1 / 34

The influence of spatial variability of polar firn on microwave emission

The influence of spatial variability of polar firn on microwave emission. 1 WSL-Institute for Snow- und Avalanche Research SLF, Davos, CH. Martin Proksch 1 , Henning Löwe 1 , Stefanie Weissbach 2 , Martin Schneebeli 1. 2 Alfred-Wegener-Institute for Polar and Marine Research, Germany.

stu
Download Presentation

The influence of spatial variability of polar firn on microwave emission

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. The influence of spatial variability of polar firn on microwave emission • 1 WSL-Institute for Snow- und Avalanche Research SLF, Davos, CH Martin Proksch1, Henning Löwe1, Stefanie Weissbach2, Martin Schneebeli1 • 2Alfred-Wegener-Institute for Polar and Marine Research, Germany • Microsnow Reading, 6. – 8. August 2014

  2. Outline • Motivation • Instrument and measurements • Simulations and Results • Spatial variability • Layer thickness • Conclusions WSL-Institut für Schnee- und Lawinenforschung SLF

  3. 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) WSL-Institut für Schnee- und Lawinenforschung SLF

  4. 1. Motivation II • Where to measure (Sampling design)? • Answer requires knowledge about snow variability! Pic: Martin Schneebeli WSL-Institut für Schnee- und Lawinenforschung SLF

  5. 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

  6. 2.2 Measurements at Kohnen Station: Density 92 SMP profiles with interval 0.5 m -> 45m transect: WSL-Institut für Schnee- und Lawinenforschung SLF

  7. 2.2 Measurements at Kohnen Station: Correlation length lex 92 SMP profiles with interval 0.5 m -> 45m transect: WSL-Institut für Schnee- und Lawinenforschung SLF

  8. 2.2 Measurements at Kohnen Station: specific surface area SSA 92 SMP profiles with interval 0.5 m -> 45m transect: WSL-Institut für Schnee- und Lawinenforschung SLF

  9. 3.1 MEMLS simulations MEMLS: Microwave Emission Model ofLayeredSnowpacks, Wiesmann andMätzler, 1999. -> withImproved Born Approximation, Mätzler 1998. MEMLS input: • 1cm layerthickness in top mostmeter • lex: SMP (no «grainsize» scaling) • Density: SMP • Snow temperatureprofile • Tsky: 0K • Snow-groundreflectivity: 0 • 20m deepprofile, linearlyincreasing WSL-Institut für Schnee- und Lawinenforschung SLF

  10. 3.2 Results: Brightnesstemperatures σ Tb Tb WSL-Institut für Schnee- und Lawinenforschung SLF

  11. 3.2 Results: Brightnesstemperatures σ Tb Tb WSL-Institut für Schnee- und Lawinenforschung SLF

  12. 3.2 Results: Brightnesstemperatures • One MEMLS run per SMP profile, total N = 92 • σ(Tb, 36GHz) = 16.6 K σ Tb Tb WSL-Institut für Schnee- und Lawinenforschung SLF WSL-Institut für Schnee- und Lawinenforschung SLF 14

  13. 3.2 Results: Brightnesstemperatures • One MEMLS run per SMP profile, total N = 92 • σ(Tb, 36GHz) = 16.6 K • Todecreaseσ, wehavetoincreasethenumberofmeasurements N: • σ(Tb) = 16 K • for N=92 • σ(Tb) = 8K • for N = 368 • σ(Tb) = 2K • for N = 2944 σ Tb Tb WSL-Institut für Schnee- und Lawinenforschung SLF

  14. 3.2 Results: Summit 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

  15. 3.2 Results: Point Barnola 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 K T89GHz, V-pol = 4.3 K WSL-Institut für Schnee- und Lawinenforschung SLF

  16. 3.3 Results: Spatialcorrelations WSL-Institut für Schnee- und Lawinenforschung SLF

  17. 3.3 Results: Spatialcorrelations WSL-Institut für Schnee- und Lawinenforschung SLF

  18. 3.3 Results: Spatialcorrelations WSL-Institut für Schnee- und Lawinenforschung SLF

  19. 3.3 Results: Spatialcorrelations WSL-Institut für Schnee- und Lawinenforschung SLF

  20. 3.3 Results: Spatialcorrelations WSL-Institut für Schnee- und Lawinenforschung SLF

  21. 3.3 Results: Spatialcorrelations WSL-Institut für Schnee- und Lawinenforschung SLF

  22. 3.3 Results: Spatialcorrelations WSL-Institut für Schnee- und Lawinenforschung SLF

  23. 3.4 Results: Layer thickness • 20m deep profile: • First meter SMP measurement • 2 – 20 meter: linear increasing, with random noise added. 20 cm 3 cm WSL-Institut für Schnee- und Lawinenforschung SLF

  24. 3.4 Results: Effectofverticalaveraging Averaging to 3cm layer thickness leads to significant loss of density variations! WSL-Institut für Schnee- und Lawinenforschung SLF

  25. 4. Summary and Conclusions • The SnowMicroPen allows the measurement of full-meter profiles in less than one minute • Transects reveals the 2D quantitative stratigraphy of polar firn • One single profile is not enough – statistically based sampling design? • Layer thickness critical • Outlook: optimize deep profiles to match Satellite data WSL-Institut für Schnee- und Lawinenforschung SLF

  26. s Thankyou! • Thanksto: • Christian Mätzler • Ludovic Brucker WSL-Institut für Schnee- und Lawinenforschung SLF

  27. WSL-Institut für Schnee- und Lawinenforschung SLF

  28. WSL-Institut für Schnee- und Lawinenforschung SLF

  29. 3.5 Results: Measurement accuracy • Meas. accuracy in top most meter • To model Tb within 1K WSL-Institut für Schnee- und Lawinenforschung SLF

  30. Outlook • Compareto SSMI WSL Institute for Snow and Avalanche Research SLF

  31. To do: • Spat var - forotherstations • Layer thickness • Measaccuracy WSL Institute for Snow and Avalanche Research SLF

  32. 3.2 Results: Spatialcorrelations WSL-Institut für Schnee- und Lawinenforschung SLF

  33. 3.2 Results: Spatialcorrelations WSL-Institut für Schnee- und Lawinenforschung SLF

  34. 3.2 Results: Spatialcorrelations WSL-Institut für Schnee- und Lawinenforschung SLF

More Related