Snow dielectric properties: from DC to microwave X-band

Broadband measurements of dielectric properties of natural snow samples near or at 0°C are reported. Measurement quantities are: dielectric permittivity, loss factor and complex propagation factor for electromagnetic waves. X-band measurements were made in a cold room in the laboratory; measurements at low and intermediate frequencies were carried out both in the field (Stubai Alps, 3300 m; Hafelekar near Innsbruck, 2100 m) and in the cold room. Results show that in the different frequency ranges the relative effect on snow dielectric properties of the parameters: density, grain-size and shape, liquid water content, shape and distribution of liquid inclusions and content of impurities, varies significantly. In the low-frequency range the influence of grain-size and shape and snow density dominates; in the medium-frequency range liquid water content and density are the dominant parameters. In the microwave X-band the influence of the amount, shape and distribution of liquid inclusions and snow density is more important than that of the remaining parameters.

Download Full-text

A Microwave Frequency Range Experiment for the Measurement of Snow Density and Liquid Water Content

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2021.3123785 ◽

2021 ◽

pp. 1-1

Author(s):

Cedric Bermond ◽

Philippe Artillan ◽

Michel Gay

Keyword(s):

Water Content ◽

Liquid Water ◽

Microwave Frequency ◽

Liquid Water Content ◽

Frequency Range ◽

Snow Density

Download Full-text

Evaluating the effect of grain size and salts on liquid water content in frozen soils of Antarctica by combining NMR, chemical equilibrium modeling, and scattered diffraction analysis

Geoderma ◽

10.1016/j.geoderma.2017.03.024 ◽

2017 ◽

Vol 299 ◽

pp. 25-31 ◽

Cited By ~ 3

Author(s):

Liliya Vugmeyster ◽

Birgit Hagedorn ◽

Matthew A. Clark ◽

R.S. Sletten

Keyword(s):

Grain Size ◽

Diffraction Analysis ◽

Water Content ◽

Liquid Water ◽

Chemical Equilibrium ◽

Liquid Water Content ◽

Frozen Soils ◽

Equilibrium Modeling ◽

Chemical Equilibrium Modeling

Download Full-text

Spatiotemporal Variations in Liquid Water Content in a Seasonal Snowpack: Implications for Radar Remote Sensing

Remote Sensing ◽

10.3390/rs13214223 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4223

Author(s):

Randall Bonnell ◽

Daniel McGrath ◽

Keith Williams ◽

Ryan Webb ◽

Steven R. Fassnacht ◽

...

Keyword(s):

Water Content ◽

Liquid Water ◽

Electromagnetic Waves ◽

Snow Water Equivalent ◽

Canopy Cover ◽

Liquid Water Content ◽

Spatiotemporal Variability ◽

Snow Density ◽

Wet Snow ◽

Observation Method

Radar instruments have been widely used to measure snow water equivalent (SWE) and Interferometric Synthetic Aperture Radar is a promising approach for doing so from spaceborne platforms. Electromagnetic waves propagate through the snowpack at a velocity determined by its dielectric permittivity. Velocity estimates are a significant source of uncertainty in radar SWE retrievals, especially in wet snow. In dry snow, velocity can be calculated from relations between permittivity and snow density. However, wet snow velocity is a function of both snow density and liquid water content (LWC); the latter exhibits high spatiotemporal variability, there is no standard observation method, and it is not typically measured by automated stations. In this study, we used ground-penetrating radar (GPR), probed snow depths, and measured in situ vertically-averaged density to estimate SWE and bulk LWC for seven survey dates at Cameron Pass, Colorado (~3120 m) from April to June 2019. During this cooler than average season, median LWC for individual survey dates never exceeded 7 vol. %. However, in June, LWC values greater than 10 vol. % were observed in isolated areas where the ground and the base of the snowpack were saturated and therefore inhibited further meltwater output. LWC development was modulated by canopy cover and meltwater drainage was influenced by ground slope. We generated synthetic SWE retrievals that resemble the planned footprint of the NASA-ISRO L-band InSAR satellite (NISAR) from GPR using a dry snow density model. Synthetic SWE retrievals overestimated observed SWE by as much as 40% during the melt season due to the presence of LWC. Our findings emphasize the importance of considering LWC variability in order to fully realize the potential of future spaceborne radar missions for measuring SWE.

