Services and Projects

CLEAR-EO

Next-generation Earth Observation for urban, agricultural, and environmental resilience

CLEAR-EO (Climate Change, Extreme Weather And Air Quality Anomalies Resilience Through Novel Earth Observation Advanced Data Analytics) is a project funded by Horizon Europe that develops an innovative geospatial analysis and notification system. It is based on next-generation satellite data (MTG, Sentinel 4/5, EPS-SG) and European cloud infrastructures (European Weather Cloud). The goal is to create a Virtual Observatory capable of triggering personalized alerts and providing advanced data and models for three key areas:

  • Management of flash floods in urban areas;

  • Climate adaptation in agriculture;

  • Air quality forecasting.

Client

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Partners: Politecnico di Torino, ECMWF, Neuropubblic AE, DMI, ARIA Technologies, Université Paris XII, Sistema GmbH, Bye Bente

A Virtual Observatory based on unified data

CLEAR-EO creates an interconnected digital system that leverages Earth observation data and European clouds to provide:

  • Targeted and timely geospatial notifications;

  • Dynamic and predictive scenarios;

  • Tools to support operational, political, and strategic decisions.

The system architecture is based on an advanced integration of satellite observations, forecasting models, in situ data, and artificial intelligence techniques.

A scalable system for extreme events and climate adaptation

The project aims to develop an operational prototype of the Virtual Observatory, which can:

  • Generate high-precision urban flood alerts;

  • Support resilient agricultural practices through early warning and climate anomaly detection;

  • Provide detailed air quality forecasts at an urban scale.

CLEAR-EO also promotes the reinforcement of Copernicus services and contributes to the objectives of the European Green Deal.

Advanced satellite data, dynamic modeling, and AI CLEAR-EO strengthens the link between environmental observation, artificial intelligence, and European operational services in response to the climate crisis. Key distinctive elements of the project include:

Flood Warning System

based on high-resolution models and satellite observations of clouds, lightning, and convection to outline realistic urban impact scenarios;

Resilient Agriculture

combined use of reanalysis, Earth Observation, and in situ data for an integrated and operational analysis at a territorial scale;

Air Quality Forecasts

based on an innovative synergy between atmospheric dispersion models and machine learning to accurately simulate the distribution and fallout of pollutants.