Decision Support Tools & Data

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SAFI Tools and Services

In order to cater for the differing needs and interests of industry stakeholders, SAFI has focused on the development of two levels of service tailored to specific client requirements. These are the SAFI Mini-Web tool and the SAFI Advanced tool.

The SAFI Mini-Web tools are web pages specifically designed for the needs of each user, tailored to their own requirements. The tools are easy to use with visual information for users over their areas of interest. Time-series data showing the pattern of environmental characteristics is available to view online and downloadable for further analysis. Near-real-time data and wave forecasting information are also available on a daily basis for up-to-date monitoring in the area. To view the Mini-Web tool, see www.acri-he.fr/projects/safi

The SAFI Advanced tool allows users to carry out a more in-depth analysis of the data. This system has been made possible with the cooperation of the Coastal Thematic Exploitation Platform, or C-TEP, from the European Space Agency. This allows for the computation of huge amounts of data using a powerful server, remotely hosted by C-TEP. For more information, see coastal-tep.eo.esa.int.

This tool can be used to conduct a long time-series analysis for a user-defined area, conduct statistical analysis such as climatology computations and spatial analyses, and to identify suitable areas that correlate with user-specified environmental criteria. To view a demo-version of this service, see safiservices.ucc.ie.  

SAFI has selected the most appropriate data from the wide variety of sources, homogenised the formats and information display, and developed user-friendly tools and services. This allows end-users to work with the information produced, without having to download large datasets locally.

Over 2 million files are available for areas covering Europe and Morocco. These have been accessed from a wide  variety of sources, in particular from the European Copernicus Programme, allowing access to long-term data archives of environmental information.

Chloe Vincent (ACRI-ST)


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