Download Full-text

X-Band Attenuation and Liquid Water Content Estimation by a Dual-Wavelength Radar

Journal of Applied Meteorology ◽

10.1175/1520-0450(1971)010<1252:xbaalw>2.0.co;2 ◽

1971 ◽

Vol 10 (6) ◽

pp. 1252-1259 ◽

Cited By ~ 49

Author(s):

Peter J. Eccles ◽

Eugene A. Mueller

Keyword(s):

Water Content ◽

Liquid Water ◽

Liquid Water Content ◽

Dual Wavelength ◽

X Band

Download Full-text

Liquid-water content and water distribution of wet snow using electrical monitoring

10.5194/tc-2020-56 ◽

2020 ◽

Author(s):

Pirmin Philipp Ebner ◽

Aaron Coulin ◽

Joël Borner ◽

Fabian Wolfsperger ◽

Michael Hohl ◽

...

Keyword(s):

Dielectric Properties ◽

Water Content ◽

Liquid Water ◽

Water Distribution ◽

Liquid Water Content ◽

Heating Process ◽

Snow Sample ◽

Starting Point ◽

Water Percolation ◽

Wet Snow

Abstract. Snow exists in a wide range of temperatures and around its melting point snow becomes a three-phase material. A better understanding of wet snow and the first starting point of water percolation in the seasonal snowpack is essential for snow pack stability, snow melt run-off and remote sensing. In order to induce and measure precisely the liquid water and the corresponding dielectric properties inside a snow sample, an experimental setup was developed. Using microwave heating at 18 kHz allows the use of dielectric properties of ice to enable heat to be dissipated homogeneously through the entire volume of snow. A desired liquid water content inside the snow sample could then be created and analysed in a micro-computer tomography. Based on the electrical monitoring a promising perspective for retrieving water content and water distribution in the snowpack is given. The heating process and extraction of water content are mainly dependent on the morphological properties of snow, the temperature and the liquid water content. The experimental observation can be divided in three different heating processes affecting the dielectric properties of snow for different densities: (1) dry snow heating process up to 0 °C indicating a temperature and snow structure dependency of the dielectric property of snow; (2) wet snow heating at stagnating temperature of 0 °C and the presence of uniformed distributed liquid water changes the dielectric properties. The presence of liquid water decreases the impedance of the snow sample until water starts to percolate; and (3) the start of water percolation is between 5–12 water volume fraction depending on the snow density and confirms the literature findings. The onset of water percolation initiated an inhomogeneity in snow and water distribution, strongly affecting the dielectric properties of the snow. These findings are pertinent to the interpretation of the snow melt run-off of spring snow. These laboratory measurements allow to find the narrow range of the starting point of water percolation in coarse-grained snow and to extract the corresponding dielectric properties which is important for remote sensing.

Download Full-text

Imminent Nowcasting for Severe Rainfall Using Vertically Integrated Liquid Water Content Derived from X-Band Polarimetric Radar

Journal of the Meteorological Society of Japan Ser II ◽

10.2151/jmsj.2018-028 ◽

2018 ◽

Vol 96A (0) ◽

pp. 201-220 ◽

Cited By ~ 4

Author(s):

Kohin HIRANO ◽

Masayuki MAKI

Keyword(s):

Water Content ◽

Liquid Water ◽

Liquid Water Content ◽

Polarimetric Radar ◽

X Band

Download Full-text

Mapping liquid water content in snow: An intercomparison of mixed-phase optical property models using hyperspectral imaging and in situ measurements

10.5194/tc-2021-247 ◽

2021 ◽

Author(s):

