Success Story
Improving Air Traffic Management through AI-powered Storm Prediction
ENAIRE OPEN INNOVATION
Air traffic management relies heavily on the ability to anticipate adverse weather events. Convective storms are one of the main operational challenges for air navigation service providers, as they can lead to capacity restrictions, rerouting, delays, and increased operator workload.
As part of the ENAIRE Open Innovation acceleration program, AI Methods collaborated closely with ENAIRE and CRIDA to develop an artificial intelligence-based solution capable of improving storm prediction and enabling earlier, more efficient decision-making.
The Challenge
Air traffic management relies heavily on the ability to anticipate adverse weather events. Convective storms are one of the main operational challenges for air navigation service providers, as they can lead to capacity restrictions, rerouting, delays, and increased operator workload.
As part of the ENAIRE Open Innovation acceleration program, AI Methods collaborated closely with ENAIRE and CRIDA to develop an artificial intelligence-based solution capable of improving storm prediction and enabling earlier, more efficient decision-making.
AI Methods developed two artificial intelligence models for storm prediction across the Iberian Peninsula and the Canary Islands with an improved forecasting horizon.
The models were deployed in real time and automatically updated, allowing ENAIRE controllers and operations room managers to access advanced weather information through a geospatial data server.
The solution included:
• AI-based storm prediction models.
• An intuitive color-coded scheme to identify convective storm risk.
• Cloud ceiling prediction for each convective cell.
• Real-time operational access for ENAIRE personnel.
The Solution
The Key to Success
Beyond the technological development itself, the project enabled continuous adaptation of the tool to ENAIRE’s operational needs through close collaboration between technical and operational teams.
The collaboration with AI METHODS has been radically different from collaborations we've had in the past. It has been much more positive, closer, more conscious of our needs, and more aware of the adaptations we requested for the interface.
— Iván Díaz, Head of Division at the Operations Directorate of ENAIRE
Innovation to enhance ATM operations
The initiative demonstrated the potential of artificial intelligence as a decision-support tool for air traffic management and aeronautical weather forecasting.
48h
of anticipation provided through real-time weather forecasts updated every 6h.
7km
Spatial resolution for convective cells, improving current resolutions.
According to CRIDA, solutions like this can help improve both the accuracy and anticipation of weather forecasts, enabling more efficient operational decisions.
