Keywords
remote sensing, scatterometer, wind retrieval, radar, hurricane, MLE, IWRAP
Abstract
Reliable ocean wind vector measurements can be obtained using active microwave remote sensing (scatterometry) techniques. With the increase in the number of severe hurricanes making landfall in the United States, there is increased emphasis on operational monitoring of hurricane winds from aircraft. This thesis presents a data processing algorithm to provide real-time hurricane wind vector retrievals (wind speed and direction) from conically scanning airborne microwave scatterometer measurements of ocean surface backscatter. The algorithm is developed to best suit the specifications for the National Oceanic and Atmospheric Administration (NOAA) Hurricane Research Division's airborne scatterometer Integrated Wind and Rain Airborne Profiler (IWRAP). Based on previous scatterometer wind retrieval methodologies, the main focus of the work is to achieve rapid data processing to provide real-time measurements to the NOAA Hurricane Center. A detailed description is presented of special techniques used. Because IWRAP flight data were not available at the time of this development, the wind retrieval performance was evaluated using a Monte Carlo simulation, whereby radar backscatter measurements were simulated with instrument and geophysical noise and then used to infer the surface wind conditions in a simulated (numerical weather model) hurricane wind field
Notes
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Graduation Date
2006
Semester
Fall
Advisor
Jones, W. Linwood
Degree
Master of Science (M.S.)
College
College of Engineering and Computer Science
Department
Electrical Engineering and Computer Science
Degree Program
Electrical Engineering
Format
application/pdf
Identifier
CFE0001477
URL
http://purl.fcla.edu/fcla/etd/CFE0001477
Language
English
Length of Campus-only Access
None
Access Status
Masters Thesis (Open Access)
STARS Citation
Vasudevan, Santhosh, "Hurricane Wind Retrieval Algorithm Development For An Airborne Conical Scanning Scatterometer" (2006). Electronic Theses and Dissertations. 986.
https://stars.library.ucf.edu/etd/986