Abstract/Details

Wind scatterometry with improved ambiguity selection and rain modeling


2003 2003

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Abstract (summary)

Although generally accurate, the quality of SeaWinds on QuikSCAT scatterometer ocean vector winds is compromised by certain natural phenomena and retrieval algorithm limitations. This dissertation addresses three main contributors to scatterometer estimate error: poor ambiguity selection, estimate uncertainty at low wind speeds, and rain corruption. A quality assurance (QA) analysis performed on SeaWinds data suggests that about 5% of SeaWinds data contain ambiguity selection errors and that scatterometer estimation error is correlated with low wind speeds and rain events.

Ambiguity selection errors are partly due to the “nudging” step (initialization from outside data). A sophisticated new non-nudging ambiguity selection approach produces generally more consistent wind than the nudging method in moderate wind conditions. The non-nudging method selects 93% of the same ambiguities as the nudged data, validating both techniques, and indicating that ambiguity selection can be accomplished without nudging.

Variability at low wind speeds is analyzed using tower-mounted scatterometer data. According to theory, below a threshold wind speed, the wind fails to generate the surface roughness necessary for wind measurement. A simple analysis suggests the existence of the threshold in much of the tower-mounted scatterometer data. However, the backscatter does not “go to zero” beneath the threshold in an uncontrolled environment as theory suggests, but rather has a mean drop and higher variability below the threshold.

Rain is the largest weather-related contributor to scatterometer error, affecting approximately 4% to 10% of SeaWinds data. A simple model formed via comparison of co-located TRMM PR and SeaWinds measurements characterizes the average effect of rain on SeaWinds backscatter. The model is generally accurate to within 3 dB over the tropics. The rain/wind backscatter model is used to simultaneously retrieve wind and rain from SeaWinds measurements. The simultaneous wind/rain (SWR) estimation procedure can improve wind estimates during rain, while providing a scatterometer-based rain rate estimate. SWR also affords improved rain flagging for low to moderate rain rates. QuikSCAT-retrieved rain rates correlate well with TRMM PR instantaneous measurements and TMI monthly rain averages. SeaWinds rain measurements can be used to supplement data from other rain-measuring instruments, filling spatial and temporal gaps in coverage.

Indexing (details)


Subject
Electrical engineering;
Oceanography;
Geophysics;
Remote sensing
Classification
0544: Electrical engineering
0415: Oceanography
0373: Geophysics
0799: Remote sensing
Identifier / keyword
Applied sciences; Earth sciences; Ambiguity selection; Rain; Scatterometry; Wind
Title
Wind scatterometry with improved ambiguity selection and rain modeling
Author
Draper, David Willis
Number of pages
237
Publication year
2003
Degree date
2003
School code
0022
Source
DAI-B 64/10, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
Advisor
Long, David G.
University/institution
Brigham Young University
University location
United States -- Utah
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3109984
ProQuest document ID
305344849
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/305344849
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