Presenting Author: Professor David Long
Originally designed only for low resolution wind retrieval, it has been demonstrated that QuikSCAT observations can be used to simultaneously estimate wind and rain at 25 km resolution with reasonable accuracy. Coupled with sigma-0 reconstruction techniques, simultaneous wind/rain (SWR) estimation can also be applied at an ultra-high resolution (UHR) of 2.5 km. The higher resolution minimizes beam-filling effects and is more commensurate with the small size of rain cells, but the oblique geometry, inexact reconstruction, high noise level, and computational requirements introduces complications in UHR SWR retrieval. Nevertheless, UHR SWR is surprisingly effective in detecting and mapping rain and improving wind accuracy in the presence of rain. UHR SWR rain estimates compare well with collocated TRMM-PR rain values and provide an effective rain flag. UHR SWR winds are improved over UHR wind-only retrieval in regions of rain. In this presentation, key tradeoffs and analysis results are discussed, including model-based techniques. The latter includes Bayesian models designed and optimized for hurricane observation. A modified wind/rain model function for use at UHR is derived from collocated TRMM-PR, NCEP, and QuikSCAT. A prototype UHR wind and rain product that includes land contamination rejection is described. Validation of UHR SWR winds and rain is also discussed. A fundamental difficulty in validating UHR wind and rain is the lack of suitable comparison data. However, a fortuitous occurrence of nearly simultaneous QuikSCAT and Radarsat-1 ScanSAR passes over Hurricane Katrina provides a unique opportunity for validation. Using collocated H*winds and NEXRAD rain observations, 1 km resolution wind speed fields are derived from the Radarsat sigma-0 images. Clearly observed high resolution rain effects are described and the implications of these observations on simultaneous wind/rain retrieval are discussed.
2021 International OVWST Meeting
February 24, March 3rd, and March 10th from 10:00 AM ET to 11:30 AM ET Virtually via GoToMeeting