1. 1 MICROSNOW Aug 2014 Sensitivity of simulated brightness temperatures to multiple grain size measurement techniques Simulated Tb from multiple grain sizes Nick Rutter, Mel Sandells nick.rutter@northumbria.ac.uk (with Laurent Arnaud, Charles Fierz, McKenzie Skiles, Lena Leppänen, Fabian Wolfsperger, Martin Proksch, plus other grain size working group intercomparison participants)

2. 2 MICROSNOW Aug 2014 Passive microwave Only current operational satellite sensors for snow and 30+ year legacy Optimal configurations of active microwave satellites for snow may be some way off (CoReH20) Satellites providing a stand alone product? Global: AMSR-E snow depth 22 cm RMSE, from ~250 WMO stations (Kelly 2009) Geographically specific improvements: Tundra (Derksen et al. 2010) Satellites as part of data assimilation product e.g. GlobSnow? 1-layer emission model (HUT), recognizing grain size as the most sensitive parameter HUT inverts satellite Tb and known depth to estimate grain size -> grain size spatially interpolated to produce Tb estimates -> minimization of cost function -> maps of assimilated SWE Current lack of faith in snow depth & mass products by other communities, e.g. not used in LSM benchmarking of NCAR models (David Lawrence, GWEX 2014) Can reduction in uncertainty of snow microstructure measurements improve evaluation of snow emission models? Rationale

3. 3 MICROSNOW Aug 2014 Aim: quantify variability in brightness temperatures (Tb) simulated by the HUT- ML microwave emission model using different grain size measurements Unprecedented opportunity to get a wide range of snow grain size values of the same snowpack using different measurement methods ~19 different instruments participated in the workshop, some with more than one grain size metric. By July 2014 seven QC’d data sets were available from six instruments: Experimental overview : Measurements Visual: TRAD (Fierz) Optical: FCP (Skiles) SSA: ASP (Arnaud), FCP (Skiles), ICE (Leppanen), INF (Wolfsperger), SMP (Proksch).

4. 4 MICROSNOW Aug 2014 Aim: quantify variability in brightness temperatures (Tb) simulated by the HUT- ML microwave emission model using different grain size measurements Unprecedented opportunity to get a wide range of snow grain size values of the same snowpack using different measurement methods ~19 different instruments participated in the workshop, some with more than one grain size metric. By July 2014 seven QC’d data sets were available from six instruments: Experimental overview : Measurements Visual: TRAD (Fierz) Optical: FCP (Skiles) SSA: ASP (Arnaud), FCP (Skiles), ICE (Leppanen), INF (Wolfsperger), SMP (Proksch).

5. 5 MICROSNOW Aug 2014 Aim: quantify variability in brightness temperatures (Tb) simulated by the HUT- ML microwave emission model using different grain size measurements Unprecedented opportunity to get a wide range of snow grain size values of the same snowpack using different measurement methods ~19 different instruments participated in the workshop, some with more than one grain size metric. By July 2014 seven QC’d data sets were available from six instruments: Experimental overview : Measurements Visual: TRAD (Fierz) Optical: FCP (Skiles) SSA: ASP (Arnaud), FCP (Skiles), ICE (Leppanen), INF (Wolfsperger), SMP (Proksch).

6. 6 MICROSNOW Aug 2014 Aim: quantify variability in brightness temperatures (Tb) simulated by the HUT- ML microwave emission model using different grain size measurements Unprecedented opportunity to get a wide range of snow grain size values of the same snowpack using different measurement methods ~19 different instruments participated in the workshop, some with more than one grain size metric. By July 2014 seven QC’d data sets were available from six instruments: Experimental overview : Measurements Visual: TRAD (Fierz) Optical: FCP (Skiles) SSA: ASP (Arnaud), FCP (Skiles), ICE (Leppanen), INF (Wolfsperger), SMP (Proksch).

7. 7 MICROSNOW Aug 2014 Aim: quantify variability in brightness temperatures (Tb) simulated by the HUT- ML microwave emission model using different grain size measurements Unprecedented opportunity to get a wide range of snow grain size values of the same snowpack using different measurement methods ~19 different instruments participated in the workshop, some with more than one grain size metric. By July 2014 seven QC’d data sets were available from six instruments: Experimental overview : Measurements Visual: TRAD (Fierz) Optical: FCP (Skiles) SSA: ASP (Arnaud), FCP (Skiles), ICE (Leppanen), INF (Wolfsperger), SMP (Proksch).

8. 8 MICROSNOW Aug 2014 Aim: quantify variability in brightness temperatures (Tb) simulated by the HUT- ML microwave emission model using different grain size measurements Unprecedented opportunity to get a wide range of snow grain size values of the same snowpack using different measurement methods ~19 different instruments participated in the workshop, some with more than one grain size metric. By July 2014 seven QC’d data sets were available from six instruments: Experimental overview : Measurements Visual: TRAD (Fierz) Optical: FCP (Skiles) SSA: ASP (Arnaud), FCP (Skiles), ICE (Leppanen), INF (Wolfsperger), SMP (Proksch).