Christopher Donahue ◽

S. McKenzie Skiles ◽

Kevin Hammonds

Keyword(s):

Grain Size ◽

Optical Property ◽

Water Content ◽

Hyperspectral Imaging ◽

Liquid Water ◽

Liquid Water Content ◽

Mixed Phase ◽

Macro Scale ◽

Wet Snow

Abstract. It is well understood that the distribution and quantity of liquid water in snow is relevant for snow hydrology and avalanche forecasting, yet detecting and quantifying liquid water in snow remains a challenge from the micro- to the macro-scale. Using near-infrared (NIR) spectral reflectance measurements, previous case studies have demonstrated the capability to retrieve surface liquid water content (LWC) of wet snow by leveraging shifts in the complex refractive index between ice and water. However, different models to represent mixed-phase optical properties have been proposed, including (1) internally mixed ice and water spheres, (2) internally mixed water coated ice spheres, and (3) externally mixed interstitial ice and water spheres. Here, from within a controlled laboratory environment, we determine the optimal mixed-phase optical property model for simulating wet snow reflectance using a combination of NIR hyperspectral imaging, radiative transfer simulations (DISORT), and an independent dielectric LWC measurement (SLF Snow Sensor). Maps of LWC were produced by finding the least residual between measured reflectance and simulated reflectance in spectral libraries, generated for each model with varying LWC and grain size, and assessed against the in situ LWC sensor. Our results show that the externally mixed model performed the best, retrieving LWC with an uncertainty of ~1 %, while the simultaneously retrieved grain size better represented wet snow relative to the established scaled band area method. Furthermore, the LWC retrieval method was demonstrated in the field, imaging a snowpit sidewall during melt conditions, mapping pooling water, flow features, and LWC distribution in unprecedented detail.

Download Full-text

Mapping liquid water content in snow at the millimeter scale: an intercomparison of mixed-phase optical property models using hyperspectral imaging and in situ measurements

The Cryosphere ◽

10.5194/tc-16-43-2022 ◽

2022 ◽

Vol 16 (1) ◽

pp. 43-59

Author(s):

Christopher Donahue ◽

S. McKenzie Skiles ◽

Kevin Hammonds

Keyword(s):

Grain Size ◽

Optical Property ◽

Radiative Transfer ◽

Water Content ◽

Hyperspectral Imaging ◽

Liquid Water ◽

Liquid Water Content ◽

Mixed Phase ◽

Wet Snow

Abstract. It is well understood that the distribution and quantity of liquid water in snow is relevant for snow hydrology and avalanche forecasting, yet detecting and quantifying liquid water in snow remains a challenge from the micro- to the macro-scale. Using near-infrared (NIR) spectral reflectance measurements, previous case studies have demonstrated the capability to retrieve surface liquid water content (LWC) of wet snow by leveraging shifts in the complex refractive index between ice and water. However, different models to represent mixed-phase optical properties have been proposed, including (1) internally mixed ice and water spheres, (2) internally mixed water-coated ice spheres, and (3) externally mixed interstitial ice and water spheres. Here, from within a controlled laboratory environment, we determined the optimal mixed-phase optical property model for simulating wet snow reflectance using a combination of NIR hyperspectral imaging, radiative transfer simulations (Discrete Ordinate Radiative Transfer model, DISORT), and an independent dielectric LWC measurement (SLF Snow Sensor). Maps of LWC were produced by finding the lowest residual between measured reflectance and simulated reflectance in spectral libraries, generated for each model with varying LWC and grain size, and assessed against the in situ LWC sensor. Our results show that the externally mixed model performed the best, retrieving LWC with an uncertainty of ∼1 %, while the simultaneously retrieved grain size better represented wet snow relative to the established scaled band area method. Furthermore, the LWC retrieval method was demonstrated in the field by imaging a snowpit sidewall during melt conditions and mapping LWC distribution in unprecedented detail, allowing for visualization of pooling water and flow features.

Download Full-text