Keywords
Remote sensing, wind vector retrieval, hurricane, QuikSCAT, SeaWinds
Abstract
Over the last three decades, microwave remote sensing has played a significant role in ocean surface wind measurement, and several scatterometer missions have flown in space since early 1990's. Although they have been extremely successful for measuring ocean surface winds with high accuracy for the vast majority of marine weather conditions, unfortunately, the conventional scatterometer cannot measure extreme winds condition such as hurricane. The SeaWinds scatterometer, onboard the QuikSCAT satellite is NASA's only operating scatterometer at present. Like its predecessors, it measures global ocean vector winds; however, for a number of reasons, the quality of the measurements in hurricanes are significantly degraded. The most pressing issues are associated with the presence of precipitation and Ku-band saturation effects, especially in extreme wind speed regime such as tropical cyclones (hurricanes and typhoons). Under this dissertation, an improved hurricane ocean vector wind retrieval approach, named as Q-Winds, was developed using existing SeaWinds scatterometer data. This unique data processing algorithm uses combined SeaWinds active and passive measurements to extend the use of SeaWinds for tropical cyclones up to approximately 50 m/s (Hurricane Category-3). Results show that Q-Winds wind speeds are consistently superior to the standard SeaWinds Project Level 2B wind speeds for hurricane wind speed measurement, and also Q-Winds provides more reliable rain flagging algorithm for quality assurance purposes. By comparing to H*Wind, Q-Winds achieves ~9% of error, while L2B-12.5km exhibits wind speed saturation at ~30 m/s with error of ~31% for high wind speed ( > 40 m/s).
Notes
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Graduation Date
2009
Advisor
Jones, W. Linwood
Degree
Doctor of Philosophy (Ph.D.)
College
College of Engineering and Computer Science
Department
Electrical Engineering and Computer Science
Degree Program
Electrical Engineering
Format
application/pdf
Identifier
CFE0002654
URL
http://purl.fcla.edu/fcla/etd/CFE0002654
Language
English
Release Date
May 2009
Length of Campus-only Access
None
Access Status
Doctoral Dissertation (Open Access)
STARS Citation
Laupattarakasem, Peth, "An Improved Hurrican Wind Vector Retrieval Algorithm Using Sea Winds Scatterometer" (2009). Electronic Theses and Dissertations. 4009.
https://stars.library.ucf.edu/etd/4009