scholarly journals 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

2022 ◽  
Vol 16 (1) ◽  
pp. 43-59
Author(s):  
Christopher Donahue ◽  
S. McKenzie Skiles ◽  
Kevin Hammonds

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.

2021 ◽  
Author(s):  
Christopher Donahue ◽  
S. McKenzie Skiles ◽  
Kevin Hammonds

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.


1994 ◽  
Vol 19 ◽  
pp. 92-96 ◽  
Author(s):  
TH. Achammer ◽  
A. Denoth

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.


2019 ◽  
Vol 55 (5) ◽  
pp. 4465-4487 ◽  
Author(s):  
Franziska Koch ◽  
Patrick Henkel ◽  
Florian Appel ◽  
Lino Schmid ◽  
Heike Bach ◽  
...  

2020 ◽  
Author(s):  
Konstantinos Doulgeris ◽  
David Brus

<p>Clouds and their interaction with aerosols are considered one of the major factors that are connected with uncertainties in predictions of climate change and are highly associated with earth radiative balance. Semi long term in-situ measurements of Arctic low-level clouds have been conducted during last 10 year (2009 - 2019) autumns at Sammaltunturi station (67◦58´N, 24◦07´E, and 560 m a.s.l.), the part of Pallas Atmosphere - Ecosystem Supersite and Global Atmosphere Watch (GAW) programme. During these years a unique data set of continuous and detailed ground-based cloud observations over the sub-Arctic area was obtained. The in-situ cloud measurements were made using two cloud probes that were installed on the roof of the station: the Cloud, Aerosol and Precipitation Spectrometer probe (CAPS) and the Forward Scattering Spectrometer Probe<strong> (</strong>FSSP<strong>)</strong>, both made by droplet measurement technologies (DMT, Longmont, CO, USA). CAPS in­cludes three instruments: the Cloud Imaging Probe (CIP, 12.5 μm-1.55 mm), the Cloud and Aerosol Spectrometer (CAS-DPOL, 0.51-50 μm) with depolarization feature and the Hotwire Liquid Water Content Sensor (Hotwire LWC, 0 - 3 g/m<sup>3</sup>). Vaisala FD12P weather sensor was used to measure all the meteorological data. The essential cloud microphysical parameters we investigated during this work were the size distributions, the total number concentrations, the effective radius of cloud droplets and the cloud liquid water content. The year to year comparison and correlations among semi long term in situ cloud measurements and meteorology are presented.</p>


2020 ◽  
Vol 10 (16) ◽  
pp. 5407
Author(s):  
Jamie Heil ◽  
Behrouz Mohammadian ◽  
Mehdi Sarayloo ◽  
Kevin Bruns ◽  
Hossein Sojoudi

Understanding the mechanisms of snow adhesion to surfaces and its subsequent shedding provides means to search for active and passive methods to mitigate the issues caused by snow accumulation on surfaces. Here, a novel setup is presented to measure the adhesion strength of snow to various surfaces without altering its properties (i.e., liquid water content (LWC) and/or density) during the measurements and to study snow shedding mechanisms. In this setup, a sensor is utilized to ensure constant temperature and liquid water content of snow on test substrates, unlike inclined or centrifugal snow adhesion testing. A snow gun consisting of an internal mixing chamber and ball valves for adjusting air and water flow is designed to form snow with controlled LWC inside a walk-in freezing room with controlled temperatures. We report that snow adheres to surfaces strongly when the LWC is around 20%. We also show that on smooth (i.e., RMS roughness of less than 7.17 μm) and very rough (i.e., RMS roughness of greater than 308.33 μm) surfaces, snow experiences minimal contact with the surface, resulting in low adhesion strength of snow. At the intermediate surface roughness (i.e., RMS of 50 μm with a surface temperature of 0 °C, the contact area between the snow and the surface increases, leading to increased adhesion strength of snow to the substrate. It is also found that an increase in the polar surface energy significantly increases the adhesion strength of wet snow while adhesion strength decreases with an increase in dispersive surface energy. Finally, we show that during shedding, snow experiences complete sliding, compression, or a combination of the two behaviors depending on surface temperature and LWC of the snow. The results of this study suggest pathways for designing surfaces that might reduce snow adhesion strength and facilitate its shedding.


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