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Experimental investigation of snow metamorphism at near-surface layers
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Fluid and Experimental Mechanics.ORCID iD: 0000-0002-5943-1476
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Snow metamorphism is a direct objective in many snow research areas, and its charactization is a major challenge in areas including winter road maintenance, detection of icing on wind turbine blades and snow quality mapping for skiing. The snow metamorphism in response to level and type of compaction associated with variations in physical properties of snow. While the compaction influence on snow metamorphism in relation to snow physical properties is fairly known, the relation between the compaction and physical properties of snow is not extensively understood. This experimental based thesis study presents three different approaches to investigate the snow metamorphism in response to compaction. Considered snow types during the thesis, experienced compaction via aging, melting-freezing process, applying uniaxial load, settling and introducing liquid water. In addition, the influence of snow compaction at near-surface layers is investigated using optical techniques.

In the first approach, near-infrared (NIR) light scattering from snow surfaces is numerically modelled based on the radiative transfer theory. The model used in this approach, was a special case of widely used Discrete Ordinates Radiative Transfer (DISORT) method that solves the radiative transfer equation for a plane-parallel and semi-infinite snowpack. This numerical methodology leads to a Legendre scattering phase function, where the link between the Legendre coefficients and physical properties of snow in relation to compaction is investigated. The observations in this approach show the consistency and strong correlation between the Legendre coefficients and physical properties of snow. The second approach of the thesis, was focused on statistical analysis of NIR reflectance measurements from snow surfaces for the determination of snow properties. In this approach, physical properties of snow such as specific surface area (SSA) and surface liquid water content (LWC) are quantified via parameterization where the reflectance data is used as input. In both of these approaches, compaction of a snowpack is investigated and understood based on grain size distribution, surface texture, snow temperature and scattering behaviour of NIR radiation. As a next step, the snow compaction is investigated on a microscopic level where a X-ray microtomography (micro-CT) was used to model the three dimensional (3D) microstructure of snow. In this approach, evolution and deformation of snow grains during compaction via applying uniaxial load is investigated. In addition, snow compaction is understood based on digital volume correlation (DVC) and porosity analysis.

In summary, the thesis presents an experimental method to quantify the link between the snow compaction at near-surface layers and physical properties of snow. The model observations show that the estimated Legendre coefficients can provide qualitative description of snow grain distribution and surface texture. The parameterization observations show that the SSA of snow and snow surface LWC can be quantified from snow reflectance measurements. The observations from the micro-CT study show the influence of compaction at a granular level. The observations presented in the thesis, are helpful to understand the metamorphism in a snowpack for relevant research areas.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2018.
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Keyword [en]
Snow metamorphism; NIR reflectance; Snow and Ice; Radiative transfer equation; Paramterization; Snow properties; Tomography; Snow microstructure
National Category
Other Materials Engineering Applied Mechanics
Research subject
Experimental Mechanics
Identifiers
URN: urn:nbn:se:ltu:diva-68394ISBN: 978-91-7790-116-7 (print)ISBN: 978-91-7790-117-4 (electronic)OAI: oai:DiVA.org:ltu-68394DiVA, id: diva2:1198568
Public defence
2018-06-18, E231, Luleå University of Technology, Luleå, 15:48 (English)
Opponent
Supervisors
Available from: 2018-04-18 Created: 2018-04-18 Last updated: 2018-04-18Bibliographically approved
List of papers
1. Estimation of a low-order Legendre expanded phase function of snow
Open this publication in new window or tab >>Estimation of a low-order Legendre expanded phase function of snow
2016 (English)In: Optics and lasers in engineering, ISSN 0143-8166, E-ISSN 1873-0302, Vol. 78, p. 174-181Article in journal (Refereed) Published
Abstract [en]

The purpose of this paper is to estimate the scattering phase function of snow from angularly resolved measurements of light intensity in the plane of incidence. A solver is implemented that solves the scattering function for a semi-infinite geometry based on the radiative transfer equation (RTE). Two types of phase functions are considered. The first type is the general phase function based on a low-order series expansion of Legendre polynomials and the other type is the Henyey-Greenstein (HG) phase function. The measurements were performed at a wavelength of 1310 nm and six different snow samples were analysed. It was found that a first order expansion provides sufficient approximation to the measurements. The fit from the first order phase function outperforms that of the HG phase function in terms of accuracy, ease of implementation and computation time. Furthermore, a correlation between the magnitude of the first order component and the age of the snow was found. We believe that these findings may complement present non-contact detection techniques used to determine snow properties.

