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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