scholarly journals Ocean surface determination from X-band radar-image sequences

2004 ◽  
Vol 109 (C4) ◽  
Author(s):  
H. Dankert
Keyword(s):  
Ocean Surface ◽  
Image Sequences ◽  
Radar Image ◽  
X Band

Wind- and Wave-Field Measurements Using Marine X-Band Radar-Image Sequences

2005 ◽  
Vol 30 (3) ◽  
pp. 534-542 ◽  
Author(s):  
H. Dankert ◽  
J. Horstmann ◽  
W. Rosenthal
Keyword(s):  
Wave Field ◽  
Field Measurements ◽  
Image Sequences ◽  
Radar Image ◽  
X Band

Estimate of Tidal Constituents in Nearshore Waters Using X-Band Marine Radar Image Sequences

2016 ◽  
Vol 54 (11) ◽  
pp. 6700-6711 ◽  
Author(s):  
Zhongbiao Chen ◽  
Jiayi Pan ◽  
Yijun He ◽  
Adam T. Devlin
Keyword(s):  
Image Sequences ◽  
Radar Image ◽  
X Band ◽  
Marine Radar ◽  
Tidal Constituents

A New Algorithm to Retrieve Wave Parameters From Marine X-Band Radar Image Sequences

2014 ◽  
Vol 52 (7) ◽  
pp. 4083-4091 ◽  
Author(s):  
Zhongbiao Chen ◽  
Yijun He ◽  
Biao Zhang ◽  
Zhongfeng Qiu ◽  
Baoshu Yin
Keyword(s):  
Image Sequences ◽  
Radar Image ◽  
Wave Parameters ◽  
X Band

scholarly journals A Marine Radar Wind Sensor

2007 ◽  
Vol 24 (9) ◽  
pp. 1629-1642 ◽  
Author(s):  
Heiko Dankert ◽  
Jochen Horstmann
Keyword(s):  
Standard Deviation ◽  
Correlation Coefficient ◽  
Wind Direction ◽  
Ocean Surface ◽  
Image Sequences ◽  
Wind Vector ◽  
Sea State ◽  
Wind Speeds ◽  
X Band ◽  
Wind Measurements

Abstract 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-to-noise 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−1. 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.

A new method to retrieve significant wave height from X-band marine radar image sequences

2014 ◽  
Vol 35 (11-12) ◽  
pp. 4559-4571 ◽  
Author(s):  
Zhongbiao Chen ◽  
Yijun He ◽  
Biao Zhang ◽  
Zhongfeng Qiu ◽  
Baoshu Yin
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Image Sequences ◽  
New Method ◽  
Radar Image ◽  
Significant Wave ◽  
X Band ◽  
Marine Radar
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