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Use of SMOS polarization data for retrieval of sea ice thickness and concentration

Georg Heygster, Marcus Huntemann, Huanhuan Wang Institute of Environmental Physics, University of Bremen SMOSIce User Workshop Hamburg, Feb 17, 2011. Use of SMOS polarization data for retrieval of sea ice thickness and concentration. Outline.

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Use of SMOS polarization data for retrieval of sea ice thickness and concentration

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  1. Georg Heygster, Marcus Huntemann, Huanhuan Wang Institute of Environmental Physics, University of Bremen SMOSIce User Workshop Hamburg, Feb 17, 2011 Use of SMOS polarization data for retrieval of sea ice thickness and concentration

  2. Outline • Concept of exploiting SMOS polarization data over sea ice • First glance at polarization data • Concept of thickness retrieval • Thickness retrieval algorithm • Simultaneous retrieval of two sea ice concentration and thickness • Conclusions and comparison intensity-based retrieval vs. polarization-based retrieval

  3. Outline • Concept of exploiting SMOS polarization data over sea ice • First glance at polarization data • Concept of thickness retrieval from polarization data • Thickness retrieval algorithm • Simultaneous retrieval of two sea ice concentration and thickness • Conclusions: Comparison of intensity-based retrieval polarization-based retrieval

  4. Analysis on one day data of 13.Oct.2010 ASI ice concentration difference of 13.10.2010 and 06.10.2010 Open water: 72-73N, 160-165W Thick ice: 80-82N, 120-130W Thin ice: 75-76N, 152-154W SMOS Tbh on 13.10.2010

  5. Analysis on one day data of 13.Oct.2010

  6. 300 200 100 50 Thick ice Thin ice OW Tb / GHz 0.0 60 120 Q = Tbv – Tbh / K Retrieval triangles based on data of 13.Oct.2010 SMOS clearly Distingushes OW against sea ice. Q of thin ice higher than OW

  7. Outline • Concept of exploiting SMOS polarization data over sea ice • First glance at polarization data • Concept of thickness retrieval • Thickness retrieval algorithm • Simultaneous retrieval of two sea ice concentration and thickness • Conclusions: Comparison of intensity-based retrieval polarization-based retrieval

  8. Time series Analysis of 4 points in Arctic The 4 NCEP grid cells: 80° N, 147.5°E 80° N, 85° E 75° N, 67.5°E 72.2°N, 190°E (Kaleschke, 2010)

  9. Cumulative Freezing Degree Days (CFDD) CFDD Tf – sea water freezing point; Ta – air temperature Ice thickness (Maykut 1978, Kaleschke 2010) Calculate CFDD from NCEP air temperatures,adjust t0 to first occurrence of sea ice in ASI sea ice maps Estimate ice thickness from CFDD

  10. 13 K 13 K Retrieval curve for all incidence angles, 10° intervals Tbv: dynamic range decreases with Θ Q: range increases with Θ dynamic range of 30…40 cm thickness Use both to stabilize retrieval 0°-10°-20°-30° -40° -50° -60° >60°

  11. Other channel combinations • I vs. Q TBh vs. Q TBv vs. Q TBv vs. TBh 0°-10°-20°-30° -40° -50° -60° >60°

  12. Outline • Concept of exploiting SMOS polarization data over sea ice • First glance at polarization data • Concept of thickness retrieval from polarization data • Thickness retrieval algorithm • Simultaneous retrieval of two sea ice concentration and thickness • Conclusions: Comparison of intensity-based retrieval polarization-based retrieval

  13. 10 Areas in Kara Sea with model and RS data 1 76.8N 42.5E 2 78.3N 47.4E 3 78.7N 57.4E 4 78.3N 66.6E 5 78.3N 74.8E 6 77.3N 81.7E 7 75.8N 79.5E 8 74.8N 69.2E 9 73.1N 61.7E 10 71.8N 60.7E

  14. Validation data from HIRLAM/HIGHTSI/HIRLAM Model (FMI) • HIGHTSI – High resolution snow and sea ice thermodynamic model • HIRLAM HIgh Resolution Limited Area Model: atmospheric forcing • - Regional Focus on Barents and Kara Seas • MODIS (Moderate-resolution Imaging Spectroradiometer) satellite • and HIRLAM model data used as input for HIGHTSI • No ice drift included

  15. Model data from Kara Sea – Area 5

  16. Synopsis 10 Regions • Sea ice thickness values: • ECMWF always lowest • NECEP highest: regions 2,4,5 • HIGHTSI highest: regions 8, 9 • Both similar regions 3, 6, 7, 10 Used for forward function fit: • Regions A • Additional regions B

  17. Retrieval algorithm based on TB & Q from 3 regions Thickness from NECP CFDDs 40° – 50° a=51.0, b=19.4, c=31.8, d=1.65 a=232.3, b= – 20.6, c=8.8

  18. Sample application to whole Arctic • Used: regions 3, 6, 7 of 10 regions • Oct 13, 2010 • RFI filter: TB>300 K  discard whole shapshot

  19. Ice Thickness Oct – Dec 2010

  20. Example: Comparison with ASI SIC

  21. Example: Comparison with MODIS-SAR SID

  22. Example: scatterplot SMOS SID – MODIS-SAR SID ● ● ●

  23. Example: scatterplot SMOS SID – MODIS-SAR SID ● ● ● Dec 18, 2010

  24. Example: scatterplot SMOS SID – MODIS-SAR SID ● ● Dec 6, 2010

  25. Outline • Concept of exploiting SMOS polarization data over sea ice • First glance at polarization data • Concept of thickness retrieval from polarization data • Thickness retrieval algorithm • Simultaneous retrieval of two sea ice concentration and thickness • Conclusions: Comparison of intensity-based retrieval polarization-based retrieval

