Methods of obtaining offshore wind direction and sea-state data from X-band aircraft SAR imagery of coastal waters

1985 ◽  
Vol 10 (2) ◽  
pp. 159-174 ◽  
Author(s):  
G. Mastin ◽  
C. Harlow ◽  
O. Huh ◽  
S. Hsu
Keyword(s):  
Wind Direction ◽  
Coastal Waters ◽  
Offshore Wind ◽  
Sea State ◽  
State Data ◽  
X Band ◽  
Sar Imagery

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.

Sea State Estimation Based on Ship Motions Measurements and Data Fusion

2013 ◽  
Author(s):  
Céline Drouet ◽  
Nicolas Cellier ◽  
Jérémie Raymond ◽  
Denis Martigny
Keyword(s):  
Data Fusion ◽  
State Estimation ◽  
Wave Height ◽  
Significant Wave Height ◽  
Wave Spectrum ◽  
Ocean Waves ◽  
Ship Motions ◽  
Sea State ◽  
Significant Wave ◽  
X Band

In-service monitoring can help to increase safety of ships especially regarding the fatigue assessment. For this purpose, it is compulsory to know the environmental conditions encountered: wind, but also the full directional wave spectrum. During the EU TULCS project, a full scale measurements campaign has been conducted onboard the CMA-CGM 13200 TEU container ship Rigoletto. She has been instrumented to measure deformation of the ship as well as the sea state encountered during its trip. This paper will focus on the sea state estimation. Three systems have been installed to estimate the sea state encountered by the Rigoletto: An X-band radar from Ocean Waves with WAMOS® system and two altimetric wave radars from RADAC®. Nevertheless, the measured significant wave height can be disturbed by several external elements like bow waves, sprays, sea surface ripples, etc… Furthermore, ship motions are also measured and can provide another estimation of the significant wave height using a specific algorithm developed by DCNS Research for the TULCS project. As all those estimations are inherently different, it is necessary to make a fusion of those data to provide a single estimation (“best estimate”) of the significant wave height. This paper will present the data fusion process developed for TULCS and show some first validation results.

Extreme Wave Condition at Doggerbank

2016 ◽  
Vol 138 (4) ◽  
Author(s):  
Espen Engebretsen ◽  
Sverre K. Haver ◽  
Dag Myrhaug
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Offshore Wind ◽  
Extreme Condition ◽  
Exceedance Probability ◽  
Extreme Wave ◽  
Sea State ◽  
Wave Condition ◽  
The North ◽  
Wave Conditions

In design of offshore wind turbines, extreme wave conditions are of interest. Usually, the design wave condition is taken as the sea state corresponding to an annual exceedance probability of 2 × 10−2, i.e., a return period of 50 years. A possible location for a future wind farm, consisting of bottom fixed wind turbines, is the Doggerbank area. The water depth in this area varies from about 60 m in the north to about 20 m in the south. The hindcast database NORA10 provides sea state characteristics from 1957 to present over a domain covering Doggerbank. Regarding the deeper areas just north of Doggerbank, this hindcast model is found to be of good quality. Larger uncertainties are associated with the hindcast results as we approach shallower water further south. The purpose of the present study is to compare sea state evolution over Doggerbank as reflected by NORA10 with the results of the commonly used shallow water hindcast model SWAN. The adequacy of the default parameters of SWAN for reflecting changes in wave conditions over a sloping bottom is investigated by comparison with model test results. Extreme wave conditions for two locations 102.5 km apart in a north–south direction are established using NORA10. This is done using both, an all sea states approach and a peak over threshold (POT) approach. Assuming the extremes for the northern position to represent good estimates, the wave evolution southward is analyzed using SWAN. The extreme condition obtained from NORA10 in the northern position is used as input to SWAN and the results from the two hindcast models are compared in the southern position. SWAN seems to suggest a somewhat faster decay over Doggerbank compared to NORA10.

scholarly journals Characteristics and Applications of the Ground-Based X Band Low Elevation Angle Brightness Temperatures under Low Sea State Based on Measured Data

2020 ◽  
Vol 12 (11) ◽  
pp. 1736
Author(s):  
Zhongqing Cao ◽  
Lixin Guo ◽  
Shifeng Kang ◽  
Xianhai Cheng ◽  
Qingliang Li ◽  
...  
Keyword(s):  
Wind Speed ◽  
Meteorological Data ◽  
Surface Wind ◽  
Elevation Angle ◽  
Surface Wind Speed ◽  
Sea Surface ◽  
Sea State ◽  
Sea Surface Wind Speed ◽  
Sea Surface Wind ◽  
X Band

In ground-based microwave radiometer remote sensing, low-elevation-angle (−3°~3°) radiation data are often discarded because they are considered to be of little value and are often difficult to model due to the complicated mechanism. Based on the observed X-band horizontal polarization low elevation angle microwave radiation data and the meteorological data at the same time, this study investigated the generation mechanism of low elevation angle brightness temperature (LEATB) and its relationship with meteorological data, i.e., temperature, humidity, and wind speed, under low sea state. As a result, one could find that the LEATB was sensitive to the atmosphere at the elevation angle between 1° to 3°, and a diurnal variation of the LEATB reached up to 10 K. This study also found a linear relationship between the LEATB and sea surface wind speed under low sea state at an elevation range from −3° to 0°, i.e., the brightness temperature decreased as the wind speed increased, which was inconsistent with the observations at the elevation angle from −10° to −5°. The variation of the LEATB difference according to the change in the over-the-horizon detection capability (OTHDC) of the shipborne microwave radar was examined to identify the reason for this phenomenon theoretically. The results showed that the LEATB difference was significantly influenced by a change in the OTHDC. Further, this study examined a remote sensing method to extract the sea surface wind speed data from experimental LEATB data under low sea state. The results demonstrated that the X-band horizontal polarization LEATBs were useful to retrieve the sea surface wind speed data at a reasonable accuracy—the root mean square error of 0.02408 m/s. Overall, this study proved the promising potential of the LEATB data for retrieving temperature profiles, humidity profiles, sea surface winds, and the OTHDC.

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