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A.I to predict SuperStorms, Solar Storms

Idea Proposed

Severe thunderstorms are unpredictable and can cause massive damage. Traditional weather models struggle with accurately forecasting their timing, intensity, and impact. This uses ensemble deep learning and radar data to improve predictions, integrating Vision Transformers (ViTs), CNNs, and LSTMs for more accurate storm tracking.

A physics-guided AI model further refines storm travel time and intensity forecasts. This approach enhances early warnings, storm tracking, and decision-making, making severe weather predictions more reliable and actionable.


How This Works:

  1. Active Region (AR) Classification:

    • Uses Vision Transformers (ViTs) to classify sunspot regions, identifying morphological properties linked to severe weather events.
    • Deep learning enables automated classification based on remote sensing images.

  2. Flare Forecasting:

    • A video-based deep learning model (CNNs + LSTM) processes historical radar and magnetogram data.
    • Predicts whether a flare (storm-triggering event) will occur within the next 24 hours.
    • Achieves high accuracy in warning about severe weather events.

  3. CME (Coronal Mass Ejection) Travel Time Prediction:

    • A physics-driven AI model integrates deterministic physics constraints (drag-based equations) into neural networks.
    • Predicts how long it will take for an extreme weather event (CME equivalent to a storm system) to reach the affected area.

  4. Geomagnetic Storm Impact Forecasting:

    • Uses an LSTM-based machine learning model trained on historical storm data.
    • Takes in meteorological measurements (solar wind speed, density, and magnetic field values) to predict the intensity of the storm.
    • Can accurately predict both the onset and recovery phases of extreme weather events.

How You Can Use This:

  1. Weather Prediction and Warnings:

    • Implement a similar deep learning + radar-based approach to predict thunderstorms.
    • Train on historical radar data and storm events.

  2. AI-Based Early Warning Systems:

    • Use CNN-LSTM models to predict storm severity within 24-hour windows.
    • Apply ensemble learning techniques to reduce false alarms.

  3. Hybrid AI-Physics Models for Storm Tracking:

    • Combine AI with physical weather models to improve prediction accuracy.
    • Apply physics-based loss functions to guide neural networks.

  4. Improving Storm Impact Forecasting:

    • Train models using real-time weather sensor data to predict storm intensity and impact.
    • Utilize LSTM and recurrent models to analyze sequential weather patterns.

Implementation

If you have considered implementing this in may 2024 superstorm, find the paper here -

Artificial Intelligence Could Have Predicted All Space Weather Events Associated with the May 2024 Superstorm: https://doi.org/10.48550/arXiv.2501.14684

Sources & citation

Artificial Intelligence Could Have Predicted All Space Weather Events Associated with the May 2024 Superstorm: https://doi.org/10.48550/arXiv.2501.14684