Towards a Unified Model for Wet Snow Permittivity

The development of a unified model for wet snow permittivity has remained a persistent challenge in remote sensing applications. While research conducted in the 1980s and 1990s yielded permittivity models for dry and wet snow and facilitated the development of in-situ snow probes, the application of these models in practical contexts, particularly across a broad frequency spectrum, remains an area requiring further investigation. The absence of a universally accepted model for wet snow impedes accurate retrievals of essential snow properties, including density, snow height, and liquid water content (LWC), from ground-based, drone-based, and satellite radar observations. This result in inconsistencies among LWC measurements from the different systems and retrieval methods.

The primary impediment to progress in this area is the limited availability of comprehensive reference datasets encompassing simultaneous measurements of permittivity, LWC, density, and a diverse range of snow conditions. The traditional method for LWC determination, employing freezing calorimetry, offers high accuracy under controlled conditions and with skilled operators but is characterized by a time-intensive measurement process, thereby limiting the feasibility of extensive data acquisition.

This study undertakes a re-evaluation of existing field campaign data concerning wet snow permittivity at a wide range of frequencies, considering the diverse acquisition methodologies employed and their associated limitations. By critically appraising the underlying assumptions and limitations of existing permittivity models, we seek to reconcile observed discrepancies. The ultimate objective of this research is to formulate recommendations for future field campaigns, emphasizing enhanced data quality and the resolution of existing knowledge gaps that currently limit the development of robust wet snow permittivity models across a broad frequency range spanning from the MHz to tens of GHz.

Through systematic analysis and the identification of critical knowledge gaps, this investigation will contribute to the advancement of a unified understanding of wet snow permittivity, with the potential to significantly enhance the accuracy of snow property retrievals derived from remote sensing observations.