The demonstrator “Marine Environmental Indicators” has a specific focus on data related to the marine environment, and it is led by the CMCC Foundation, in collaboration with IFREMER, Mercator Ocean International, the Royal Netherlands Meteorological Institute (KNMI), and the University of Bergen.

The objectives of this demonstrator are to:

  • Calculate and distribute online information and indicators on the environmental quality of the marine area.
  • Obtain new added-value data applying big data analysis and machine learning methods on multi-source data sets.
  • Enable users to perform online and on-the-fly operations, such as selecting a portion of a dataset, to perform statistical analysis or display the data.

The target audience of the service includes intermediate users such as environmental protection agencies, and international stakeholders in the Marine Strategy Framework Directive (MSFD), in the UN Sustainable Development Goal 14 "Life below water", and in the Blue Economy.

The service offers to them a flexible capacity to perform statistical analyses of the quality and characteristics of the marine environment for the Mediterranean Sea region, with the possibility to scale in the next version up to the Global Ocean.

Attention is also dedicated to scientific users, providing to them a tool to facilitate the discovery of new climatic indicators based on machine learning, and a simplified way to analyse oceanographic data.


This version of the service provides the following features:

  • A prototype MEI Generator app

    • The prototype MEI Generator app provides an interface that allows the user to generate new added-value data and to display data already available. To generate new data, the user can specify the desired type and field, then specify the additional parameters depending on the selected type of
      output. In this prototype version, the working domain is the Mediterranean Sea and the available input data are inside the time range 1987-1989. After the selection of all the expected input parameters, the user can submit the job. At its termination, the new output will be available for the display.
  • Notebooks for a specific investigation related to the Ocean Patterns 

    • For the ‘Ocean patterns indicator’, the workflow is structured in two notebooks: a model development notebook and a prediction notebook. In the model development notebook, the user will download a training dataset, parameter, optimize and train the model, and then save it in a NetCDF file. In the second notebook (prediction notebook), the user will upload the model generated in the first notebook, download the dataset to be predicted and plot the results. The figures and the dataset including the computed variables can be saved in the user workspace.
  • Storm Severity Index (SSI) dataset

    • The SSI dataset provides users insights on atmospheric wind/storm circumstances that impact the circulation of seas such as the Mediterranean Sea. The user can combine this information with other marine environmental indicators for correlations. Calculated SSI can also be related to individual storms or seasonal distribution across a spatial sea area for a longer period of time (i.e. the storm season for the Aegean Sea). Series of calculated SSI distributions over a period like 30 years can provide insight into changes in the storm climate of a sea region hence the sea circulation impact.