  26. Potential to retrieve thickness and concentration 40…50° 280 200 100 280 200 100 • 2 input parameters Tbv, Q: • Chance to retrieve 2 parameters● = 40% ice, 30 cm thick + 60% open water • Requires curved retrieval line  higher incidence angle TBv [K] ● 0 40 80 Q [K] 0 40 80 Q [K]

  27. Potential to retrieve thickness and concentration 40…50° 260 200 80 • 2 input parameters Tbv, Q: • Chance to retrieve 2 parameters:● = 40% ice, 30 cm thick + 60% open water • Requires curved retrieval line  higher incidence angle TB [K] ● 0 50 100 Q [K] 0°-10°-20°-30° -40° -50° -60° >60°

  28. Potential to retrieve thickness and concentration IC = 100% ● d > 50cm ●

  29. Potential to retrieve thickness and concentration IC = 100% d > 50cm

  30. Potential to retrieve thickness and concentration IC = 100% d > 50cm I = 115 K IC = 0%

  31. Ice thickness results 1 P: thickness D 2 P: D and IC 2P115

  32. Ice thickness results 1 P: thickness D TOPAZ 2P115

  33. Ice concentration results • AMSR-E/ASI SMOS-2P SMOS-2P115

  34. Outline • Concept of exploiting SMOS polarization data over sea ice • First glance at polarization data • Concept of thickness retrieval from polarization data • Thickness retrieval algorithm • Simultaneous retrieval of two sea ice concentration and thickness • Conclusions: Comparison of intensity-based retrieval polarization-based retrieval

  35. Incidence angle Resolution • SMOS Snapshot Characteristics

  36. Comparing two approaches for thickness retrieval

  37. Operational aspects • Data availability • L1C 24…48 h now available • BUFR few h future • Question to audience: delivery within few h required?

  38. Conclusions • Polarization signal • present at higher incidence angles; resolution lower • carries SID information up to ~40…50 cm SID • 40°…50° incidence angle ad hoc selected • Intensity vs. polarization difference plane selected • Retrieval plausible, validation ongoing • Construct SID retrieval at varying incidence angles? • Simultaneous retrieval of SIC, SID • possible in principle • Arctic maps plausible • at low SID refinement required Operational aspect: L1C (~1d) or BUFR (few h) ?

  39. Backup slides

  40. Thickness of thin sea ice • Simulations of SMOS (1.4 GHz) observations with sea ice emissivity model MEMLS (Tonboe)

  41. a=51.0 b=19.4 c=31.8 d=1.65 a=232.3 b= – 20.6 c=8.8 Common forward fit for TB & Q a– – b – a (a–b)/2

  42. – b b – b– (a+b)/2 – – a Fit functions to Tbv(CFDD) and Q(CFDD), Q = Tbv – Tbh, 4 points –b b b –

  43. Retrieval curve from combination of Tbv and Q • Retrieval: Find thickness of closest dot • automatically adjusts to parameter of higher sensitivity • stable • error easy to determine from errors in Tbv and Q = Tbv – Tbh ● Black: every 1 cm Red :every 10 cm

  44. Thickness retrieved for 10° incidence angle intervals

  45. TBv, Q TBv, TBh 0..10° ..30° ..50° 70° 0..10° ..30° ..50° ..70° Incidence Angle Incidence Angle Channel combinations: rms thickness vs. incidence angle Retrieval based on I, Q TBh, Q

  46. Channel combinations: rms thickness vs. ice thickness Retrieval based on I, Q TBh, Q TBv, Q TBv, TBh 0..10 ..30 ..50 ..70 ..90 0..10 ..30 ..50 ..70 ..90 Ice Thickness [cm] Ice Thickness [cm]

  47. Conversion antenna frame to Earth reference • SMOS: all 4 components of Stokes vector • L1c data in instrument (x,y)reference frame, documented in Soil Moisture ATBD of 24/01/2010 • Rotation to Earth reference frame (Ev, Eh) by angle α = αr + ωF , αr = -(Φa+ Ψ ) rotation angle ωF : Faraday angle • Transform singular when α≈ 45° • Avoid problem by using full-pol data • Software:Python and GMT • Data: Full pol L1c of 13 Oct 2010 (29 swathes)

  48. Transformation antenna frame to Earth reference • Forward transform: rotation of Stokes components reference frame by angle α, routine from D. Leroux (Matlab, RWAPI-toolkit) • Back transform: rotation by – α • Multiple snapshots needed for one conversion, with small δt for small Faraday rotation • FORTRAN implementation • RFI filtering

  49. Error influence of TBh and TBv – Open Water • Area 8 – Oct 3, 2010 • Bimodal error histogram • Excluding higher errors in TBv and TBh leads to higher TBv avg and lower TBh avg • --> Q up to 10 K higher! • ... to investigate: • caused by incident angle variations (40° to 50°) ? • Consequence onretrieval?

  50. Error influence of TBh and TBv – Sea ice • Area 8 – Dec 26, 2010 • Bimodal error histogram • Excluding higher errors in TBv and TBh leads to higher TBv avg and lower TBh avg • --> Q up to 10 K higher! • ... to investigate: • caused by incident angle variations (40° to 50°) ? • Consequence onretrieval?

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