A Novel Hurricane OVW Retrieval Technique for QuikSCAT W. Linwood Jones 1 , Peth Laupattarakasem 1 , Suleiman Alsweiss 1 , Christopher C. Hennon 2 , and Svetla Hristova-Veleva 3 1 Central Florida Remote Sensing Laboratory University of Central Florida Orlando, Florida 32816 2 University of North Carolina Asheville Asheville, NC 28804 3 Jet Propulsion Laboratory Pasadena, CA Ocean Vector Winds Science Team Meeting Annapolis, MD May 9-11, 2011
Outline • SeaWinds OVW measurements in hurricanes • Radar backscatter geophysical model function • X-Winds OVW retrieval algorithm – wind direction retrieval – wind direction retrieval – wind speed retrieval • X-Wind Comparisons with H*Wind
SeaWinds Hurricane OVW Measurements • Historically Ku-band scatterometers have consistently under estimated hurricane wind speeds • Issues – Inadequate spatial resolution – Inadequate spatial resolution – Rain contamination – Geophysical OVW algorithms • GMF – relationship between radar backscatter and surface wind speed • Rain correction
Extreme Winds Sigma-0 Geophys Model Function (XW-GMF) • Special GMF developed for hurricanes – Training dataset of 35 QScat hurricane overpasses – 3-D GMF: σ o = f(ws, relative wdir, atmos transmis) • WS: one-minute sustained 10m wind speeds from NOAA • WS: one-minute sustained 10m wind speeds from NOAA HRD H*Wind surface wind analysis • Relative wind direction χ: from multi-radar az looks and from H*Wind analysis • Atmos transmissivity: inferred from simultaneous observations of QRad H-pol brightness temperatures
Hurricane Geophysical Model Function (GMF) QuikSCAT H-pol, 30 m/s o = C 0 ( ws ) + C 1 ( ws ) ∗ cos χ + C 2 ( ws ) ∗ cos2 χ σ -6 -8 σ o , dB -10 ) ) B -12 d ( 0 -14 σ σ σ σ -16 -18 -20 0 50 100 150 200 250 300 350 χ χ ( ° χ χ ° ) ° °
GMF C 0 Coeff Dependence on Wind Speed Extreme Winds GMF C 0 H-pol coefficient -6 ) B -8 d , dB ( -10 l -12 o o C 0 , dB p -14 - H -16 0 C -18 -20 -22 1 10 10 20 40 60 Wind Speed (dB) Wind Speed, m/s
GMF Anisotropy with Wind Speed ∆ σ o = C 1 ( ws ) ∗ cos χ + C 2 ( ws ) ∗ cos2 χ Extreme Winds GMF C 1 H-pol coefficient Extreme Winds GMF C 2 H-pol coefficient 1 2 r units r units 0.9 1.8 0.8 1.6 C 1 , linear uni C 2 , linear uni l l 0.7 1.4 o o o o 0.6 1.2 p p - - 0.5 1 H H 1 2 0.4 0.8 C C 0.3 0.6 0.2 0.4 0.1 0.2 0 0 1 1 10 10 10 20 40 60 10 20 40 60 Wind Speed (dB) Wind Speed (dB) Wind Speed, m/s Wind Speed, m/s
XW-GMF Atmospheric Transmissivity • Rain attenuation is corrected implicitly through use of the QRad H-pol brightness temperature T bh – GMF = f(ws,rel_wdir, T bh ) -10.5 T bh -11 170 bh -11.5 155 K σ 0 H-pol (dB) -12 140 K σ σ σ -12.5 -13 -13.5 50 100 150 200 250 300 350 χ ( ° ° ) χ χ χ ° °
X-Winds OVW retrieval Algorithm • Attributes – Assumes cyclonic wind direction rotation about TC center – Assumes rain effects are primarily absorptive • Rain volume backscatter is neglected • Rain volume backscatter is neglected – Empirical 3-D XW-GMF accounts for backscatter saturation with wind speed & rain absorption – Uses scalar wind direction and wind speed estimation • Not traditional maximum likelihood estimation
Scalar Wind Direction Estimation • Relies on anisotropy of measured difference between forward and aft looking backscatter measurements ∆σ o meas = ( σ o fore – σ o aft ) @ top-of-the-atmos ∆σ o - linear units its Latitude Index Longitude Index
Wind Direction Modeling of ∆σ o Sigma-0 anisotropy model for single radar azimuth ∆ σ o = C 1 ( ws ) ∗ cos χ + C 2 ( ws ) ∗ cos2 χ look Taking the difference between fore & aft radar looks yields o ( ( ) + C 2 cos 2 χ fore − cos2 χ aft ) + C 2 cos 2 χ fore − cos2 χ aft ( ( ) ) ∆ σ mod = cos χ fore − cos χ aft ∆ σ mod = cos χ fore − cos χ aft Wind direction solutions (aliases) are roots of ( ) = 0 o o ∆ σ m eas − ∆ σ mod Yields ambiguous wind direction solutions typically ~ 6 – 8
• Keep ambiguities within window ± 30 ° from CCW spiral Wind Direction Alias Removal • Multi-pass median filter and populate missing pixels through interpolation • Use smoothed TC wind field for wind speed estimation Wind Directions Ambiguities Initial Wind Direction Field
X-Winds Scalar Wind Speed Estimation • Use smooth TC wind field as “estimated true” wind direction and calculate relative wind direction χ = (radar az – “est-wind dir”) Find 1-D wind speed solution for each σ o flavor (pol & direction) that satisfies this relationship direction) that satisfies this relationship ( ) = 0 o σ meas − ( XW − GMF )
X-Winds Wind Speeds Retrievals (4- σ o flavors) HF HA VF VA Wind Speed (m/s)
X-Winds Comparisons - L2B-12.5km OVW product - H*Wind surface wind analysis
Hurricane Fabian Rev#21898 X-Winds X-Winds Previous Q-Winds Previous Q-Winds H*Wind L2B-WS 12.5km
Hurricane Ivan Rev#27217 X-Winds X-Winds Previous Q-Winds Previous Q-Winds H*Wind L2B-WS 12.5km
Wind Speeds Comparison (10 revs) X-Winds QuikSCAT L2B-12.5km Tb H-pol Tb H-pol 50 50 260 260 240 240 40 40 220 220 m L2B-12.5km 200 200 30 30 X-Winds 180 180 160 160 20 20 140 140 10 120 10 120 100 100 0 0 0 10 20 30 40 50 0 10 20 30 40 50 H*Wind H*Wind
Wind Direction Comparison (10 revs) X-Winds QuikSCAT L2B-12.5km Tb H-pol Tb H-pol 350 350 260 260 300 300 ° ) 240 240 ion ( ° ° ° ° ) n ( ° ° ° X-Winds Wind Direction L2B-12.5km Wind Directio 220 220 250 250 200 200 200 200 180 180 150 150 160 160 140 140 100 100 120 120 50 50 100 100 0 0 0 100 200 300 0 100 200 300 H*Wind Wind Direction ( ° ° ) H*Wind Wind Direction ( ° ° ) ° ° ° °
Conclusion • A new OVW retrieval algorithm has been developed for SeaWinds – Specifically tailored to to tropical and extra- tropical cyclones – Retrieves wind speeds that are approx 10 m/s higher than the standard L2B-12.5km OVW product • Performs better in comparison with H*Wind surface wind analyses • A new L-3 SeaWinds TC OVW data set will be produced starting this summer 2011
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