Imagine if airports had a crystal ball that could predict storms and turbulence much more accurately than today's tools. This project uses AI to spot weather hazards earlier and with more detail, acting like a high-definition map for the sky. It helps planes avoid bad weather, meaning fewer cancelled flights and smoother journeys for passengers.
Current weather forecasting models often lack the precision and lead time needed to prevent costly aviation delays. This leads to inefficient airspace use and increased operational costs for airlines and airports.
A suite of AI-driven forecasting models including a Regional Forecast Model, a National Forecast Model, and a Local Nowcast Model, alongside specialized tools for hazards like turbulence and ice crystals.
If you are an airline dealing with costly flight diversions and fuel waste due to sudden storms — this project developed AI-based MET forecasting models that provide better lead times. This allows for smarter route planning to reduce delays and save costs.
If you are an ANSP dealing with airspace congestion during bad weather — this project developed AI convective and hazard models that integrate with decision-support tools. This helps balance demand and capacity more efficiently across the network.
If you are an airport operator dealing with ground delays caused by low visibility or dust storms — this project developed AI MET applications for specific hazards. This enables better planning of takeoff and landing slots to minimize operational disruptions.
Based on available project data, no specific pricing or cost structures are mentioned as this is a research and innovation project.
Yes, the project aims to reach TRL 7 through operational demonstrations and integrates with major entities like EUROCONTROL, ENAIRE, and DSNA to ensure network-level scalability.
Based on available project data, specific licensing terms are not provided, but the project is coordinated by an SME (Applied Innovative Methods, SL) with a consortium of 13 partners.
The AI models are designed to be integrated directly into existing decision-support tools and platforms used by air traffic management stakeholders.
The project runs from 2023-06-01 to 2026-05-31, with the goal of maturing results to TRL 7 by the end of the period.
The consortium is heavily weighted toward industrial application, with 6 industry partners (46% ratio) and 4 SMEs. With 13 partners across 7 countries, the group combines academic research (1 university, 4 research centers) with operational heavyweights like EUROCONTROL and ENAIRE, ensuring the AI tools are built for real-world aviation environments rather than just theoretical use.
Contact Applied Innovative Methods, SL in Spain
Contact us to connect with the KAIROS consortium for AI weather integration.
