Predict solar and geomagnetic events with AI to protect satellites, ground stations, and critical infrastructure.
Space weather AI ingests solar imagery, magnetometer data, particle flux, and models to forecast flares, CMEs, geomagnetic storms, and radiation levels. Reliability, calibration, and clear communications are essential.
Improve lead time and accuracy of space weather alerts with transparent, actionable outputs for operators and grid stakeholders.
Solar imagery (SDO, SOHO), coronagraphs, magnetometers, in-situ particle measurements – the data foundation.
Event detection/classification, CME arrival prediction, time-series forecasting for Kp/Dst, radiation dose estimation, uncertainty quantification.
Alerting dashboards, APIs, severity levels, recommended actions, and integration to operations centers.
Recommended modes for satellites/grids/aviation – tailored responses based on risk assessments.
Adaptive observation planning for new events.
Implementation Blueprint
Frequently asked questions
What data sources does the AI system use?
The AI system utilizes a comprehensive suite of data, including SDO, SOHO, coronagraph imagery, magnetometer readings, and particle flux measurements alongside established space weather models.
How accurate are the predictions generated by the AI?
While the AI significantly improves prediction accuracy compared to traditional methods, it's crucial to acknowledge inherent uncertainties. The system provides confidence levels alongside its forecasts.
What is the role of human operators in the space weather alert process?
Human operators play a vital role in validating AI predictions, particularly for high-severity events, and ensuring appropriate actions are taken based on evolving conditions.
▶ Try it live
Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.