Imagine the upper atmosphere as a giant mirror for radio waves; sometimes, 'ripples' called TIDs distort this mirror and mess up signals. This project creates a weather forecast for these ripples using AI and satellite data. It gives operators a few hours of warning so they can adjust their systems before the signal drops.
Space weather disturbances (TIDs) disrupt critical HF radio and satellite communications, causing signal loss and operational downtime for aerospace and civil protection sectors.
AI-based prediction models for large and medium scale TIDs and prototype alert services validated through ground tests.
If you are an HF radio operator dealing with sudden signal loss or interference—this project developed prediction models that forecast disturbances up to 3 hours ahead. This allows you to switch frequencies or reroute traffic to maintain connectivity.
If you are a navigation provider dealing with inaccurate HF direction finding—this project developed prototype services that alert you to ionospheric disturbances. This reduces the risk of positioning errors during critical transit phases.
If you are a disaster response agency dealing with unreliable emergency communications—this project developed validated models to issue alerts for TIDs. This ensures that critical ground infrastructure remains operational during space weather events.
Based on available project data, the EU contributed EUR 999,750 to develop the system, but specific commercial pricing for the end-user service is not listed.
The project developed prototype services and an architectural concept for a real-time operational service, but it is currently at the demonstration and validation stage.
Based on available project data, there is no specific mention of licensing terms or patent filings; the project focused on developing validated models and prototype services.
The system is designed to be fully compliant and complementary to the ESA Space Weather services.
The models provide forecasts for Large Scale Travelling Ionospheric Disturbances with a forecasting horizon of up to 3 hours.
The consortium is heavily research-driven, consisting of 10 partners across 9 countries, with 8 research organizations and only 1 SME (10% industry ratio). This indicates the project is primarily focused on scientific validation and model accuracy rather than immediate commercial productization, though the inclusion of an SME suggests a path toward service delivery.
Contact ETHNIKO ASTEROSKOPEIO ATHINON for technical specifications on the TID prediction models.
Contact SciTransfer to find a partner for implementing these AI-driven space weather alerts into your communication infrastructure.
