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Refinement of the Seawinds/QuikScat sea ice edge

Refinement of the Seawinds/QuikScat sea ice edge. Jörg Haarpaintner, Rasmus T. Tonboe David G. Long, Michael L. VanWoert, Kyle R. Dedrick. Refinement of the Seawinds/QuikScat sea ice edge. 1) Defining the sea ice edge 2) QuikScat/Seawinds sensor and its enhanced resolution products

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Refinement of the Seawinds/QuikScat sea ice edge

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  1. Refinement of the Seawinds/QuikScat sea ice edge Jörg Haarpaintner, Rasmus T. Tonboe David G. Long, Michael L. VanWoert, Kyle R. Dedrick

  2. Refinement of the Seawinds/QuikScat sea ice edge 1) Defining the sea ice edge 2) QuikScat/Seawinds sensor and its enhanced resolution products 3) Typical sea ice signatures 4) The refined QuikScat/Seawinds sea ice edge algorithm 5) Validation with RADARSAT SAR and Use of data 6) Conclusion

  3. Definition of the ice edge? • Border between ice and ocean is often not clear. • What do we consider as ice? • Parameters to consider: ice concentration, type, thickness, floe size • What do we need the ice edge for? Operation, climate, regional models ? • For near-real time operations & safety: all detectable ice • Time: • How old is the information about the ice edge? • For how long is it considered to be valid? • Space: • What can we get? Resolution of the remote sensing sensor • How far did the ice move during the data processing?

  4. QuikScat/SeaWinds facts • Ku-band scatterometer (13.4 GHz) • Scanning pencil beam • HH and VV polarization • Incident angles: 47° (HH), 55 ° (VV) • Developed to measure wind over ocean • Daily coverage of the whole Arctic • Signal processing and resolution enhancement algorithm improve resolution from 25 km to about 8 km by producing 36h composites over the Arctic.

  5. Available QuikScat/SeaWinds products • 5 Daily 36h composites (SIR format) in 2.225 km grid (NOAA/NESDIS). • HH = Horizontal polarized backscatter • VV = Vertical polarized backscatter • 2 daily standard deviation images (STDh and STDv) • ice mask => Active polarization ratio APR=(HH-VV)/(HH+VV)

  6. Current ice mask (Remund & long, 1999) • Automatic algorithm using the SIR images: HH, VV, PR, std(H&V) • Auto. thresholding and filtering eliminates low ice concentrations. • CIS estimated the ice edge at about 70%. =>Need to study the scatter characteristics for ice classes

  7. Extraction of ice classes from DMI ice chartsand co-location with QS L2B data • Egg-code information • Total ice concentration • Classes of the 3 thickest types • Floe size / type • Definition of 6 ice classes • Close multi-year ice (>70%) • Close first-year ice (>70%) • Medium MYI (30% - 70%) • Medium FYI (30% - 70%) • Open MYI (<30%) • Open FYI (<30%)

  8. Seasonal HH-backscatter variation

  9. Seasonal variation of the polarization ratio PR=(HH-VV)/(HH+VV)

  10. Active polarization ratio versus SSM/I NASA-Team ice concentration

  11. Daily standard deviation versus SSM/I NASA-Team ice concentration

  12. New ice edge algorithm • Resolution reduction to 6.675 km (3x3 pixels) • Fixed thresholds based on the statistical analysis • Main parameter: -0.02 < APR -0.02 < APRmax • Noise reduction: HH > -25 dB (28 dB) AND VV > -25 dB (28 dB) AND STDmax < 4 (5) And if contact with land mask

  13. Results compared to RADARSAT

  14. Possible improvements • Geophysical dynamics during integration time (36h) dominate inaccuracy. • Resolution enhancement depends on number of observations. • Balance between maximum resolution (5-10 km) and scale of geophysical dynamics (ice drift ~1km/h)during integration time • Solutions to reduce the integration time: • Smaller areas (Barents Sea) need less time to be covered • Changing the composite from 36h to last 3 orbits • Combining Seawinds sensors on QuikScat and on ADEOS-2

  15. Use of data, collaboration and future developments • Ice charts • Operational near-real time ice services (ICEMON & SATHAV) • Climatic research • Input for models • Seasonal and interannual evolution • Ice dynamics • Ice drift (Framst.) • Thermodynamics Future • Ice concentration • Antarctic

  16. Conclusion • Ice edge determination is not only a question of feasibility but a question of definition and purpose considering time and spatial scales • Simple algorithm based on fixed thresholds from statistical analysis • Good agreement between QS ice edge and RADARSAT images even for low ice concentration. • Encountered problems: • with strong winds (ocean noise) especially at lower latitudes where satellite cover is sparse • melting events in summer • Limited by scale of geophysical dynamics (thermodynamics and kinetics) during integration time • Promoting the data for collaboration

  17. That’s it!

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