Scaling API-first – The story of a global engineering organization
TU1.T10.2.pptx
1. A Novel Ocean Vector Winds RetrievalTechnique for Tropical CyclonesPeth Laupattarakasem1, Suleiman Alsweiss1, W. Linwood Jones1, and Christopher C. Hennon21Central Florida Remote Sensing LaboratoryUniversity of Central FloridaOrlando, Florida 328162University of North Carolina AshevilleAsheville, NC 28804IGARSS 2011Vancouver, CanadaJuly 26, 2011
2. Outline SeaWinds OVW measurements in hurricanes Radar backscatter geophysical model function X-Winds OVW retrieval algorithm wind direction retrieval wind speed retrieval X-Wind Comparisons with H*Wind & QuikSCAT standard L2B OVW product
3. SeaWinds Hurricane Measurements Historically Ku-band scatterometers have under estimated hurricane wind speeds Issues Inadequate spatial resolution (25 km IFOV) Rain contamination (backscatter & attenuation) Geophysical OVW retrieval algorithm Developed for global winds Not optimized for tropical cyclones
4. Extreme Winds Sigma-0 Geophys Model Function (XW-GMF) Special QuikSCATGMF developed for hurricanes Training dataset of 35 QScat hurricane overpasses 3-D GMF: so = f(ws, relative wdir, atmostransmis) WS: one-minute sustained 10m wind speeds from NOAA HRD H*Wind surface wind analysis Relative wind direction c:the difference between wind direction and the radar azimuth look Atmostransmissivity: inferred from simultaneous observations of QRad H-pol brightness temperatures
7. GMF Anisotropy with Wind Speed C1 , linear units C2 , linear units 10 20 40 60 Wind Speed, m/s 10 20 40 60 Wind Speed, m/s
8. XW-GMF Atmospheric Transmissivity Rain attenuation is corrected implicitly through use of the QRad H-pol brightness temperature Tbh GMF = f(ws, rel_wdir, Tbh) Tbh 140 k 160 k 190 k
9. 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 Empirical 3-D XW-GMF accounts for backscatter saturation with wind speed & rain absorption Uses sequential scalar wind direction and wind speed estimation Not traditional ocean vector wind “maximum likelihood estimation” retrieval
10. Scalar Wind Direction Estimation Relies on anisotropy of measured difference between forward and aft looking backscatter measurements Dsomeas = (sofore – soaft) @ top-of-the-atmos Typical hurricane image Latitude Index Dso - linear units Longitude Index
11. Wind Direction Modeling of Dso so anisotropy model for single radar azimuth look Taking the difference between fore & aft radar looks yields Wind direction solutions (aliases) are roots of Yields ambiguous wind direction solutions typically ~ 6 – 8
12. Wind Direction Alias Removal Keep ambiguities within window ±30 from CCW spiral Multi-pass median filter and populate missing pixels through interpolation Use smoothed TC wind field for wind speed estimation Wind Directions Ambiguities Retrieved Wind Direction Field
22. Conclusion A new OVW retrieval algorithm has been developed for SeaWinds Specifically tailored for tropical and extra-tropical cyclones Performance evaluated using comparisons with H*Wind surface wind analysis and L2B-12.5km Better agreement with H*Wind A new (11-year) SeaWindsTC OVW data set will be produced starting next year 2012
Editor's Notes
Put markers every 90 degrees for the GMF plots to differentiate the two GMFs
Put markers every 90 degrees for the GMF plots to differentiate the two GMFs