Evaluation of wet snow dielectric mixing models for L-band radiometric measurement of liquid water content in Greenland's percolation zone
Journal Article
Overview
abstract
Abstract. The effective permittivity of wet snow and firn links the snow microphysics to its radiometric signature, making it essential for accurately estimating the liquid water amount (LWA) in the snowpack. Here, we compare ten commonly used microwave dielectric mixing models for estimating LWA in wet snow and firn using L-band radiometry. We specifically focus on the percolation zone of the Greenland Ice Sheet (GrIS), where the average volume fraction of liquid water is between 0 % and 6 %. We used L-band brightness temperature (TB) observations from the NASA Soil Moisture Active Passive (SMAP) mission in an inversion-based framework to estimate LWA, applying different dielectric mixing formulations in the forward simulation. We compared the effective permittivities of the mixing models over a range of conditions and evaluated their impact on the LWA retrieval. We also compared the LWA retrievals to the corresponding LWA from two state-of-the-art Surface Energy and Mass Balance (SEMB) models. Both SEMB models were forced with in situ measurements from automatic weather stations (AWS) of the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) and Greenland Climate Network (GC-Net) located in the percolation zone of the GrIS and initialized with relevant in situ profiles of density, stratigraphy, and sub-surface temperature measurements. The results show that the mixing models produce substantially different real and imaginary parts of the dielectric constant, significantly impacting the LWA retrieved from the TB. The correspondence with the SEMB-derived LWA varied by model and site, with correlation coefficients ranging from 0.67 to 0.98 and RMSD values between 5.4 and 23.9 mm. Overall, the power law-based empirical models demonstrated better performance for 2023 melt season. The analysis supports informed selection of dielectric mixing models for improved LWA retrieval accuracy.