National Category
Applied Mechanics
Research subject
Experimental Mechanics
Identifiers
urn:nbn:se:ltu:diva-4108 (URN)10.1016/j.optlaseng.2015.10.013 (DOI)1fb86369-88ce-4293-89e3-fae4460cfe1f (Local ID)1fb86369-88ce-4293-89e3-fae4460cfe1f (Archive number)1fb86369-88ce-4293-89e3-fae4460cfe1f (OAI)
Note
Validerad; 2015; Nivå 2; 20151028 (lavepp)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-04-18Bibliographically approved
2. Investigation of single scattering properties of snow based on first order Legendre phase function
Open this publication in new window or tab >>Investigation of single scattering properties of snow based on first order Legendre phase function
2017 (English)In: Optics and lasers in engineering, ISSN 0143-8166, E-ISSN 1873-0302, Vol. 91, p. 151-159Article in journal (Refereed) Published
Abstract [en]

Angularly resolved bidirectional reflectance measurements were modelled by ap- proximating a first order Legendre expanded phase function to retrieve single scattering properties of snow. The measurements from 10 different snow types with known density and specific surface area (SSA) were investigated. A near infrared (NIR) spectrometer was used to measure reflected light above the snow surface over the hemisphere in the wavelength region 900 nm to 1650 nm. A solver based on discrete ordinate radiative transfer (DISORT) model was used to retrieve the estimated Legendre coefficients of the phase function and a cor- relation between the coefficients and physical properties of different snow types is investigated. Results of this study suggest that the first two coefficients of the first order Legendre phase function provide sufficient information about the physical properties of snow where the latter captures the anisotropic behaviour of snow and the former provides a relative estimate of the single scattering albedo of snow. The coefficients of the first order phase function were com- pared with the experimental data and observed that both the coefficients are in good agreement with the experimental data. These findings suggest that our approach can be applied as a qualitative tool to investigate physical properties of snow and also to classify different snow types.

Keyword
Radiative transfer model, Scattering phase function, Snow, SSA, Reflectance spectrum, NIR spectrometer
National Category
Applied Mechanics
Research subject
Experimental Mechanics
Identifiers
urn:nbn:se:ltu:diva-60532 (URN)10.1016/j.optlaseng.2016.11.013 (DOI)000393264300016 ()2-s2.0-84998694302 (Scopus ID)
Note

Validerad; 2017; Nivå 2; 2016-12-19 (andbra)

Available from: 2016-11-18 Created: 2016-11-18 Last updated: 2018-04-18Bibliographically approved
3. Estimation of Liquid Water Content of Snow Surface by Spectral Reflectance
Open this publication in new window or tab >>Estimation of Liquid Water Content of Snow Surface by Spectral Reflectance
2018 (English)In: Journal of cold regions engineering, ISSN 0887-381X, E-ISSN 1943-5495, Vol. 32, no 1, article id 05018001Article in journal (Refereed) Published
Abstract [en]

This study measures the spectral reflectance from snow with known liquid water content (LWC) in a climate chamber using two optical sensors, a near-infrared (NIR) spectrometer and a Road eye sensor. The spectrometer measures the backscattered radiation in the wavelength range of 920–1,650 nm. The Road eye sensor was developed to monitor and classify winter roads based on reflected intensity measurements at wavelengths of 980, 1,310, and 1,550 nm. Results of the study suggest that the spectral reflectance from snow is inversely proportional to the LWC in snow. Based on the effect of LWC on the spectral reflectance, three optimum wavelength bands are selected in which snow with different LWCs is clearly distinguishable. A widely used remote sensing index known as the normalized difference water index (NDWI) is used to develop a method to estimate the surface LWC for a given snow pack. The derived NDWI values with respect to the known LWC in snow show that the NDWI is sensitive to the LWC in snow and that the NDWI and LWC are directly proportional. Based on this information, the NDWI is used to estimate the surface LWC in snow from measurements on a ski track using the Road eye sensor. The findings suggest that the presented method can be applied to estimate the surface LWC in order to classify snow conditions potentially for ski track and piste applications.

