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WIND RETRIEVAL WITH CROSS-POLARIZED SAR RETURNS

WIND RETRIEVAL WITH CROSS-POLARIZED SAR RETURNS. Paul A. Hwang Remote Sensing Division Naval Research Laboratory Washington DC, USA William Perrie and Biao Zhang Fisheries and Oceans Canada Bedford Institute of Oceanography Dartmouth, NS, Canada. Radar backscatter from the ocean surface

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WIND RETRIEVAL WITH CROSS-POLARIZED SAR RETURNS

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  1. WIND RETRIEVAL WITH CROSS-POLARIZED SAR RETURNS Paul A. Hwang Remote Sensing Division Naval Research Laboratory Washington DC, USA William Perrie and Biao Zhang Fisheries and Oceans Canada Bedford Institute of Oceanography Dartmouth, NS, Canada

  2. Radar backscatter from the ocean surface NRCS (0)  roughness  wind velocity Scatterometer (wind) Polarimetric returns (VV, HH;VH & HV) RADARSAT-2 quad-pol Co-pol (VV, HH): Good agreement with Bragg theory Wind sensitivity decreases toward high wind De-pol (VH & HV) : >>Bragg theory Increased sensitivity with wind speed (cubic) Quad-pol (25 km x 25 km); Dual-pol (scan SAR mode 300 km x 300 km or 500 km x 500 km) Passive and Active systems (scatterometer vs. radiometer)

  3. Altimeter (LP-Filtered roughness) According to the 2θ deviation, the phase shift causes constructive (left figure) or destructive (right figure) interferences (Wikipedia) Scatterometer (Bragg roughness) Composite surface Bragg theory

  4. CB theory matured by 1960s, but … Hwang 2011 Hwang 2008 Plant 2002 Elfouhaily 1997

  5. CB theory matured by 1960s, but … Hwang 2011 Hwang 2008 Plant 2002 Elfouhaily 1997

  6. RADARSAT-2 quad-polarimetric data HH HV VV Decreasing sensitivity with wind Saturation/dampening Increasing sensitivity with wind: Linear: low wind Cubic: high wind R2 >> CB Breaking contribution missing in CB computation R2 and CB in good agreement Hwang et al. 2010

  7. rms 1.65 rms 1.50 rms 1.67 rms 1.62 VV,HH: CMOD5 Hwang et al. 2010

  8. Hwang et al. 2010

  9. Black: Quad-pol (# 427); Blue: Dual-pol (# 372) Quad-pol (25 km x 25 km) Dual-pol (300 km x 300 km or 500 km x 500 km)

  10. Noise subtracted Quad-pol (25 km x 25 km) Dual-pol (300 km x 300 km or 500 km x 500 km)

  11. VH noise sub. B: -0.022, c': 0.965, D: 1.901, R: 0.837, SI: 0.250 VH w/ noise B: -0.020, c': 0.965, D: 1.912, R: 0.835, SI: 0.251 Linear U10(VH) B: 0.000, c': 0.971, D: 1.989, R: 0.819, SI: 0.261

  12. -31 dB noise subtracted

  13. Vachon and Wolfe 2011

  14. 22 32.5 Normalized Radar Cross Section (dB) Wind speed U10 5 20 45 m/s 43.5 54 Radar: Co-pol saturation problem Airborne high wind data Donnelly et al. 1999

  15. Radiometer: if saturation, it’s at a much higher wind speed because the foam factor. WindSat global analysis Data: Meissner and Wentz (2009) Hwang (2011, TGRS, submitted)

  16. Radiometer: If saturation, it’s at a much higher wind speed. Hwang (2011, TGRS, submitted)

  17. Radiometer: If saturation, it’s at a much higher wind speed. V: black; H: red Hwang (2011, TGRS, submitted)

  18. RADARSAT-2 polarimetric data HH VV VH VH -noise Quad-pol (25 km x 25 km) Dual-pol (300 km x 300 km or 500 km x 500 km) VH

  19. Scatterometer wind retrieval Wind velocity dependence of radar returns NRCS (0)  roughness  wind speed Co-pol (VV, HH): high wind less sensitive (saturation problem) De-pol (VH, HV): high wind more sensitive (may not saturate) VH wind retrieval: dual-pol scan SAR high wind speed range from active radar may approach that of passive radiometer if de-pol is used

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