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CERAINE

Leveraging short-term regional wave and current forecasts through AI techniques and remote sensing data within Copernicus Marine Service

September 2024 - September 2026








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The Client

The Copernicus Marine Service is a provider of free and open marine data and services to enable marine policy implementation, support Blue growth and scientific innovation. The Copernicus Marine Service (or Copernicus Marine Environment Monitoring Service), it is funded by the European Commission (EC) and implemented by Mercator Ocean International. CERAINE is a Copernicus Marine Service Evolution Project.

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The Challenge

CERAINE aims to enhance wave and circulation forecasts in the Copernicus Marine Service IBI-MFC, by improving the (winds and surface currents) forcing. Biases and errors in coastal winds and currents will be corrected with Artificial Neural Networks (ANNs), and the latter will be trained with satellite data (Satellite Synthetic Aperture Radar [SAR] and scatterometer) and High Frequency Radar (HFR) data. These ANNs will be able to predict winds at the whole NE Atlantic region and surface currents at specific coastal locations. The ANNs predictions will be blended with the existing operational ones, improving the forecast service reliability.

Partners

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Figure 1: (Up) In-situ moorings (coloured dots (*), see legend) and HFR networks (dashed blue/red polygons) at North-East Atlantic. Dashed green/purple lines display the 30 km/1º zones from coastline.

The Results

CERAINE aims to enhance IBI-MFC regional wave and circulation forecasts, by benefiting from the growing volume of available satellite data and the latest advances in Artificial Intelligence.

CERAINE contributes positively to:

  • Respond to the increasing demand for better coastal winds and currents for (i) search-&-rescue operations, (ii) pollutant, oil-spill, debris and passive tracers transport, (iii) renewable energy.
  • Respond to the increasing demand for reliable coastal wave products: Wave and sea level forecasts are inputs for coastal flooding modelling for the Copernicus Emergency Service (EMS).
  • Benefit from new observational sources: Enhancement of forecast applications by integration of satellite data with model outputs.
  • Increase forecast accuracy without compromising service delivery timeliness: The low-computational cost for correcting operational forecasts with ANNs, once they are properly trained. Most of the computational costs comes at the training stage.
  • Scalability of the infrastructure for future improvements: The ANNs can be extended, as new observations will be available, without compromising the operational processes.
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Figure 2: (Left) Taylor diagram of the SAR wind speed (Sentinel-1 mission) at Canary Islands (yellow star). Inferred speed is compared with CERAINE (red dot) vs ECMWF-IFS (blue). Units in m/s. Figure 3: (Right) Subinertial current roses at Galicia. Columns: (i) (IBI-PHY); (ii) (HF-Radar) and (iii) (ANN-predicted). The polar plot radius shows the frequency % at a given directional sector. Sectors are binned by increments marked in the legend. Data from the validation dataset. Directional convention: the direction where the currents go. The predicted period spans the 2023 Spring, 2023 Autumn and 2023-2024 Winter.