Gap Filling of the CALYPSO HF Radar Sea Surface Current Data through Past Measurements and Satellite Wind Observations. (9th November 2016)

• Record Type: Journal Article
• Authors: Gauci, Adam; Drago, Aldo; Abela, John
• Other Names: Dogandzic Aleksandar Academic Editor.
• Is Part Of: International journal of navigation and observation. Volume 2016(2016)
• Publication Date: 2016-11-09
• Subjects: Navigation -- Periodicals; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Global Positioning System -- Periodicals; Earth sciences -- Periodicals; Environmental monitoring -- Periodicals; Navigation; Télédétection; Systèmes d'information géographique; GPS; Sciences de la terre; Environnement -- Surveillance; Earth sciences; Environmental monitoring; Geographic information systems; Global Positioning System; Navigation; Remote sensing; Periodicals; Electronic journals; 629.045
• DOI: 10.1155/2016/2605198
• Languages: English
• ISSNs: 1687-5990

Abstract

Abstract : 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.