Quantitative interpretation of SMP signals - CGISS


Quantitative interpretation of SMP signals - CGISS

Quantitative interpretation ofSMP signalsH.P. MarshallBSU, CRRELM. SchneebeliSLFJ. JohnsonCRRELSnow Characterization Workshop, April 13-15, 2009

Emperical Relationships• Textural Index [Schneebeli, Pielmeier,Johnson, 1999, CRST]TI=1.45+5.72 CV

SMP hardnessshows goodagreement handhardness profiles• Serial sectionshows similarboundaries andtexture indextrend makessense

Emperical Relationships• Density [Pielmeier, 2003; Stahli et al, J Glac, 2003]Rho=55.6 * ln(mR)+317.4 [kg/m^3][Marshall, 2005]

Emperical RelationshipsThermal conductivity [Stahli et al, J Glac, 2003; Dadic,Schneebeli, Lehning, Hutterli, Ohmura, in press JGR ]

parameterization of thermal conductivityusing penetration hardness

summit snow profile - top 0.5 mSnowMicroPenshapesizedensityNIP

Summit Temperature100 mm depth 300 mm depthTemperature measuredTemperature simulated 1 mm layer resolutionTemperature simulated 100 mm layer resolution

Summit temperature simulationsimulationlayer thickness100 mm1 mm

Hardness analysis• Spatial variability [e.g. Kronholm,2003,…]• Temporal variability [Birkeland et al,2004, Annals…]• Weak layer thickness [Lutz et al, 2005,CRST]

But SMP has detailedmicrostructural signal

Similar features can be found in nearby profiles,and coincide with layer boundaries from manualprofiles and radar measurements[Marshall, Schneebeli, Koh, 2007, CRST]

Snow under rapid loadingbehaves nearly linear elastically

Mechanical Properties• Physics-based model [Schneebeli &Johnson, 98, Annals; Johnson andSchneebeli, 99, CRST]• Further improvements [Sturm et al, 04(Manali); Marshall and Johnson, in review,JGR]

Basic structural element[Johnson and Schneebeli, 99, CRST]

Multiple structural elementssimultaneously engaged with SMP tip

Simulated signal shows similarstructure to field measurements

Retrieval of microstructural andmicromechanical properties• [Johnson and Schneebeli, 99, CRST]• L,F,delta, k, microscale stress/strain atrupture, microscale elastic modulus• {derivation on board}

Improvement to physical theory• Removed assumption of uniformrandom distribution of elements[Sturm et al, 2004]

Use typical parameters, generateMonte-Carlo, check results

Isolated sources of error, andmade modifications• Overlapping ruptures• Solve exactly for delta• Remove increase in force during rupture(digitization)• Include all force values in calculation

Correction for overlapping ruptures

Accuracy of retrieving L

Accuracy of retrieving f

Accuracy of retrieving delta

Real data is noisy, includes forcevariations not due to ruptures• Rupture force threshold [Johnson and Schneebeli, 99]• Rupture slope threshold [Kronholm et al]• Air signals typically have ruptures ~0.01N

Resulting microstructural parameters aresensitive to snow type

Application to 4 snow types

Application to 8 snow types

Basic statistics

Emperical Models

Basic microstructural parameters

Derived micromechanical parameters

Scaling to macroscale

Scaling Elastic Modulus

Scaling Compressive strength

Macro scale mechanicalproperties important formodeling stress on slope

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