AI for Climate Resilience — From Space Weather to Actionable Risk Intelligence
Use case summary
This use case proposes the development of an artificial intelligence system capable of combining space weather data (solar activity, geomagnetic storms and solar wind) with terrestrial and oceanic climate data, in order to produce more comprehensive forecasts of extreme weather events, and to produce them further in advance. The intelligence generated is turned into decision-making tools that speed up the shift from reactive to anticipatory climate risk management
1. Technical description of the AI applied
1.1 Data sources
Space weather
Geomagnetic indices (Kp, Dst), solar wind speed and density, sunspot activity and coronal mass ejections, obtained from solar observation satellites (NOAA SWPC and ESA, for example).
Terrestrial satellite data
Remote sensing imagery, sea surface temperature, soil moisture and cloud cover.
Historical and real-time data
Time series of extreme events (hurricanes, heatwaves, floods and droughts).
1.2 Model architecture
Multimodal data fusion
Combining time series (space weather and ocean data) with gridded spatial data (satellite imagery), using hybrid architectures (CNNs for spatial data, together with sequential models such as Transformers or LSTMs for time series).
Predictive models
Neural networks trained to correlate space weather anomalies with the probability and magnitude of extreme events, extending the lead time available compared with traditional meteorological models.
Uncertainty quantification
Probabilistic methods (ensembles and Bayesian networks) that attach a confidence level to every forecast, which is critical when making risk decisions.
Continuous learning
Periodic retraining of the models with new observational data, so that they adapt as the climate system evolves.
1.3 System output
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Forecasts of extreme events at higher spatial and temporal resolution.
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Risk scores by region and by critical infrastructure asset.
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Actionable early warnings for governments, insurers and infrastructure operators.
2. Workflow
3. Benefits for climate risk detection
Longer warning times
By incorporating space weather signals, the system extends the forecasting horizon beyond conventional meteorological models, giving governments and communities more time to react.
More comprehensive forecasts
Fusing multiple data sources (space, satellite and ocean) captures patterns that single-source models miss, improving the spatial and temporal resolution of forecasts.
Decisions based on quantified risk
Risk scores with confidence levels make it possible to prioritise resources and interventions far more efficiently.
A shift from a reactive approach to an anticipatory one
Insurers, critical infrastructure operators and government bodies can anticipate impacts and trigger mitigation protocols before an event occurs.
Scalability and adaptability
The continuous learning architecture allows the system to improve over time and adapt to different geographies and types of climate risk.
Socio-economic impact
Fewer lives and assets lost, insurance premiums optimised around real risk, and stronger resilience of critical infrastructure in the face of climate change.
