Intercomparison of snow density measurements: bias, precision, and vertical resolution

Abstract

Abstract. Density is a fundamental property of porous media such as snow. A wide range of snow properties and physical processes are linked to density, but few studies have addressed the uncertainty in snow density measurements. No study has yet quantitatively considered the recent advances in snow measurement methods such as micro-computed tomography (µCT) in alpine snow. During the MicroSnow Davos 2014 workshop, different approaches to measure snow density were applied in a controlled laboratory environment and in the field. Overall, the agreement between µCT and gravimetric methods (density cutters) was 5 to 9 %, with a bias of −5 to 2 %, expressed as percentage of the mean µCT density. In the field, density cutters overestimate (1 to 6 %) densities below and underestimate (1 to 6 %) densities above a threshold between 296 to 350 kg m −3 , dependent on cutter type. Using the mean density per layer of all measurement methods applied in the field (µCT, box, wedge, and cylinder cutters) and ignoring ice layers, the variation between the methods was 2 to 5 % with a bias of −1 to 1 %. In general, our result suggests that snow densities measured by different methods agree within 9 %. However, the density profiles resolved by the measurement methods differed considerably. In particular, the millimeter-scale density variations revealed by the high-resolution µCT contrasted the thick layers with sharp boundaries introduced by the observer. In this respect, the unresolved variation, i.e., the density variation within a layer which is lost by lower resolution sampling or layer aggregation, is critical when snow density measurements are used in numerical simulations.

Annual Greenland accumulation rates (2009–2012) from airborne snow radar

“…Our assigned uncertainty in the top meter is the relative standard deviation in observed density (12 %), which we assume is due to the natural variability in surface density. This uncertainty is higher than the assumed mean measurement uncertainty of 2-5 % (Proksch et al, 2016) and smaller than the mean difference between the modeled and observed values within the top meter (16 %). No spatial bias is evident between the mean model used here in the top 1 m of snow/firn and the observed density.…”

Snow Precipitation Measured by Gauges: Systematic Error Estimation and Data Series Correction in the Central Italian Alps

“…They showed a 7% coefficient of variation of 92 SWE estimates conducted with combined depth and density measurements. This is a result of a 1 cm standard uncertainty of snow depth measurements and a typical 5% standard relative uncertainty of gravimetric snow density measurements with density cutters and portable dynamometers in the field [22]. Rain gauge types are: manual rain gauge with 1000 cm 2 hole and melting snow operation made at the measurement time (P1); manual rain gauge (1000 cm 2 ) in a heated housing SIAP model (P2); mechanical tipping-bucket recording gauge (1000 cm 2 ) SIAP or Salmoiraghi in a heated housing SIAP model (P3); CAE PMB2 electronic tipping-bucket recording heated gauge (1000 cm 2 ) (P4).…”

Continuous monitoring of snowpack dynamics in alpine terrain by aboveground neutron sensing

“…Traditional snow sampling techniques (snow pits, snow core surveys) [ Goodison et al ., ; Kinar and Pomeroy , ; Proksch et al ., ] are invasive, labor intensive, and suffer from several drawbacks like small support, large spacing and/or low repeat frequency. A number of automated in situ measurements for snow depth (SD), snow water equivalent (SWE), or liquid water content exist [ Johnson and Marks , ; Stähli et al ., ; Egli et al ., ; Lundberg et al ., ; Koch et al ., ] but are usually limited to a very small support with a high sensitivity to local anomalies.…”