A physical SNOWPACK model for the Swiss avalanche warning Part III: meteorological forcing, thin layer formation and evaluation

By M. Lehning, P. Bartelt, B. Brown, C. Fierz. 2002

560 citations recorded by the source corpus.

Abstract The development of the seasonal snow cover is entirely driven by atmospheric forcing. SNOWPACK uses measured snow depths to determine snow precipitation rates via the calculated settling rates. This requires a rigid data control algorithm. A new statistical model is used to estimate fresh snow density as a function of the measured atmospheric conditions. A statistical model is also derived for the snow albedo, which is necessary to determine the absorbed radiation. The surface sensible and latent heat flux parameterizations are derived from Monin–Obukhov similarity and include a formulation for wind pumping. The formulations will also adapt to drifting snow conditions. The new suggestion is consistent with the observation of different roughness lengths for scalars and momentum over snow. An accurate formulation, especially for the latent heat exchange, is crucial because latent heat exchange determines the formation of surface hoar, a very important weak layer. We also account for the effect of wind pumping on the thermal conductivity in the uppermost snow layers. The surface energy and mass exchange formulations are evaluated by looking at the formation of the important thin layers surface hoar and melt–freeze crusts in SNOWPACK. Those layers are well simulated. In addition, the complete snow profile development is modeled successfully for the parameters grain type, temperature, density, grain size and liquid water content. An overall score between 0 and 1 is used to describe the profile agreement with observations and an average score of over 0.8 is reached.

Subfields: Atmospheric Science, Global and Planetary Change, Management, Monitoring, Policy and Law

View Paper: https://www.semanticscholar.org/paper/a5b5cb94de148709f62141025a7cb1ad50c59802

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

  1. Ola
  2. Rachel
  3. Persson
  4. Peter
  5. Jordan
  6. Andrey
  7. Grachev
  8. Guest

Citations in the corpus