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International Journal of Navigation and Observation
Volume 2016 (2016), Article ID 2605198, 9 pages
Research Article

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

1Physical Oceanography Research Group, Department of Geosciences, Faculty of Science, University of Malta, Msida, Malta
2Department of Intelligent Computer Systems, Faculty of ICT, University of Malta, Msida, Malta
3Department of Computer Information Systems, Faculty of ICT, University of Malta, Msida, Malta

Received 30 June 2016; Revised 11 September 2016; Accepted 29 September 2016

Academic Editor: Aleksandar Dogandzic

Copyright © 2016 Adam Gauci et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.