Meteor Velocity Variations Research
A comprehensive research project investigating diurnal and seasonal variations of meteor velocity at middle latitude, combining advanced signal processing techniques with novel algorithmic approaches to analyze atmospheric phenomena.
Project Overview
This long-term research project (Jan 2022 - Sep 2024) focuses on understanding meteor velocity patterns using sophisticated data analysis and signal processing techniques. The research contributes to our understanding of atmospheric dynamics and meteor shower mechanisms.
Technical Implementation
Advanced Signal Processing
- Double-Gaussian Fitting Algorithm: Designed and implemented custom algorithm to identify dual peak meteor velocities (~28, 54 km/s)
- Multi-technique Signal Processing: Applied 5+ signal processing techniques for comprehensive data analysis
- Continuous Wavelet Transform: Implemented with tuned Morlet wavelets for high-resolution frequency analysis
- MATLAB/Python Integration: Utilized both platforms for optimal performance and analysis capabilities
Data Analysis & Methodology
- High-Resolution Analysis: Developed techniques for precise frequency analysis of meteor speed data
- Pattern Recognition: Identified systematic variations in meteor velocity patterns
- Statistical Modeling: Applied advanced statistical methods to understand underlying mechanisms
Key Research Findings
Scientific Discoveries
- Dual Peak Identification: Successfully identified and characterized dual peak meteor velocities at ~28 and 54 km/s
- Meteor Shower Impact: Discovered that meteor showers cause 45-97% number increase over background levels
- Seasonal Patterns: Documented systematic diurnal and seasonal variations in meteor characteristics
- Mechanism Understanding: Established causal relationships between meteor showers and velocity variations
Technical Achievements
- Novel Algorithm Development: Created innovative Double-Gaussian fitting approach
- Signal Processing Innovation: Advanced the field through sophisticated wavelet analysis techniques
- Data Processing Pipeline: Developed comprehensive system for processing large-scale meteor radar data
Collaboration & Supervision
International Research Team:
- Dr. Xianghui Xue - University of Science and Technology of China
- Dr. Wen Yi - University of Science and Technology of China
- Dr. Iain Reid - University of Adelaide, Australia
This international collaboration demonstrates experience in cross-institutional research and global scientific partnerships.
Technical Skills Demonstrated
Programming & Analysis
- MATLAB: Advanced signal processing and statistical analysis
- Python: Data processing and visualization
- Algorithm Development: Custom mathematical and statistical approaches
- Signal Processing: Wavelet analysis, spectral analysis, filtering techniques
Research Methodologies
- Time Series Analysis: Long-term data pattern recognition
- Statistical Modeling: Advanced statistical inference and hypothesis testing
- Data Validation: Quality control and verification of large datasets
- Scientific Communication: Documentation and presentation of complex findings
Project Repository
- Source Code: https://github.com/ktwu01/Meteor-Speed-Variations
- Documentation: Comprehensive code documentation and analysis notebooks
- Data Processing Pipeline: Complete workflow from raw data to scientific insights
Publications & Impact
This research contributed to peer-reviewed publications and presentations at major scientific conferences, advancing our understanding of meteor dynamics and atmospheric physics.
Research Significance
The project represents a significant contribution to atmospheric science and meteor research, combining innovative computational approaches with rigorous scientific methodology. The findings have implications for understanding atmospheric dynamics and space weather phenomena.
Skills and Expertise Gained
- Advanced Signal Processing: Expertise in sophisticated mathematical analysis techniques
- Research Leadership: Independent project management over 2+ years
- International Collaboration: Working effectively with global research teams
- Scientific Programming: Development of research-grade computational tools
- Data Science: Large-scale data analysis and pattern recognition