Use Cases

CSCS carries out glider missions all over the world. Gliders are small autonomous underwater vehicles that use changes in displacement to change buoyancy, and internal mass movements to attain pitch and roll movements. Coordinated by an on-board flight computer, this slow but very efficient profiling device collects data as it descends and ascends, sending the results and receiving commands every few hours when at the surface for many months at a time. 

One of the main goals of OceanGliders, as part of GOOS, is to monitor and promote the activity of the glider observing network. The goal is not to provide data, but to document and give guidance on how to share someone’s glider activity, in order to raise their visibility and the visibility of gliders as whole, as concerns long-term sustained observing for societal benefit.

With over 300,000 water column profiles, in both coastal and deep sea areas, the National Oceanographic Data Centre at OGS manages the most comprehensive marine data storage in Italy. The multidisciplinary  archive includes data since 1909 up to now. It ranges  from physics to chemistry, from biology and geology to meteorology. Data are mainly collected by Italian institutions within the Mediterranean Sea basin, but also by other countries within neighbouring sea basins. Near real-time data are consistently integrated into the historical archive.

The Euro-Mediterranean Center on Climate Change (CMCC) has developed a first version of a web application with the “Total Sea Level” variable from EMODnet Physics. This solution presents a demonstrator of end-user application and decisional support dashboard systems to support public authorities in their daily management activities.

A joint Copernicus Marine and EMODnet data catalogue for the Marine Strategy Framework Directive (MSFD) has been developed. It gathers all relevant marine data products from Copernicus Marine Service and EMODnet for all the MSFD descriptors (except Descriptor 4) in the Baltic Sea.

Wind, wave and sea level data are valuable sources under the GRISIS multidisciplinary and holistic analysis of natural risks. EMODnet Physics is making available aggregated and easy to use products that can be easily machine-to-machine integrated into the innovative multi-risk technological platform for management and mitigation of multiple risks that GRISIS project is developing in order to build resilence plans and infrastructure in South Italy.

In close collaboration with OceanOPS, EMODnet Physics is connecting multiple data sources with the OceanOPS metadata repository thanks to the unique identification system managed by OceanOPS. This is automatically reducing the duplication issues related to multi data sources and increasing the quality of the information system developed by EMODnet Physics. At the same time, EMODnet Physics is connecting OceanOPS to networks that are not yet under its radar to continuously improve the monitoring of the Ocean Observing systems operating in the global ocean.

Assist in Gravitation and Instrumentation (AGI) srl., was created as a spin-off of the Italian National Institute for Astrophysics (INAF). They recently developed the OS‑IS^®(Ocean Seismic – Integrated Solution) technology, which is based on high sensitivity accelerometers to measure the sea-state (waves significant height and period) from inshore land.

Despite fishes’ well documented strong sensitivity to temperature and other physical ocean parameters, modelling these shifts has proven difficult. A key issue is the scarcity of temperature data at depth, where fish usually are. As stated by Captain Lawrence Moffet, F/V Matt & Patt:

Berring Data Collective (BDC) is a start-up dedicated to ocean observation via collaboration with fishing vessels. Fishing gears such as nets and traps make an ideal platform for compact IoT sensors. When the gears are let down to the bottom and pulled back up with fish, sensors that are along for the ride collect water column profile data. When the net surface data is automatically sent onto ocean data users in near real time.