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The influence of spatial variability of polar firn on microwave emission
Martin Proksch1, Henning Löwe1, Stefanie Weissbach2, Martin Schneebeli1
1 WSL-Institute for Snow- und Avalanche Research SLF, Davos, CH2 Alfred-Wegener-Institute for Polar and Marine Research, Germany
Microsnow Reading, 6. – 8. August 2014
Outline
1. Motivation
2. Instrument and measurements
3. Simulations and Results
– Spatial variability– Layer thickness
4. Conclusions
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1. Motivation I
• Microwave observations are essential in polar regions (think about polar night!)
• To understand the microwave signatures of polar firn, in-situ data is necessary, but traditional snow measurements are:– limited in spatial resolution– limited by extensive measurement times – constrained due to harsh polar environments– subjective (variability between observers)
• Desirable: fast derivation of the relevant objective parameters with sufficient resolution (e.g. Correlation length and density to model microwave emission)
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1. Motivation II • Where to measure (Sampling design)?
• Answer requires knowledge about snow variability!
Pic: Martin Schneebeli
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2.1 Instrument: SnowMicroPen (SMP) Specifications:
- High resolution: vertical ~1mm
- Fast: 1 m profile ~ 1 minute– Portable=> Ideal for spatial variability
Output:– Density, SSA and Correlation
length (Proksch et al, submitted)
– 2D stratigraphy from transects
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2.2 Measurements at Kohnen Station:Density
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92 SMP profiles with interval 0.5 m -> 45m transect:
2.2 Measurements at Kohnen Station:Correlation length lex
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92 SMP profiles with interval 0.5 m -> 45m transect:
2.2 Measurements at Kohnen Station:specific surface area SSA
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92 SMP profiles with interval 0.5 m -> 45m transect:
3.1 MEMLS simulations
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MEMLS: Microwave Emission Model of Layered Snowpacks, Wiesmann and Mätzler, 1999.
-> with Improved Born Approximation, Mätzler 1998.
MEMLS input: • 1cm layer thickness in top most meter• lex: SMP (no «grain size» scaling)• Density: SMP• Snow temperature profile• Tsky: 0K• Snow-ground reflectivity: 0• 20m deep profile, linearly increasing
3.2 Results: Brightness temperatures
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σ Tb
Tb
3.2 Results: Brightness temperatures
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σ Tb
Tb
3.2 Results: Brightness temperatures
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One MEMLS run per SMP profile, total N = 92
σ(Tb, 36GHz) = 16.6 K
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σ Tb
Tb
3.2 Results: Brightness temperatures One MEMLS run
per SMP profile, total N = 92
σ(Tb, 36GHz) = 16.6 K
To decrease σ, we have to increase the number of measurements N:
σ(Tb) = 16 K for N=92
σ(Tb) = 8 K for N = 368
σ(Tb) = 2 K for N = 2944
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σ Tb
Tb
3.2 Results: Summit
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Standard deviations:
• T19GHz, V-pol = 13.9 K
• T36GHz, V-pol = 24.1 K
• T89GHz, V-pol = 23.5 K
Constant Density: Constant corr. length
• T19GHz, V-pol = 13.5 K T19GHz, V-pol = 3.7 K T36GHz, V-pol = 26.1 K T36GHz, V-pol = 3.8 K T89GHz, V-pol = 27.8 K T89GHz, V-pol = 7.0 K
3.2 Results: Point Barnola
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Standard deviations:
• T19GHz, V-pol = 3.3 K
• T36GHz, V-pol = 11.0 K
• T89GHz, V-pol = 21.2 K
Constant Density: Constant corr. length
• T19GHz, V-pol = 4.5 K T19GHz, V-pol = 1.2 K T36GHz, V-pol = 12.8 K T36GHz, V-pol = 1.5 K T89GHz, V-pol = 23.7 KT89GHz, V-pol = 4.3 K
3.3 Results: Spatial correlations
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3.3 Results: Spatial correlations
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3.3 Results: Spatial correlations
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3.3 Results: Spatial correlations
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3.3 Results: Spatial correlations
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3.3 Results: Spatial correlations
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3.3 Results: Spatial correlations
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3.4 Results: Layer thickness
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• 20m deep profile: – First meter SMP measurement– 2 – 20 meter: linear increasing, with random noise added.
3 cm
20 cm
3.4 Results: Effect of vertical averaging
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Averaging to 3cm layer thickness leads to significant loss of density variations!
4. Summary and Conclusions
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One single profile is not enough – statistically based sampling design?
Layer thickness critical
The SnowMicroPen allows the measurement of full-meter profiles in less than one minute
Transects reveals the 2D quantitative stratigraphy of polar firn
o Outlook: optimize deep profiles to match Satellite data
s
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Thank you!
Thanks to:Christian MätzlerLudovic Brucker
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3.5 Results: Measurement accuracy
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• Meas. accuracy in top most meter
• To model Tb within 1K
Outlook
• Compare to SSMI
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To do:
• Spat var - for other stations
• Layer thickness
• Meas accuracy
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3.2 Results: Spatial correlations
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3.2 Results: Spatial correlations
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3.2 Results: Spatial correlations
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