In Situ Determination of Dry and Wet Snow Permittivity: Snow Water
Equivalent Algorithm Development for Low Frequency Radar Applications
Abstract
Extensive efforts are made to observe the accumulation and melting of
seasonal snow. However, making accurate observations of snow water
equivalent (SWE) at the global scale remains elusive. Active radar
systems show promise, provided the dielectric properties of the snowpack
are accurately understood. The dielectric relative permittivity (k)
determines the velocity of the radar wave through snow. Equations used
to estimate k have been validated only for specific conditions with
limited in situ validation for seasonal snow applications. The goal of
this work is to further understand the dielectric permittivity of
seasonal snow under dry and wet conditions. We utilize extensive in situ
observations of k with snow density and liquid water content (LWC)
observations to: (1) Test current permittivity equations for dry snow
conditions, (2) Test current permittivity equations for wet snow
conditions, and (3) Determine if any improvements to current
permittivity equations are necessary. Data were collected in the Jemez
Mountains, NM; Sandia Mountains, NM; Grand Mesa, CO; and Cameron Pass,
CO from February 2020 to May 2021. We will present empirical
relationships based on 146 snow pits for dry snow conditions and 92 LWC
observations in naturally melting snowpacks. Regression results have
r2 values of 0.57 and 0.37 for dry and wet snow
conditions, respectively. Our results in dry snow showed large
differences between our in situ observations and commonly applied
equations. We attribute these differences to assumptions in shape of the
snow grains that may not hold true for seasonal snow applications.
Different assumptions, and thus different equations, may be necessary
for varying snowpack conditions in different climates, though further
testing is necessary. When considering wet snow, large differences were
found between commonly applied equations and our in situ testing. Many
previous equations assume a background (dry) k that we found to be
inaccurate, as previously stated, that is the primary driver of
resulting uncertainty. Our results suggest large errors in SWE or LWC
estimates based on current equations. The work presented here could
prove useful for making accurate observations of changes in SWE using
future remote sensing opportunities such as NISAR and ROSE-L.