Intercomparison of quantification methods for snow microstructure during the SnowAPP experiment

Abstract

Snow microstructure defines the physical, mechanical and electromagnetic properties of snow. Accurate information of snow structure is needed by many applications, including avalanche forecasting (Hirashima et al., 2008) and numerical weather prediction (de Rosnay et al., 2014). The interaction of electromagnetic waves with snow properties can be applied in satellite remote sensing to retrieve, for example, global information of snow mass (Pulliainen et al., 2020). Objective in-situ observations of snow microstructure are needed to validate and develop both physical models and satellite snow retrieval algorithms. Conventional measurements of snow grain size are unsatisfactory in this regard, as the parameter is difficult to measure objectively, and even its definition is ambiguous (Mätzler, 2002). Hence, recent efforts have focused on developing forward models of microwave interactions and snow specific surface area (SSA), which can be objectively measured in field and laboratory conditions using various methods. A recently proposed approach links SSA to microwave scattering properties through another physically defined parameter (Picard et al., 2022).In the SnowAPP project, three field campaigns were carried out at the Finnish Meteorological Institute Arctic Research Centre in Sodankylä, with the goal of collecting data on snow microstructural properties and establishing the relation of microstructure to both optical reflectance and microwave emission and scattering from snow.  During the spring 2019 campaign, six different methods were used for measuring SSA; and several methods were used for measuring snow density, another important factor affecting especially the extinction of microwave energy. Furthermore, multi-frequency radiometry and a wide-band, high resolution spectrometer were used to measure microwave emission and reflectance. In this study, we compare objectively the SSA and density values obtained by the different methods in a round-robin exercise. The relation of measured snow microstructures to measured spectral properties of snow are discussed.SnowAPP was funded by the Academy of Finland, with contributions from WSL Institute for Snow and Avalanche Research SLF, Centre Tecnològic de Telecomunicacions de Catalunya, Beijing Normal University, National Institute for Polar Research, Meteorological Research Institute (Japan), and Université Grenoble Alpes. de Rosnay, P., Balsamo, G., Albergel, C., Muñoz-Sabater, J., & Isaksen, L. (2014). Initialisation of land surface variables for numerical weather prediction. Surveys in Geophysics, 35(3), 607–621. https://doi.org/10.1007/s10712-012-9207-xHirashima, H., Nishimura, K., Yamaguchi, S., Sato, A., & Lehning, M. (2008). Avalanche forecasting in a heavy snowfall area using the snowpack model. Cold Regions Science and Technology, 51(2–3), 191–203. https://doi.org/10.1016/j.coldregions.2007.05.013Mätzler, C., 2002. Relation between grain-size and correlation length of snow. J. Glaciol., (48)162: 461-466.Picard, G., Löwe, H., Domine, F., Arnaud, L., Larue, F., Favier, V., & Meur, E. le. (2022). The Microwave Snow Grain Size: A New Concept to Predict Satellite Observations Over Snow-Covered Regions. https://doi.org/10.1029/2021AV000630Pulliainen, J., Luojus, K., Derksen, C., Mudryk, L., Lemmetyinen, J., Salminen, M., Ikonen, J., Takala, M., Cohen, J., Smolander, T., & Norberg, J. (2020). Patterns and trends of Northern Hemisphere snow mass from 1980 to 2018. Nature, 581(7808), 294–298. https://doi.org/10.1038/s41586-020-2258-0