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

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.

Short-Term Forecasting of Coastal Surface Currents Using High Frequency Radar Data and Artificial Neural Networks

“…In addition, the correlation coefficient of velocity u component (0.95) between results from Model U8 and HFR data was better than that value (0.93) obtained by Gauci et al [34] for radar data gap filling; however, their correlation coefficient (0.93) was slightly better than that value (0.91) for velocity v component between results from Model V9 and HFR data. Thus, the accuracy achieved by the authors when forecasting is of the same order obtained by others in gap filling (in effect hindcasting), which is an easier process using data available at the time of interest rather than forecasting.…”

“…Results indicate that numerical model forecasts were improved considerably by adopting an ANN approach. Gauci et al [34] also used past measurements of HFR data and satellite wind observations to fill gaps of HFR data with ANN technique. Karimi et al [35] used previous sea level values to establish ANN and ANFIS models.…”

Sea Surface Circulation Structures in the Malta-Sicily Channel from Remote Sensing Data

“…Grid points were included in the analysis only if they satisfied a minimum data return of 50% using an interpolation technique described in Cosoli et al [27]. Validation of this array has been carried out in different studies since the installation of the system making this dataset a reliable product [27][28][29].…”

The Global High Frequency Radar Network

“…HFR data and predictions are one important part of SAR in the United States, being used as operational input HFR surface currents have been shown to reduce the search area by a factor of three in comparison with HYCOM after 96 h, presenting much higher skill score than a global model (Roarty et al, 2010). In Europe, significant efforts are being made to promote the use of the HFR data as reliable surface current input of the SAR emergency response and environmental modeling tools in the Iberian-Biscay-Ireland seas (e.g., the ongoing CMEMS User Uptake IBISAR project) and in Malta (Gauci et al, 2016). A first coordinated approach in Mediterranean Sea on SAR applications was made during the Tosca Project (Bellomo et al, 2015), involving five HF Radar sites in different countries.…”