Cyclone Data Processing Pipeline for LLM Training

📋 Project Overview

This pipeline transforms raw IMD (Indian Meteorological Department) Best Tracks cyclone data into a comprehensive dataset suitable for Large Language Model (LLM) training, specifically designed for cyclone intensity prediction and tracking using transformers.

Original Dataset: c08063_Best Tracks__Data (1982-2024).xls
Final Output: LLM-ready dataset with rich text context
Time Period: 1982-2024 (43 years)
Data Retention: 8,070 records (86.6% retention rate)

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🗂️ Directory Structure

Cyclone_Processing_Pipeline/
│
├── data/
│   └── c08063_Best Tracks__Data (1982-2024).xls  # Original IMD data
│
├── scripts/
│   ├── 00_Data_Cleaning.py                    # Initial data cleaning
│   ├── final_cleanup.py                       # Final cleanup and validation
│   ├── 01_Data_Preprocessing_and_EDA.py       # EDA and analysis
│   ├── llm_data_cleaning.py                   # LLM-focused data extraction
│   ├── analyze_text_patterns.py               # Text pattern analysis
│   └── analyze_llm_dataset.py                 # LLM dataset analysis
│
├── output/
│   └── [Generated output files will be here]
│
├── logs/
│   └── pipeline_YYYYMMDD_HHMMSS.log          # Pipeline execution logs
│
├── README.md                                  # This file
└── LLM_Data_Cleaning_Methodology.md          # Detailed methodology

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🚀 Quick Start

Prerequisites

• Python 3.8+
• Required packages: pandas, numpy, matplotlib, seaborn, openpyxl
• Virtual environment (recommended)

Installation

# Clone or navigate to the project directory
cd Cyclone_Processing_Pipeline

# Install required packages
pip install pandas numpy matplotlib seaborn openpyxl

Running the Pipeline

# Option 1: Run complete pipeline automatically
python run_pipeline.py
# All output will be saved to logs/pipeline_YYYYMMDD_HHMMSS.log

# Option 2: Run individual steps
cd scripts

# Step 1: Initial data cleaning
python 00_Data_Cleaning.py

# Step 2: Final cleanup and validation
python final_cleanup.py

# Step 3: EDA and analysis
python 01_Data_Preprocessing_and_EDA.py

# Step 4: LLM data extraction
python llm_data_cleaning.py

# Step 5: Text pattern analysis
python analyze_text_patterns.py

# Step 6: LLM dataset analysis
python analyze_llm_dataset.py

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📊 Processing Pipeline Overview

Phase 1: Initial Data Cleaning (00_Data_Cleaning.py)

Objective: Clean the raw Excel data while preserving maximum information

Key Features:

• Multi-sheet Excel processing (43 sheets, one per year)
• Header standardization across different years
• Data type validation and cleaning
• Time format standardization (HHMM)
• Geographic coordinate validation
• Meteorological parameter validation

Output: cleaned_cyclone_data.csv

Phase 2: Final Cleanup (final_cleanup.py)

Objective: Remove unwanted columns and finalize dataset structure

Key Features:

• Remove duplicate and corrupted columns
• Reorder columns in logical sequence
• Final data quality validation
• Duplicate row removal
• Dataset summary generation

Output: cleaned_cyclone_data_final.csv

Phase 3: EDA and Analysis (01_Data_Preprocessing_and_EDA.py)

Objective: Comprehensive exploratory data analysis

Key Features:

• Temporal analysis (yearly, monthly patterns)
• Geographic analysis (basin distribution)
• Intensity analysis (grade distribution)
• Correlation analysis
• Visualization generation
• Data quality assessment

Output: Multiple analysis plots and statistics

Phase 4: LLM Data Extraction (llm_data_cleaning.py)

Objective: Create LLM-ready dataset with rich text context

Key Features:

• Text annotation extraction and classification
• Merged content preservation
• Context text generation
• Rich feature engineering
• Multi-format data preservation

Output: llm_cyclone_dataset.csv (8,070 records, 23 columns)

Phase 5: Text Pattern Analysis (analyze_text_patterns.py)

Objective: Analyze text patterns in specific sheets (2009, 2024)

Key Features:

• Text pattern identification
• Merged content analysis
• Annotation type classification
• Pattern statistics generation

Output: text_patterns_analysis.csv, merged_content_analysis.csv

Phase 6: LLM Dataset Analysis (analyze_llm_dataset.py)

Objective: Comprehensive analysis of the final LLM dataset

Key Features:

• Rich text content analysis
• Context length statistics
• Annotation type distribution
• Visualization generation
• Training example extraction

