Comparison of traditional and optical grain-size field measurements with SNOWPACK simulations in a taiga snowpack

Abstract

Knowledge of snow microstructure is relevant for modelling the physical properties of snow cover and for simulating the propagation of electromagnetic waves in remote-sensing applications. Characterization of the microstructure in field conditions is, however, a challenging task due to the complex, sintered and variable nature of natural snow cover. A traditional measure applied as a proxy of snow microstructure, which can also be determined in field conditions, is the visually estimated snow grain size. Developing techniques also allow measurement, for example, of the specific surface area (SSA) of snow, from which the optical-equivalent grain size can be derived. The physical snow model SNOWPACK simulates evolution of snow parameters from meteorological forcing data. In this study we compare an extensive experimental dataset of measurements of traditional grain size and SSA-derived optical grain size with SNOWPACK simulations of grain-size parameters. On average, a scaling factor of 1.2 is required to match traditional grain-size observations with the corresponding SNOWPACK simulation; a scaling factor of 2.1 was required for the optical equivalent grain size. Standard deviations of scaling factors for the winters of 2011/12 and 2012/13 were 0.36 and 0.42, respectively. The largest scaling factor was needed in early winter and under melting conditions.

Sodankylä manual snow survey program

“…Advantages of post-processed grain size related to direct visual estimation of grain size and grain type in the field are the possibility of having a more accurate image than the with bare eye or magnifying lens, and the repeatability of the estimation. The measurement errors are described more closely by Leppänen et al (2015).…”

“…Successful calibration measurements are also needed for accurate results. A further discussion of error sources is presented by Leppänen et al (2015).…”

“…Therefore, stratigraphy is defined by one person for a snow pit. The discussion of error sources is presented by Leppänen et al (2015).…”

“…Leppänen et al (2015) compared manual snow microstructure measurements to a snow model. Snow pit data were used in the development and validation of a new method to measure SWE by differential interferometry (Leinss et al, 2015) and a new ground-snow radiative transfer model at L-band .…”

Nordic Snow Radar Experiment

“…The problem is aggravated by snow particles of increasing size and complex shape, as well as by sintering and clustering of snow grains. However, using data collected during NoSREx, Leppänen et al (2015) demonstrated that visually established grain sizes E correlated with optical grain sizes measured using an objective measure of SSA. Furthermore, Lemmetyinen et al (2015) showed that an average grain size used to fit emission model predictions captured both the magnitude and the seasonal trend of the visually estimated grain sizes during NoSREx-II.…”