The Microwave Snow Grain Size: A New Concept to Predict Satellite Observations Over Snow-Covered Regions

Satellite observations of snow-covered regions in the microwave range have the potential to retrieve essential climate variables such as snow height. This requires a precise understanding of how microwave scattering is linked to snow microstructural properties (density, grain size, grain shape and arrangement). This link has so far relied on empirical adjustments of the theories, precluding the development of robust retrieval algorithms. Here we solve this problem by introducing a new microstructural parameter able to consistently predict scattering. This microwave grain size is demonstrated to be proportional to the measurable optical grain size and to a new factor describing the chord length dispersion in the microstructure, a geometrical property known as polydispersity. By assuming that the polydispersity depends on the snow grain type only, we retrieve its value for rounded and faceted grains by optimization of microwave satellite observations in 18 Antarctic sites, and for depth hoar in 86 Canadian sites using ground-based observations. The value for the convex grains (0.6) compares favorably to the polydispersity calculated from 3D micro-computed tomography images for alpine grains, while values for depth hoar show wider variations (1.2-1.9) and are larger in Canada than in the Alps. Nevertheless, using one value for each grain type, the microwave observations in Antarctica and in Canada can be simulated from in-situ measurements with good accuracy with a fully physical model. These findings improve snow scattering modeling, enabling future more accurate uses of satellite observations in snow hydrological and meteorological applications.

Automated summary

This study improves the snow scattering model by introducing a new microstructural parameter that accurately predicts microwave scattering. It demonstrates the relationship between microwave grain size, optical grain size, and polydispersity, and retrieves the value of polydispersity for different types of snow grains. The findings enable more accurate uses of satellite observations in snow hydrological and meteorological applications.