English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Density is a fundamental property of porous media such assnow. A wide range of snow properties and physical processes arelinked to density, but few studies have addressed the uncertainty insnow density measurements. No study has yet quantitatively considered the recent advances insnow measurement methods such as micro-computed tomography (μCT) in alpine snow. During the MicroSnow Davos 2014 workshop, differentapproaches to measure snow density were applied in a controlledlaboratory environment and in the field. Overall, the agreementbetween μCT and gravimetric methods (density cutters) was 5 to9 %, with a bias of −5 to 2 %, expressed aspercentage of the mean μCT density.In the field, density cutters overestimate(1 to 6 %) densities below and underestimate (1 to 6 %) densities above athreshold between 296 to 350 kg m−3, dependent on cutter type.Using the mean density per layer of all measurement methods applied inthe field (μCT, box, wedge, and cylinder cutters) and ignoring icelayers, the variation between the methods was 2 to5 % with a bias of −1 to 1 %. In general, ourresult suggests that snow densities measured by different methodsagree within 9 %. However, the density profiles resolved bythe measurement methods differed considerably. In particular, themillimeter-scale density variations revealed by the high-resolutionμCT contrasted the thick layers with sharp boundaries introduced bythe observer. In this respect, the unresolved variation, i.e., thedensity variation within a layer which is lost by lower resolution sampling or layeraggregation, is critical when snow density measurements are used in numericalsimulations

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