SSA. For both, the IS and the IC, sampling and the surface preparation, respectively, had a significant influence on the results

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WSL-Institut für Schnee- und Lawinenforschung SLF | Autorenschaft | Datum
Evaluation of the InfraSnow: a handheld device
to measure snow specific surface (SSA)
•The snow microstructure is crucial for any physical process within the snowpack
•Measuring snow structural quantities on the field is still limited
• Measure snow’s SSA from its diffuse near-infrared reflectance (945 nm)
combined with a radiative transfer model
•Allow non-destructive on-field measurements with an affordable instrument
•Develop small batch series of the validated InfraSnow (IS) (Gergely et al., 2014)
with a sensor design similar to the existing prototype
•Compare lab and field measurements with established methods (IceCube , µCT & SMP)
MOTIVATION & IDEA
•IS overestimated the SSA systematically →A correction factor k
was introduced to fit SSAIS to SSAµCT (k = 1.162; Fig. 5)
•Field measurements with a first implementation of k (= 1.09) were in good
agreement with SSA values deduced from SnowMicroPen (SMP) measurements
•Snow sampling (IC), snow surface preparation (IS), and the snow density ρ(IS)
can influence the results considerably: ±20 kg m-3 can lead to an error of up to
20 mm-1. For ρ> 150 kg m-3 the error propagation remains below 8 mm-1
•The InfraSnow is considered an adequate SSA measurement method
with an outstanding ease of use
Conclusion
Laboratory
•SSAIS deviated -2 % to 81 % (RMSErel = 15.2 %)
from the µCT measurements (Fig. 2)
•SSAIS deviated -67 % to 87 % (RMSErel = 12.4 %)
from the IC measurements
•SSAIC (IceCube) deviated -23 % to 45 %
(RMSErel = 7.5 %) from the µCT measurements
Results
Methods and snow samples
Fig. 3: SSA and density profiles from pits taken at Davos Laret. (a) 9th Dec. 2021. (b) 15th Dec. 2021. (c) 22th Dec. 2021. (d) 12th Jan. 2022.
Field
•SSAIS deviated on average between -36 % to 10 %
from a SSA profile derived from a single SMP
measurement (Fig. 3 & Tab. 1)
Fig. 1: Cross section of the housing of the
InfraSnow showing the integrating sphere
(d =38 mm) with the LED light source (red),
diffusing cone (white), photodiode detector
(yellow), circuit board (green) and the cap
which serves as a calibration target.
abcd
Fig. 2: SSA of 13 snow samples measured in the lab with the InfraSnow
and the IceCube versus µCT reference measurements. The marked (*)
data points were excluded from the calculated deviations as the IC snow
sampling was limited.
Tab. 1: Minimal, mean, and maximal SSA deviation from a single SSASMP.
profile
Literature:
Gergely, M., Wolfsperger, F., & Schneebeli, M. (2014).
Simulation and validation of the InfraSnow: an instrument
to measure snow optically equivalent grain size. IEEE
Transactions on Geoscience and Remote Sensing, 52(7),
4236-4247. https://doi.org/10.1109/TGRS.2013.2280502
InfraSnow
950 nm
Fig. 2: Spectral reflectance of snow for varying grain
size (adapted from Nolin, 2000).
Tab. 2: Minimal, mean, and maximal deviation of a dielectric density
measurement (SSS) vs. a volume (100 cm2)weighted (cutter).
*
*
snow1 snow4 snow5 snow6 snow7
snow type
snow
classification
snow preparation snow# ρbox
[kg m
-3
]
ρµCT
[kg m
-3
]
SSA
µCT
[mm
-1
]
coarse
FC, RGlr ice pow coarse >2mm stored ca. one year 6 - 544 2.9
RG ca. 1 y stored; ice pow mid 0.7…1.4 mm -506 6.9
RG ca. 1 y stored; ice pow mid 0.7…1.4 mm -505 4.5
RG 01.12.20; ice pow mid 0.7…1.4 mm 5455 441 9.6
ice powder
RGsr, FC 02.12.20; fine sieved < 0.71 mm 4253 254 39.9
RGsr, FC app one year old; fine sieved < 0.71 mm 243 311 16.0
new snow
PP, DF 01.12.20; sieved 2 mm 84 92 60.5
PP, DF 01.12.20; compressed 127 125 44.7
RGsr compacted in nature; sintered ca. 2 years storage 7 - 399 9.7
snowmaker
PP, DF 01.12.20; sieved 4 mm 160 63 63.4
PP, DF 01.12.20; compressed 113 107 44.5
snowmaker
PP, DF
snowmaker
-15/30°
C freshly produced sieved 4 mm
120 116 50.2
coarse MF melt freeze crusts / MF sieved with 2 mm 421 419 10.0
Fig. 4: The expected (model-based) dependency
of reflectance, density and SSA of the InfraSnow
(contour plot), and the µCT validation data (black
dots with colored circle).
Fig. 5: Sum of squares ∑(SSAIS - SSAµCT )2 of 13 snow
samples plotted over the linear correction factor to
adapt the underlying density-SSA-reflection
dependency to best fit SSAIS to SSAµCT.
WSL Institute for Snow and Avalanche Research SLF | wolfsperger@slf.ch | 26.09.2022