9. 9 MICROSNOW Aug 2014 Experimental methods: Measurements Model inputs ( 1-D profiles of stratigraphy, density, temperature and ‘grain size’) Stratigraphy, layer identification (ice, crusts, grain type) in pit MA1 (Fierz), Density measurements (n = 3) so….used density in pit MA3 (< 2 m away), which had 3 cm vertical resolution using 100 cm^3 cutter (Proksch) (n = 44) Temperature (2 to 10 cm vertical resolution) Substrate was a frozen clay tennis court

10. 10 MICROSNOW Aug 2014 WSL Institute for Snow and Avalanche Research SLF Thanks to Charles Fierz

11. 11 MICROSNOW Aug 2014 Data: Density profiles

12. 12 MICROSNOW Aug 2014 Experimental methods: HUT-ML emission model Grain size measurements were averaged within stratigraphic layers Cubic interpolation used to determine temperature and grain size for thin layers Model parameters: Roughness = 0, epsilon = 6-j; Angle = 53 deg, Freq/Pol: 19V 19H 37V 37H Effective grain diameter determine from visual / optical and SSA: Visual (d obs) and optical to effective grain size (Deff): following Kontu and Pulliainen (2010) SSA (per mass of ice) to effective grain size following Gallet et al. (2009) and Montpetit et al. (2012): SMP derives SSA from correlation length (p c ) following Debye et al. (1957):

13. 13 MICROSNOW Aug 2014 Experimental methods: HUT-ML emission model For each combination of frequency and polarisation: three extinction coefficients: Hallikainen et al. (1987), from snow in southern Finland: Kontu and Pulliainen (2010), optimized for deeper and denser snow with larger grain sizes than taiga snow: Roy et al. (2004), to account for multiple scattering by densely packed ice particles where γ and δ are 2 ± 1 and 0.20 ± 0.04 respectively:

14. 14 MICROSNOW Aug 2014 Results Visual (TRAD) lowest Tb SSA reasonably similar with exception of SMP ( a bit lower) ICE and ASP very similar (should be as same techniques) Optical FCS similar to SSA FCS

15. 15 MICROSNOW Aug 2014 Conclusions Quantified spread in Tb from a snow emission model from eight independent grain size measurements (a first with this many simultaneous measurements?) Improvement on sensitivity analysis using synthetic grain size data (constrains relative grain size differences between stratigraphic layers) Despite looking at the same snow in the most controlled circumstances as possible, measurements techniques propagate a 6-82 K spread in estimated Tb With the caveat that is just a quick pilot study which needs more thorough work, there is clearly a challenge ahead. How might a follow-up experiment meet this challenge? Results & Conclusion Subset19V19H37V37H All39308262 just SSA24184836 just SSA without SMP761713

16. 16 MICROSNOW Aug 2014 Need to know more about the substrate Completely frozen? Percentage interstitial liquid water content? Soil texture? Organics? Groups currently working on this for soil moisture and freeze-thaw More snowpack heteorgeneity? St Mortiz homogeneity ideal for measurement inter-comparison (tennis court, wind-sheltered) Lacking in snow grain types (e.g. depth hoar and wind slab) that are representative of shallow sub-Arctic to high-Arctic snowpacks (< 120-150 mm SWE saturation levels) More applicable to satellite remote sensing & assimilation products? Can we conduct a similar experiment to St Moritz in Arctic or sub-Arctic snowpacks ? Future experiment?

17. 17 MICROSNOW Aug 2014 Future experiment? Capability: independent Tb measurements for model evaluation using portable ground-based (sled-mounted) radiometers There’s extra capacity within working group to provide redundancy

18. 18 MICROSNOW Aug 2014 Rutter et al. (2014) Capability: identify spatial variability in stratigraphic layer boundaries at sub-cm resolution in shallow (< 1m) snowpacks Future experiment? Domine et al. (2012)

19. 19 MICROSNOW Aug 2014 Capability: spatially interpolate 1-D profiles of measurements throughout horizontal layers Future experiment? Watts et al. (in prep.)

20. 20 MICROSNOW Aug 2014 Future experiment? Large 50 m extents: Lengths scales of layer thickness & layer properties, Roughness of layer boundaries, Distributions of layer properties (tails!) as model inputs.

21. 21 MICROSNOW Aug 2014 Physical snow model with full range of snow process representations (e.g. Essery et al., 2013) Multiple grain size measurement techniques (X-ray tomography, plus multiple SSA, Pc, Optical) Seasonal grain size evaluation data Emission model interchangeability incorporating easy: Conversion between grain size metrics (SSA, Pc, Dobs, Opt) Field data & snow model output to emission model initialization (HUT, DMRT, MEMLS) Long term, high quality meteorological data & logistical support in Arctic environment with cost-efficient accessibility Goal: to allow all modelling groups to evaluate uncertainties, algorithms and process representations in the estimation of Tb Wish list

22. 22 MICROSNOW Aug 2014 Extra Slides

23. 23 MICROSNOW Aug 2014 Differences in Tb using Fierz low resolution densities (n=3) and Proksch high resolution densities (n=44)

24. 24 MICROSNOW Aug 2014

25. 25 MICROSNOW Aug 2014 Data: Snowpit measurements