Place, publisher, year, edition, pages
American Society of Civil Engineers (ASCE), 2018
National Category
Oceanography, Hydrology and Water Resources Applied Mechanics Geotechnical Engineering
Research subject
Experimental Mechanics; Soil Mechanics
Identifiers
urn:nbn:se:ltu:diva-65754 (URN)10.1061/(ASCE)CR.1943-5495.0000158 (DOI)000428257200013 ()2-s2.0-85040254798 (Scopus ID)
Note

Validerad;2018;Nivå 2;2018-02-05 (rokbeg)

Available from: 2017-09-21 Created: 2017-09-21 Last updated: 2018-04-25Bibliographically approved
4. Estimation of specific surface area of snow based on density and multispectral infrared reflectance
Open this publication in new window or tab >>Estimation of specific surface area of snow based on density and multispectral infrared reflectance
(English)In: Journal of cold regions engineering, ISSN 0887-381X, E-ISSN 1943-5495Article in journal (Refereed) Submitted
Abstract [en]

This paper presents a multiple regression method for predicting snow SSA based on multispectral reflectance and snow density. Multispectral near-IR reflectance from snow was measured at wavelengths 980 nm, 1,310 nm and 1,550 nm. In total, 16 different artificially prepared snow samples were investigated using two optical sensors, a spectrometer and a Road eye sensor. Both the sensors measured backscattered radiance from snow and measurements were carried out in a climate chamber.  Snow types with variations in physical properties such as grain distribution, surface texture, SSA, density and depth are considered. Variations in snow density were obtained through compaction and aging process. Correlation between the snow density and reflectance is investigated and influence of snow density and multispectral reflectance on snow SSA is also investigated. A generalized linear model is developed to predict the snow SSA with a coefficient of determination equal to 98\%. The preliminary validation of results show that the SSA can be accurately estimated from the density and multispectral reflectance. The model results indicate that the snow density has minor effect on the variations in snow SSA. Results suggest that snow with varying physical properties can be qualitatively characterized based on the presented approach, which is of interest for applications such as winter roads classification and pistes classification.  

Keyword
Snow reflectance; Specific surface area; snow density; Parameterization; Spectrometer; Road eye
National Category
Other Materials Engineering
Research subject
Experimental Mechanics
Identifiers
urn:nbn:se:ltu:diva-68398 (URN)
Available from: 2018-04-17 Created: 2018-04-17 Last updated: 2018-04-25
5. 3D analysis of deformation and porosity of dry natural snow during compaction
Open this publication in new window or tab >>3D analysis of deformation and porosity of dry natural snow during compaction
(English)In: Journal of materials science, ISSN 1996-1944Article in journal (Refereed) Submitted
Abstract [en]

The presented study focuses on three-dimensional (3D) microstructure analysis of dry natural snow during compaction. The X-ray computed microtomography (micro-CT) system was used to record 1601 projections of the snow volume. Experiments were performed at four in-situ load conditions as 0 N, 10 N, 18 N and 25 N, to investigate the effects of compaction on structural features of snow grains. The micro-CT system produces high resolution images i.e. 4.3 μm per voxel in 6 hours of scanning time and the equipment was in a cold room at -15◦c. The micro-CT images of the snow illustrate that the grain shapes are mostly dominated by needles, capped columns and dendrites. It was found that the majority of grains appeared to have a deep hallow core irrespective of the grain shape. A digital volume correlation (DVC) method was applied to investigate the displacement fields in the snow volume due to compaction. Results from DVC analysis show that grains close to the punch experience most of the deformation. The reconstructed snow volume is divided vertically into several sections to study the effect of compaction on porosity. It was observed that the porosity (for the whole volume) in principle decreases as the level of compaction increases, however, no clear correlation is found between compaction and porosity with respect to the individual sections. The observations in this work provide a valuable analysis to maximize the understanding of snow microstructure during compaction.

Keyword
Tomography; snow grains; snow microstructure; Digital volume correlation; Porosity
National Category
Other Materials Engineering
Research subject
Experimental Mechanics
Identifiers
urn:nbn:se:ltu:diva-68399 (URN)
Available from: 2018-04-18 Created: 2018-04-18 Last updated: 2018-04-18

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