scholarly journals Applying the Method of EOF Interpolation and 2dVar to Complete the Ocean Surface Current Data Obtained from HF Radar System

2018 ◽  
Vol 34 (1S) ◽  
Author(s):  
Nguyen Thi Thu Mai ◽  
Alexei Sentchev ◽  
Tran Manh Cuong
Keyword(s):  
Interpolation Method ◽  
Surface Current ◽  
Ocean Surface ◽  
Radar Data ◽  
Current Data ◽  
Surface Flow ◽  
Radar System ◽  
Hf Radar ◽  
High Frequency Radar ◽  
Ocean Surface Current

Abstract: There are now over 350 high frequency radar (HF radar) stations operating on the coast of 37 countries around the world that allow the mapping of ocean surface current. However, observation from HF radars are often interrupted (loss of data) in both space and time due to signal inference, backscatters, ocean state.Therefore, in this study, we will present a method to improve the surface current data collected from HF radar system. Firstly, the radial surface current data will be filtered intermittently, then the result is interpolated over time and space by the orthogonal experimental EOF and the 2dVar bi-directional variable interpolation. In addition, the authors have initially applied 2dVar interpolation method to the HF radar data in Vietnam and received initial positive results. The methods used in this paper promise to be effective when applied to improve surface flow data obtained from HF radar stations in Vietnam in the future.   Keywords:EOF interpolation, 2dVar, Iroise sea, HF radar, ocean surface current.

Measuring Antenna Patterns for Ocean Surface Current HF Radars with Ships of Opportunity

2014 ◽  
Vol 31 (7) ◽  
pp. 1564-1582 ◽  
Author(s):  
Brian M. Emery ◽  
Libe Washburn ◽  
Chad Whelan ◽  
Don Barrick ◽  
Jack Harlan
Keyword(s):  
Surface Current ◽  
Ocean Surface ◽  
Radar Data ◽  
Direction Finding ◽  
Antenna Pattern ◽  
Hf Radar ◽  
Ocean Current ◽  
Identification System ◽  
Screening Methods ◽  
Ocean Surface Current

Abstract HF radars measure ocean surface currents near coastlines with a spatial and temporal resolution that remains unmatched by other approaches. Most HF radars employ direction-finding techniques, which obtain the most accurate ocean surface current data when using measured, rather than idealized, antenna patterns. Simplifying and automating the antenna pattern measurement (APM) process would improve the utility of HF radar data, since idealized patterns are widely used. A method is presented for obtaining antenna pattern measurements for direction-finding HF radars from ships of opportunity. Positions obtained from the Automatic Identification System (AIS) are used to identify signals backscattered from ships in ocean current radar data. These signals and ship position data are then combined to determine the HF radar APM. Data screening methods are developed and shown to produce APMs with low error when compared with APMs obtained with shipboard transponder-based approaches. The analysis indicates that APMs can be reproduced when the signal-to-noise ratio (SNR) of the backscattered signal is greater than 11 dB. Large angular sectors of the APM can be obtained on time scales of days, with as few as 50 ships.

scholarly journals Gap Filling of the CALYPSO HF Radar Sea Surface Current Data through Past Measurements and Satellite Wind Observations

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Adam Gauci ◽  
Aldo Drago ◽  
John Abela
Keyword(s):  
Oil Spills ◽  
Surface Current ◽  
Radar Data ◽  
Current Data ◽  
Hf Radar ◽  
Sea Surface ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Spatial Coverage ◽  
Essential Components

High frequency (HF) radar installations are becoming essential components of operational real-time marine monitoring systems. The underlying technology is being further enhanced to fully exploit the potential of mapping sea surface currents and wave fields over wide areas with high spatial and temporal resolution, even in adverse meteo-marine conditions. Data applications are opening to many different sectors, reaching out beyond research and monitoring, targeting downstream services in support to key national and regional stakeholders. In the CALYPSO project, the HF radar system composed of CODAR SeaSonde stations installed in the Malta Channel is specifically serving to assist in the response against marine oil spills and to support search and rescue at sea. One key drawback concerns the sporadic inconsistency in the spatial coverage of radar data which is dictated by the sea state as well as by interference from unknown sources that may be competing with transmissions in the same frequency band. This work investigates the use of Machine Learning techniques to fill in missing data in a high resolution grid. Past radar data and wind vectors obtained from satellites are used to predict missing information and provide a more consistent dataset.

Measurements of upper ocean surface current shear with high-frequency radar

1996 ◽  
Vol 101 (C12) ◽  
pp. 28615-28625 ◽  
Author(s):  
Daniel M. Fernandez ◽  
John F. Vesecky ◽  
Calvin C. Teague
Keyword(s):  
High Frequency ◽  
Surface Current ◽  
Ocean Surface ◽  
Upper Ocean ◽  
High Frequency Radar ◽  
Ocean Surface Current

Ocean Surface Current Extraction Scheme With High-Frequency Distributed Hybrid Sky-Surface Wave Radar System

2018 ◽  
Vol 56 (8) ◽  
pp. 4678-4690 ◽  
Author(s):  
Miao Li ◽  
Lan Zhang ◽  
Xiongbin Wu ◽  
Xianchang Yue ◽  
William J. Emery ◽  
...  
Keyword(s):  
Surface Wave ◽  
High Frequency ◽  
Surface Current ◽  
Ocean Surface ◽  
Radar System ◽  
Wave Radar ◽  
Extraction Scheme ◽  
Ocean Surface Current

scholarly journals DEDNet: Offshore Eddy Detection and Location with HF Radar by Deep Learning

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 126
Author(s):  
Fangyuan Liu ◽  
Hao Zhou ◽  
Biyang Wen
Keyword(s):  
Surface Current ◽  
Current Data ◽  
Hf Radar ◽  
Natural Phenomenon ◽  
High Frequency Radar ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Classical Statistics ◽  
Oceanic Eddy ◽  
Eddy Detection

Oceanic eddy is a common natural phenomenon that has large influence on human activities, and the measurement and detection of offshore eddies are significant for oceanographic research. The previous classical detecting methods, such as the Okubo–Weiss algorithm (OW), vector geometry algorithm (VG), and winding angles algorithm (WA), not only depend on expert’s experiences to set an accurate threshold, but also need heavy calculations for large detection regions. Differently from the previous works, this paper proposes a deep eddy detection neural network with pixel segmentation skeleton on high frequency radar (HFR) data, namely, the deep eddy detection network (DEDNet). An offshore eddy detection dataset is firstly constructed, which has origins from the sea surface current data measured by two HFR systems on the South China Sea. Then, a spatial globally optimum and strong detail-distinguishing pixel segmentation network is presented to automatically detect and localize offshore eddies in a flow chart. An eddy detection network based on fully convolutional networks (FCN) is also presented for comparison with DEDNet. Experimental results show that DEDNet performs better than the FCN-based eddy detection network and is competitive with the classical statistics-based methods.

Simulation of tsunami signatures in ocean surface current maps measured by HF radar

2009 ◽  
Author(s):  
Anna Dzvonkovskaya ◽  
Klaus-Werner Gurgel ◽  
Thomas Pohlmann ◽  
Thomas Schlick ◽  
Jiangling Xu
Keyword(s):  
Surface Current ◽  
Ocean Surface ◽  
Hf Radar ◽  
Ocean Surface Current
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