Quantitative interpretation of SMP signals

1. Quantitative interpretation of SMP signals H.P. Marshall M. Schneebeli J. Johnson INSTAAR, CRREL SLF CRREL Snow Characterization Workshop, March 18-20, 2008

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?] [Marshall, 2005] Rho=55.6 * ln(mR)+317.4 [kg/m^3]

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

15. 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 prep, Cryosphere]

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 and micromechanical properties • [Johnson and Schneebeli, 99, CRST] • L,F,delta, k, microscale stress/strain at rupture, microscale elastic modulus • {derivation on board}

20. Improvement to physical theory • Removed assumption of uniform random distribution of elements

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

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

23. Correction for overlapping ruptures

24. Accuracy of retrieving L

25. Accuracy of retrieving f

26. Accuracy of retrieving delta

27. 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

28. Resulting microstructural parameters are sensitive to snow type

29. Application to 4 snow types

30. Application to 8 snow types

31. Basic statistics

32. Emperical Models

33. Basic microstructural parameters

34. Derived micromechanical parameters

35. Scaling to macroscale

36. Scaling Elastic Modulus

37. Scaling Compressive strength

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