README.md

metadata

title: RadarRedo
emoji: 🌦️
colorFrom: blue
colorTo: gray
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: gpl-3.0
short_description: Transform Canadian radar data to American color standards

🌦️ RadarRedo: Radar Reclassification System

A powerful system that transforms Canadian weather radar data to American color standards while preserving meteorological accuracy.

🔥 Key Features

• Real-time Radar Processing: Fetches live Canadian radar data from Environment Canada
• Intelligent Color Detection: Uses K-means clustering to identify unique radar colors
• DBZ Mapping: Maps colors to accurate reflectivity values (dBZ)
• Color Reclassification: Converts Canadian color scheme to American NWS standards
• Interactive Interface: Three-tab Gradio interface for exploration and analysis
• Color Analysis: Detailed analysis of radar color distributions

🛠️ Installation

1. Clone the repository:

   git clone <repository-url>
   cd RadarRedo
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Run the application:

   python app.py
   

4. Open your browser to http://localhost:7860

🎯 How It Works

1. Data Acquisition

• Fetches radar tiles from Environment Canada's MSC GeoMet WMS service
• Supports multiple radar types: Rain, Snow, Composite, 24h/6h precipitation

2. Color Detection & Analysis

• Uses K-means clustering to identify dominant colors in radar images
• Handles transparency and anti-aliasing artifacts
• Maps detected colors to corresponding DBZ (reflectivity) values

3. Color Reclassification

• Converts Canadian color scheme to American NWS standards
• Preserves meteorological data integrity during transformation
• Maintains accurate DBZ-to-color relationships

4. Visualization

• Interactive maps with original and reclassified radar overlays
• Color scale comparisons between Canadian and American standards
• Detailed color analysis and distribution reports

📊 Color Standards

Canadian (ECCC) Scale

• Environment and Climate Change Canada color scheme
• Optimized for Canadian meteorological standards
• Range: -30 to +70 dBZ

American (NWS) Scale

• National Weather Service color scheme
• Standard for US meteorological applications
• Range: -30 to +70 dBZ with different color mappings

🎮 Usage

Tab 1: Original Canadian Radar

• View real-time Canadian radar data
• Select different radar layers (Rain, Snow, Composite)
• Adjust opacity and update frequency

Tab 2: Reclassified Radar (American Colors)

• Generate reclassified radar with American color scheme
• Customize bounding box for specific geographic areas
• Compare side-by-side with original data

Tab 3: Color Analysis

• Analyze color distributions in radar images
• View detailed DBZ mappings
• Generate color scale comparison charts

🧪 Testing

Run the test suite to verify functionality:

python test_reclassification.py

This will test:

• Color detection accuracy
• DBZ mapping precision
• Color scale consistency
• Legend generation

🏗️ Architecture

RadarRedo/
├── app.py                    # Main Gradio application
├── radar_processor.py        # Core image processing logic
├── test_reclassification.py  # Test suite
├── requirements.txt          # Python dependencies
├── CLAUDE.md                # Development guidance
└── README.md                # This file

Core Components

• CanadianRadarApp: Main application class with Folium integration
• RadarImageProcessor: Image processing and color reclassification engine
• RadarColorScale: Predefined color standards for Canadian and American systems
• ColorDBZMapping: Data structure for color-to-reflectivity mappings

🌍 Geographic Coverage

• Primary Coverage: Canada and northern United States
• Data Source: Environment and Climate Change Canada (ECCC)
• Update Frequency: Every 10 minutes
• Resolution: 1km for radar data

🔧 Configuration

Bounding Box Format

Specify geographic bounds as: West, South, East, North

Example (Toronto area): -80.0, 43.0, -78.0, 45.0

Available Radar Layers

• RADAR_1KM_RRAI: Rain radar (1km resolution)
• RADAR_1KM_RSNO: Snow radar (1km resolution)
• RADAR_1KM_RDBR: Composite radar
• RDPA.24F_PR: 24-hour precipitation accumulation
• RDPA.6F_PR: 6-hour precipitation accumulation

📈 Performance

• Color Detection: ~1-2 seconds for 512x512 images
• Reclassification: ~2-3 seconds per image
• Memory Usage: ~50-100MB during processing
• Network: Dependent on WMS response times

🚀 Future Enhancements

• