Fractional snow-covered area: scale-independent peak of winter parameterization

Abstract

Abstract. The spatial distribution of snow in the mountains is significantly influenced through interactions of topography with wind, precipitation, shortwave and longwave radiation, and avalanches that may relocate the accumulated snow. One of the most crucial model parameters for various applications such as weather forecasts, climate predictions and hydrological modeling is the fraction of the ground surface that is covered by snow, also called fractional snow-covered area (fSCA). While previous subgrid parameterizations for the spatial snow depth distribution and fSCA work well, performances were scale-dependent. Here, we were able to confirm a previously established empirical relationship of peak of winter parameterization for the standard deviation of snow depth σHS by evaluating it with 11 spatial snow depth data sets from 7 different geographic regions and snow climates with resolutions ranging from 0.1 to 3 m. An enhanced performance (mean percentage errors, MPE, decreased by 25 %) across all spatial scales ≥ 200 m was achieved by recalibrating and introducing a scale-dependency in the dominant scaling variables. Scale-dependent MPEs vary between −7 % and 3 % for σHS and between 0 % and 1 % for fSCA. We performed a scale- and region-dependent evaluation of the parameterizations to assess the potential performances with independent data sets. This evaluation revealed that for the majority of the regions, the MPEs mostly lie between ±10 % for σHS and between −1 % and 1.5 % for fSCA. This suggests that the new parameterizations perform similarly well in most geographical regions.

Accounting for Topographic Effects on Snow Cover Fraction and Surface Albedo Simulations Over the Tibetan Plateau in Winter

“…To improve the snow cover simulations, we add the topographic factor to include the topographic effects based on the analysis of satellite observations. In addition to SDtopo, the squared slope has been used to represent subgrid topography in recent research (Helbig, Bühler, et al, 2021;Helbig, Schirmer, et al, 2021;Helbig et al, 2015;Skaugen & Melvold, 2019). Helbig, Bühler, et al (2021) performed a correlation analysis for subgrid SDP variability with various commonly applied topographic characteristics, and a squared slope-related parameter shows a better correlation than SDtopo with subgrid spatial SDP variability.…”

“…In addition to SDtopo, the squared slope has been used to represent subgrid topography in recent research (Helbig, Bühler, et al, 2021;Helbig, Schirmer, et al, 2021;Helbig et al, 2015;Skaugen & Melvold, 2019). Helbig, Bühler, et al (2021) performed a correlation analysis for subgrid SDP variability with various commonly applied topographic characteristics, and a squared slope-related parameter shows a better correlation than SDtopo with subgrid spatial SDP variability. Using the slope-related parameter to represent the subgrid topography is more realistic for snow processes.…”

“…It offers us a new method to account for topographic effects on TP snow cover simulations. The scheme of Helbig, Bühler, et al (2021) was developed and evaluated based on the SDP observations with meter-scale spatial resolution in North America and Europe. However, on the TP, there are only satellite observations of SDP with a spatial resolution of kilometer level and sparse in-situ observations of SDP.…”

Modeling spatially distributed snow instability at a regional scale using Alpine3D

“…2a). This dataset, consisting of gridded snow depth data at 3 m spatial resolution, was described in Helbig and others (2021) and subsets were validated in Mazzotti and others (2019). We eliminated 16% of the grid points, since these were in forests, urban areas and lakes.…”

Snowfall deposition in mountainous terrain: a statistical downscaling scheme from high-resolution model data on simulated topographies

“…The significance of high-resolution snow maps in mountainous areas has led to extensive research in statistically describing spatial snow depth distribution. Empirical relationships have been established with terrain parameters (e.g., Grünewald et al, 2013;Helbig et al, 2015;Skaugen and Melvold, 2019;Helbig et al, 2021) or with horizontal wind speed maps and terrain parameters such as shelter or exposure to prevailing winds (e.g., Purves et al, 1998;Winstral and Marks, 2002). Several snow cover shaping processes can be described simultaneously through these statistical descriptions.…”