Solar Weather Monitoring System

📋 Table of Contents

• Our Idea
• Presentation
• Documentation
• Team
• Technologies
• Appreciation

💡 Our Idea

Our objective is to develop a monitoring system for solar weather events such as solar flares, solar energetic particles (SEPs), geomagnetic storms, and ionospheric disturbances. The system aims to provide real-time alerts and impact simulations for different industries like space agencies, aviation, and power grids to better prepare for these events.

📄 Presentation

Presentation

📄 Documentation

1. Solar Flares:

What: Sudden bursts of electromagnetic radiation (X-ray/UV light) from the Sun, often linked to CMEs. Why Alert: Cause radio blackouts (GPS, aviation communication). Can damage satellites or disrupt power grids if extreme. Key Metrics: Class (B, C, M, X): X-class flares are the strongest. Peak X-ray flux (measured by GOES satellites). Data Sources: NASA’s GOES X-ray Flux data. Solar Dynamics Observatory (SDO) imagery.

2. Solar Energetic Particle (SEP) Events:

What: High-energy protons ejected during solar flares or CME shocks. Why Alert: Radiation risk for astronauts, high-altitude aviation, and satellites. Can cause "single-event upsets" (memory errors) in electronics. Key Metrics: Proton flux levels (≥10 MeV particles). NOAA’s Solar Radiation Storm Scale (S1-S5). Data Sources: ACE satellite’s EPAM instrument. NOAA’s Space Weather Prediction Center (SWPC).

The Helcats (Heliospheric Catalog of Events) dataset provides valuable information about solar events such as Coronal Mass Ejections (CMEs). This dataset can be used to analyze past solar activity, as well as predict the occurrence of future CMEs and their potential severity.

3. What is the Helcats Dataset?

The Helcats dataset includes:

• CME Data: Information on the timing, speed, and direction of CMEs.
• Event Classification: Each event is classified based on its observed characteristics (e.g., Halo CMEs).
• Solar Wind Data: Data that helps estimate the effect of CMEs on Earth’s magnetosphere.

The dataset is collected from solar observation satellites such as the SOHO and STEREO missions, and is maintained by the European Space Agency (ESA).

4. Predicting Future CMEs and Their Severity

Using the Helcats dataset, machine learning algorithms and statistical models can be applied to predict future CMEs. These predictions can be made based on:

1. Historical Patterns: By analyzing the properties of past CMEs, we can predict the likelihood of future events.
2. Solar Activity Monitoring: Continuous monitoring of solar activity can provide early warnings of impending CMEs.
3. Severity Assessment: By analyzing the size, speed, and magnetic properties of CMEs, we can estimate their potential severity and the impact on Earth (e.g., geomagnetic storms, radiation exposure, etc.).

5. Tools for Prediction

• Machine Learning Models: The Predictive model Random Forests is trained on historical CME data to predict future events.
• Helcats Dataset: Access real-time CME data to continuously monitor the Sun’s activity and trigger early warnings.

6. Example Use Cases:

1. Aviation: Predicting CMEs and their severity can help airlines prepare for potential disruptions in satellite communication and GPS systems.
2. Satellite Protection: Space agencies can use predictions to protect satellites by adjusting orbits and shutting down sensitive equipment.
3. Power Grid Management: Utility companies can prepare for geomagnetic storms that may affect power grids.

7. Data Sources:

• Helcats Dataset: The dataset can be accessed via the Helcats Database.
• NASA’s Space Weather Prediction Center: For real-time CME data and space weather predictions.

🌱 Team

1. Project Manager - VBKanev22

• GitHub profile: VBKanev22

2. Front-end Developer - Oktay Mehmed

• GitHub profile: Oktay Mehmed

3. Back-end Developer - trephy

• GitHub profile: trephy

⚒️ Tools

💻 Languages

Live Demo

https://www.infradash.space/

👏 Appreciation

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