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Quantitative interpretation of SMP signals

Quantitative interpretation of SMP signals. H.P. Marshall M. Schneebeli J. Johnson. BSU, CRREL. SLF. CRREL. Snow Characterization Workshop, April 13-15, 2009. Emperical Relationships. Textural Index [Schneebeli, Pielmeier, Johnson, 1999, CRST]. TI=1.45+5.72 CV.

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Quantitative interpretation of SMP signals

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  1. Quantitative interpretation of SMP signals H.P. Marshall M. Schneebeli J. Johnson BSU, CRREL SLF CRREL Snow Characterization Workshop, April 13-15, 2009

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

  3. SMP hardness shows good agreement hand hardness profiles • Serial section shows similar boundaries and texture index trend makes sense

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

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

  6. parameterization of thermal conductivity using penetration hardness

  7. summit snow profile - top 0.5 m density shape size NIP SnowMicroPen

  8. Summit Temperature 100 mm depth 300 mm depth Temperature measured Temperature simulated 1 mm layer resolution Temperature simulated 100 mm layer resolution

  9. Summit temperature simulation simulation layer thickness 100 mm 1 mm

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

  11. But SMP has detailed microstructural signal

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

  13. Snow under rapid loading behaves nearly linear elastically

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

  15. SnowMicroPenetrometer (SMP) • Micro-scale measurements (resolution = 0.004 mm) • Deflection and rupture of individual elements measured (Johnson and Schneebeli, 1999)

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

  17. Multiple structural elements simultaneously engaged with SMP tip

  18. Simulated signal shows similar structure to field measurements

  19. Retrieval of microstructural properties

  20. Micromechanical properties • Mechanical properties are very sensitive to errors in basic microstructural properties

  21. Improvement to physical theory • Removed assumption of uniform random distribution of elements [Sturm et al, Manali, 2004]

  22. Use typical parameters, generate Monte-Carlo, check results

  23. Used Monte-Carlo to simulate signals, applied theory, and made modifications to improve accuracy • Overlapping ruptures • Solve exactly for delta • Remove increase in force during rupture (digitization) • Include all force values in calculation

  24. Correction for overlapping ruptures

  25. Accuracy of retrieving L

  26. Accuracy of retrieving f

  27. Accuracy of retrieving delta

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

  29. Resulting microstructural parameters are sensitive to snow type

  30. Application to 4 snow types

  31. Application to 8 snow types

  32. Basic statistics

  33. Emperical Models

  34. Basic microstructural parameters

  35. Derived micromechanical parameters

  36. Scaling to macroscale

  37. Scaling Elastic Modulus

  38. Scaling Compressive strength

  39. Macro scale mechanical properties important for modeling stress on slope

  40. Comparison SMP and traditional stability tests [Pielmeier and Marshall, ISSW 2008]

  41. SMP profile near failure interface

  42. Classification of stability based on SMP analysis 88% total accuracy, 87% stable accuracy, 89% unstable accuracy

  43. Accuracy of classification

  44. Testing changes in strength with increased load [Lutz et al, ISSW 2008]

  45. Strength estimates agree with stability tests • Decrease in strength with increasing load

  46. Conclusions • Major sources of error in micro-mechanical analysis corrected • Retrieval of parameters from simulated signals accurate over wide range of parameters • Analysis applied to field studies show stability can be classified based on parameters with 88% accuracy • Provides new rapid method for studying spatial variability of snowpack stability

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