The near-surface current velocity determined from image sequences of the sea surface

Senet, C.M.; Seemann, J.; Ziemer, F.

doi:10.1109/36.911108

Cited by 198 publications

(172 citation statements)

References 6 publications

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“…After determining water depth (Ϸ30 m at FINO-I ) and velocity of encounter (between fixed platform and near-surface current vector), by fitting the theoretical dispersion relation to the signal coordinates of the linear surface waves in the wavenumber frequency domain (Young et al 1985;Senet et al 2001), the dispersion relation is used as a signal filter in the image power spectrum to separate the linear sea state signal from the background noise (Ziemer 1991):…”

Section: B Consideration Of the Sea Statementioning

confidence: 99%

A Marine-Radar Wind Sensor

Dankert¹,

Horstmann²

2006

2006 IEEE International Symposium on Geoscience and Remote Sensing

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A new method for retrieving the wind vector from radar-image sequences is presented. This method, called WiRAR, uses a marine X-band radar to analyze the backscatter of the ocean surface in space and time with respect to surface winds. Wind direction is found using wind-induced streaks, which are very well aligned with the mean surface wind direction and have a typical spacing above 50 m. Wind speeds are derived using a neural network by parameterizing the relationship between the wind vector and the normalized radar cross section (NRCS). To improve performance, it is also considered how the NRCS depends on sea state and atmospheric parameters such as air-sea temperature and humidity. Since the signal-tonoise ratio in the radar sequences is directly related to the significant wave height, this ratio is used to obtain sea state parameters. All radar datasets were acquired in the German Bight of the North Sea from the research platform FINO-I, which provides environmental data such as wind measurements at different heights, sea state, air-sea temperatures, humidity, and other meteorological and oceanographic parameters. The radar-image sequences were recorded by a marine X-band radar installed aboard FINO-I, which operates at grazing incidence and horizontal polarization in transmit and receive. For validation WiRAR is applied to the radar data and compared to the in situ wind measurements from FINO-I. The comparison of wind directions resulted in a correlation coefficient of 0.99 with a standard deviation of 12.8°, and that of wind speeds resulted in a correlation coefficient of 0.99 with a standard deviation of 0.41 m s Ϫ1. In contrast to traditional offshore wind sensors, the retrieval of the wind vector from the NRCS of the ocean surface makes the system independent of the sensors' motion and installation height as well as the effects due to platform-induced turbulence.

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Section: B Consideration Of the Sea Statementioning

confidence: 99%

A Marine-Radar Wind Sensor

Dankert¹,

Horstmann²

2006

2006 IEEE International Symposium on Geoscience and Remote Sensing

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“…In the past, marine radar image sequences have been operationally used to determine two-dimensional wave spectra (Young et al 1985) and significant wave height (Nieto-Borge et al 2004). Furthermore, marine radars have been used to measure near-surface currents (Senet et al 2001;Trizna 2010), bathymetry (Bell 1999;Flampouris et al 2008), individual waves (Dankert and Rosenthal 2004), and wave groups (Dankert et al 2003a) and to detect ships (Vicen-Bueno et al 2011a). Marine radars are also useful for estimating wind speed and direction (Dankert et al 2003b;Dankert and Horstmann 2007).…”

Section: Introductionmentioning

confidence: 99%

Real-Time Ocean Wind Vector Retrieval from Marine Radar Image Sequences Acquired at Grazing Angle

Vicen-Bueno

Horstmann

Terril

et al. 2013

Journal of Atmospheric and Oceanic Technology

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This paper proposes a novel algorithm for retrieving the ocean wind vector from marine radar image sequences in real time. It is presented as an alternative to mitigate anemometer problems, such as blockage, shadowing, and turbulence. Since wind modifies the sea surface, the proposed algorithm is based on the dependence of the sea surface backscatter on wind direction and speed. This algorithm retrieves the wind vector using radar measurements in the range of 200-1500 m. Wind directions are retrieved from radar images integrated over time and smoothed (averaged) in space by searching for the maximum radar cross section in azimuth as the radar cross section is largest for upwind directions. Wind speeds are retrieved by an empirical third-order polynomial geophysical model function (GMF), which depends on the range distance in the upwind direction to a preselected intensity level and the intensity level. This GMF is approximated from a dataset of collocated in situ wind speed and radar measurements (;31 000 measurements, ;56 h). The algorithm is validated utilizing wind and radar measurements acquired on the Research Platform (R/P) FLIP (for Floating Instrumentation Platform) during the 13-day Office of Naval Research experiment on HighResolution Air-Sea Interaction (HiRes) in June 2010. Wind speeds ranged from 4 to 22 m s 21 . Once the proposed algorithm is tuned, standard deviations and biases of 148 and 218 for wind directions and of 0.8 and 20.1 m s 21 for wind speeds are observed, respectively. Additional studies of uncertainty and error of the retrieved wind speed are also reported.

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“…The wave parameters are then used to resolve the effects on the wave dispersion of an unknown depth and unknown Doppler shift by the mean current; a process known as inversion. Further information on the wave inversion method can be found in [2], [3], [4] and [5]. The calculation of wave parameters requires the analysis of a finite area of sea surface carrying the assumption that the wavenumber and frequency spectra across the discrete analysis area are homogenous.…”

Section: Methodsmentioning

confidence: 99%