sensitivity of simulated brightness temperatures to multiple grain size measurement techniques

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 [email protected]

(with Laurent Arnaud, Charles Fierz, McKenzie Skiles, Lena Leppänen, Fabian Wolfsperger, Martin Proksch, plus other grain size working

group intercomparison participants)


• 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


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).











• SSA:

• ASP (Arnaud),

• FCP (Skiles),

• ICE (Leppanen),


• SMP (Proksch).

9 MICROSNOW Aug 2014Experimental 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 MICROSNOW Aug 2014WSL Institute for Snow and Avalanche Research SLF

Thanks to Charles Fierz

11 MICROSNOW Aug 2014Data: Density profiles

12 MICROSNOW Aug 2014Experimental 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 (pc) following Debye et al. (1957):

13 MICROSNOW Aug 2014Experimental 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 MICROSNOW Aug 2014Results

• 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


• 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

Subset 19V 19H 37V 37HAll 39 30 82 62

just SSA 24 18 48 36just SSA without SMP 7 6 17 13


• 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 MICROSNOW Aug 2014Future 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


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)


Capability: spatially interpolate 1-D profiles of measurements throughout

horizontal layers

Future experiment?

Watts et al. (in prep.)

20 MICROSNOW Aug 2014Future experiment?

Large 50 m extents:

• Lengths scales of layer thickness & layer properties,

• Roughness of layer boundaries,

• Distributions of layer properties (tails!) as model inputs.


• 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


Extra Slides


• Differences in Tb using Fierz low resolution densities (n=3) and Proksch high resolution densities (n=44)

DifferencesExtinction coeffi cient 19V_all_Deff 19H_all_Deff 37V_all_Deff 37H_all_Deff0 = Hallikainen et al. 1 6 -1 21 = Roy et al. 1 5 -1 32 = metu 1 5 -1 2

mean of differences (all extinction coeffs)1 5 -1 3

25 MICROSNOW Aug 2014Data: Snowpit measurements

sensitivity of simulated brightness temperatures to multiple grain size measurement techniques

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