Spatio-Temporal Deep Learning for Ocean Current Prediction Based on HF Radar Data

2019 ◽  
Author(s):  
Nathachai Thongniran ◽  
Peerapon Vateekul ◽  
Kulsawasd Jitkajornwanich ◽  
Siam Lawawirojwong ◽  
Panu Srestasathiern
Keyword(s):  
Deep Learning ◽  
Radar Data ◽  
Hf Radar ◽  
Ocean Current ◽  
Spatio Temporal

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.

The Grid-Based Spatial ARIMA Model: An Innovation for Short-Term Predictions of Ocean Current Patterns with Big HF Radar Data

2019 ◽  
pp. 26-36
Author(s):  
Ratchanont Pongto ◽  
Nopparat Wiwattanaphon ◽  
Peerapon Lekpong ◽  
Siam Lawawirojwong ◽  
Siwapon Srisonphan ◽  
...  
Keyword(s):  
Arima Model ◽  
Radar Data ◽  
Hf Radar ◽  
Ocean Current ◽  
Short Term ◽  
Grid Based

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.

Surface circulation and Lagrangian transport in the SE Bay of Biscay from HF radar data

2013 ◽  
Author(s):  
Anna Rubio ◽  
Lohitzune Solabarrieta ◽  
Manuel Gonzalez ◽  
Julien Mader ◽  
Sonia Castanedo ◽  
...  
Keyword(s):  
Radar Data ◽  
Hf Radar ◽  
Bay Of Biscay ◽  
Lagrangian Transport ◽  
Surface Circulation

scholarly journals Ice-edge detection from Japanese C-band radar and high-frequency radar coastal stations

2013 ◽  
Vol 54 (62) ◽  
pp. 59-64 ◽  
Author(s):  
K. Shirasawa ◽  
N. Ebuchi ◽  
M. Leppäranta ◽  
T. Takatsuka
Keyword(s):  
Sea Ice ◽  
Edge Detection ◽  
High Frequency ◽  
Open Water ◽  
Radar Data ◽  
Hf Radar ◽  
Sea Surface ◽  
Geometrical Structures ◽  
Overlapping Data ◽  
Ice Edge

AbstractA C-band sea-ice radar (SIR) network system was operated to monitor the sea-ice conditions off the Okhotsk Sea coast of northern Hokkaido, Japan, from 1969 to 2004. The system was based on three radar stations, which were capable of continuously monitoring the sea surface as far as 60 km offshore along a 250 km long coastal section. In 2004 the SIR system was closed down and a sea surface monitoring programme was commenced using high-frequency (HF) radar; this system provides information on surface currents in open-water conditions, while areas with ‘no signal’ can be identified as sea ice. The present study compares HF radar data with SIR data to evaluate their feasibility for sea-ice remote sensing. The period of overlapping data was 1.5 months. The results show that HF radar information can be utilized for ice-edge mapping although it cannot fully compensate for the loss of the SIR system. In particular, HF radar does not provide ice concentration, ice roughness and geometrical structures or ice kinematics. The probability of ice-edge detection by HF radar was 0.9 and the correlation of the ice-edge distance between the radars was 0.7.

scholarly journals Multimodal spatio-temporal deep learning approach for neonatal postoperative pain assessment

2021 ◽  
Vol 129 ◽  
pp. 104150
Author(s):  
Md Sirajus Salekin ◽  
Ghada Zamzmi ◽  
Dmitry Goldgof ◽  
Rangachar Kasturi ◽  
Thao Ho ◽  
...  
Keyword(s):  
Deep Learning ◽  
Postoperative Pain ◽  
Pain Assessment ◽  
Learning Approach ◽  
Spatio Temporal
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