DocumentCode :
22027
Title :
Estimating Wind Stress at the Ocean Surface From Scatterometer Observations
Author :
Ali, Md Mortuza ; Bhat, G.S. ; Long, David G. ; Bharadwaj, Samarth ; Bourassa, M.A.
Author_Institution :
Nat. Remote Sensing Centre, Hyderabad, India
Volume :
10
Issue :
5
fYear :
2013
fDate :
Sept. 2013
Firstpage :
1129
Lastpage :
1132
Abstract :
Wind stress is the most important ocean forcing for driving tropical surface currents. Stress can be estimated from scatterometer-reported wind measurements at 10 m that have been extrapolated to the surface, assuming a neutrally stable atmosphere and no surface current. Scatterometer calibration is designed to account for the assumption of neutral stability; however, the assumption of a particular sea state and negligible current often introduces an error in wind stress estimations. Since the fundamental scatterometer measurement is of the surface radar backscatter (sigma-0) which is related to surface roughness and, thus, stress, we develop a method to estimate wind stress directly from the scatterometer measurements of sigma-0 and their associated azimuth angle and incidence angle using a neural network approach. We compare the results with in situ estimations and observe that the wind stress estimations from this approach are more accurate compared with those obtained from the conventional estimations using 10-m-height wind measurements.
Keywords :
atmospheric techniques; calibration; neural nets; ocean waves; wind; fundamental scatterometer measurement; neural network approach; neutrally stable atmosphere; ocean surface; scatterometer calibration; scatterometer observations; scatterometer-reported wind measurements; sea state; sigma-0 scatterometer measurements; surface radar backscatter; tropical surface currents; wind stress estimations; Artificial neural networks; Ocean temperature; Radar measurements; Sea measurements; Sea surface; Stress; Wind; Atmospheric stability; neutral stability; scatterometer; wind stress;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing Letters, IEEE
Publisher :
ieee
ISSN :
1545-598X
Type :
jour
DOI :
10.1109/LGRS.2012.2231937
Filename :
6416917
Link To Document :
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