Output: Multiple analysis files and visualizations

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📈 Data Quality Metrics

Metric	Value	Description
Data Retention Rate	86.6%	8,070 out of 8,928 original rows
Text Annotation Coverage	8.1%	651 records with text annotations
Merged Content Coverage	3.2%	258 records with merged content
Average Context Length	155.3 chars	Rich context for LLM training
Annotation Types	5	CROSSING_EVENT, WEAKENING_EVENT, etc.
Time Period	43 years	1982-2024 complete coverage
Unique Cyclones	115	Diverse cyclone patterns

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🎯 LLM Training Features

Structured Features

1. Basic Cyclone Data: Year, Name, Basin, Date, Time
2. Geographic Data: Latitude, Longitude
3. Meteorological Data: Grade, Wind_Speed, Central_Pressure, CI_Number
4. Derived Features: Pressure_Drop, Outermost_Isobar, Size

Text Features

1. Text_Annotations: Structured text annotations with type classification
2. Annotation_Count: Number of annotations per record
3. Merged_Content: Multi-column text content
4. Has_Merged_Content: Boolean flag for merged content
5. Context_Text: Rich natural language context
6. Context_Length: Character count for context richness

Annotation Types Discovered

• CROSSING_EVENT: 235 instances (landfall descriptions)
• WEAKENING_EVENT: 295 instances (intensity changes)
• COORDINATE_REFERENCE: 78 instances (geographic details)
• TIME_REFERENCE: 41 instances (temporal information)
• LOCATION_REFERENCE: 8 instances (place names)

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🚀 Applications for Cyclone Prediction

1. Intensity Prediction

• Input: Historical cyclone data + text descriptions
• Output: Predicted intensity changes (wind speed, pressure)
• Model: Transformer with sequence modeling

2. Path Tracking

• Input: Current cyclone state + historical path
• Output: Predicted path coordinates and landfall location
• Model: Transformer with geographic attention

3. Natural Language Generation

• Input: Structured cyclone data
• Output: Natural language cyclone reports
• Model: Transformer with text generation capabilities

4. Knowledge Extraction

• Input: Historical cyclone descriptions
• Output: Structured meteorological insights
• Model: Transformer with information extraction

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📁 Output Files

Main Datasets (3 CSV Files)

1. output/cleaned_cyclone_data.csv (564KB, 7,715 records)

   • Purpose: Initial cleaned dataset after basic data cleaning
   • Significance: First step in data quality improvement, contains standardized headers and cleaned data types
   • Use Case: Baseline for comparison with final dataset

2. output/cleaned_cyclone_data_final.csv (491KB, 7,715 records)

   • Purpose: Final cleaned dataset with optimized structure
   • Significance: Removes duplicate/corrupted columns, reorders for logical sequence
   • Use Case: Primary dataset for traditional cyclone analysis and modeling

3. output/llm_cyclone_dataset.csv (2.1MB, 8,070 records)

   • Purpose: LLM-ready dataset with rich text context
   • Significance: Core training dataset for transformer models with structured + text features
   • Use Case: Primary dataset for cyclone intensity prediction and tracking with transformers

Analysis Files (6 CSV Files)

4. output/rich_text_records.csv (351KB, 668 records)

   • Purpose: Subset of records containing rich text annotations
   • Significance: High-value training examples with natural language context
   • Use Case: Fine-tuning models on text-rich cyclone events

5. output/training_examples.csv (26KB, 102 records)

   • Purpose: Curated sample of diverse training examples
   • Significance: Representative examples showing different annotation types and patterns
   • Use Case: Model validation, testing, and demonstration

6. output/dataset_summary.csv (247B, 3 records)

   • Purpose: Statistical summary of the complete dataset
   • Significance: Quick overview of data quality, coverage, and distribution
   • Use Case: Data quality assessment and reporting

7. output/text_patterns_analysis.csv (17KB, 688 records)

   • Purpose: Detailed analysis of text patterns in specific sheets (2009, 2024)
   • Significance: Understanding text annotation patterns and classification
   • Use Case: Improving text extraction algorithms and pattern recognition

8. output/merged_content_analysis.csv (9.6KB, 64 records)

   • Purpose: Analysis of merged cell content across sheets
   • Significance: Preserves complex multi-column text information
   • Use Case: Enhanced context generation for LLM training

9. output/processed_cyclone_data.csv (786KB, 7,715 records)

   • Purpose: Intermediate processed dataset with enhanced features
   • Significance: Contains derived features and enhanced data structure
   • Use Case: Feature engineering and advanced analysis

Log Files

• logs/pipeline_YYYYMMDD_HHMMSS.log: Complete pipeline execution logs with timestamps

Visualizations

• cyclone_eda_plots.png: EDA visualizations
• correlation_heatmap.png: Correlation analysis
• time_series.png: Temporal analysis
• geographic_distribution.png: Geographic analysis
• llm_dataset_analysis_fixed.png: LLM dataset analysis

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🔧 Technical Details

Key Libraries Used

• pandas: Data manipulation and cleaning
• numpy: Numerical operations
• matplotlib/seaborn: Data visualization
• openpyxl: Excel file processing
• re: Regular expressions for text processing

Processing Pipeline

1. Sheet-by-Sheet Processing: Handle each year's data individually
2. Header Detection: Automatically identify and standardize headers
3. Text Extraction: Preserve all descriptive text content
4. Data Standardization: Consistent column names and data types
5. Context Generation: Create rich context for LLM training
6. Quality Validation: Ensure data integrity and completeness

Performance Considerations

• Memory Efficient: Process sheets individually to manage memory
• Scalable: Methodology applicable to larger datasets
• Reproducible: Consistent results across different runs
• Logging: Complete execution logs saved for debugging and reproducibility

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🎯 Success Metrics

Data Quality Achievements

✅ 86.6% Data Retention: Preserved most original data
✅ 100% Text Content Preserved: All annotations and merged content extracted
✅ 5 Annotation Types: Comprehensive text classification system
✅ Rich Context: Average 155.3 characters of context per record
✅ 43-Year Coverage: Complete temporal coverage (1982-2024)
✅ 115 Unique Cyclones: Comprehensive cyclone diversity

LLM Training Readiness

✅ Structured + Text Data: Perfect for transformer model training
✅ Rich Context: Natural language descriptions for each record
✅ Diverse Patterns: Multiple annotation types and content styles
✅ Temporal Patterns: Long-term cyclone evolution data
✅ Geographic Coverage: Multiple basins and regions

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🚀 Future Enhancements

Potential Improvements

1. Advanced Text Classification: Machine learning-based annotation classification
2. Semantic Analysis: Extract deeper meaning from text descriptions
3. Multi-Modal Features: Incorporate satellite imagery data
4. Real-Time Processing: Stream processing for live cyclone data
5. Cross-Dataset Integration: Combine with other meteorological datasets

LLM Training Applications

1. Fine-tune Large Language Models: Specialized cyclone prediction models
2. Develop Chatbots: Interactive cyclone information systems
3. Automated Report Generation: Natural language cyclone reports
4. Knowledge Discovery: Extract patterns from historical data
5. Educational Tools: AI-powered cyclone learning systems

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📝 Usage Examples

For Intensity Prediction

import pandas as pd

# Load the LLM dataset
df = pd.read_csv("output/llm_cyclone_dataset.csv")

# Filter for intensity-related annotations
intensity_data = df[df['Text_Annotations'].str.contains('WEAKENING_EVENT', na=False)]

# Use for transformer training
# Input: Context_Text + structured features
# Output: Next intensity prediction

For Path Tracking

# Filter for crossing events
tracking_data = df[df['Text_Annotations'].str.contains('CROSSING_EVENT', na=False)]

# Use for path prediction
# Input: Historical coordinates + text context
# Output: Next position prediction

For Model Training

# Load the complete dataset
df = pd.read_csv("output/llm_cyclone_dataset.csv")

# Prepare training data
training_data = df[df['Context_Length'] > 50].copy()

# Use for transformer fine-tuning
# This dataset is ready for cyclone prediction model training

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🤝 Contributing

This pipeline is designed to be:

• Reproducible: All steps are documented and automated
• Extensible: Easy to add new processing steps
• Maintainable: Clear code structure and documentation
• Scalable: Applicable to larger datasets

For questions or improvements, please refer to the detailed methodology in LLM_Data_Cleaning_Methodology.md.

👨‍💻 Development

This cyclone data processing pipeline was developed to address the challenges of transforming raw meteorological data into a format suitable for modern transformer-based models. The pipeline incorporates advanced text extraction techniques, data cleaning methodologies, and context generation to create a comprehensive dataset for cyclone intensity prediction and tracking.

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📄 License

This project is developed for cyclone prediction research using transformers. The methodology and scripts are provided for research and educational purposes.

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📞 Contact

For questions about the cyclone data processing pipeline or LLM training methodology, please refer to the comprehensive documentation in LLM_Data_Cleaning_Methodology.md.

🎯 Project Achievements

Total Processing Time: ~15 minutes
Data Quality Score: 86.6% retention with 100% text preservation
LLM Training Readiness: Excellent (8,070 rich context records)

This pipeline successfully demonstrates the potential of combining structured meteorological data with natural language processing techniques for advanced cyclone prediction systems.