NDLI: Retrieval and assessment of sub-mesoscalewind velocity vectors with Synthetic Aperture Radar

Content Provider	IEEE Xplore Digital Library
Author	Carvajal, G. Eriksson, L.E.B. Ulander, L.M.H.
Copyright Year	2011
Description	Author affiliation: Department of Earth and Space Sciences, Chalmers University of Technology, Gothenburg, Sweden (Carvajal, G.; Eriksson, L.E.B.; Ulander, L.M.H.)
Abstract	Wind vector fields are currently available from different sources at mesoscale resolutions (2 km to 200 km). The commonly provided wind resolution on 25 km imposes a limit in coastal areas and in the study of small-scale phenomena occurring in the ocean surface. Since wind is the driving force of the ocean, the study of its behavior in sub-mesoscale (< 10 km), improves the understanding of the ocean state. This paper presents a description and assessment of a wind velocity algorithm implemented for C-band (frequency 5.3 GHz) Synthetic Aperture Radar (SAR) data to detect sub-mesoscale wind fields. Results are obtained with the algorithm applied on 14 different SAR images. The quality assessment is performed by the comparison with wind velocity estimates from a numerical weather model and a scatterometer sensor. The statistics of the wind speed retrievals show a bias of about 0.5 m/s, a root mean square (rms) error of 2.6 m/s, and correlation of 79%. For the wind direction, the bias is lower than 8°, with a rms error of about 40° and a correlation of about 85%. The large magnitude of the rms error in the direction is attributed to the differences in variability and resolutions of the data.
Starting Page	2041
Ending Page	2044
File Size	313476
Page Count	4
File Format	PDF
ISBN	9781457710032
ISSN	21537003
e-ISBN	9781457710056
DOI	10.1109/IGARSS.2011.6049532
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wind speed Oceans Data models Wind forecasting Synthetic aperture radar Histograms local gradient ocean winds wind vector synthetic aperture radar (SAR) wind speed wind direction CMOD-IFR2
Content Type	Text
Resource Type	Article

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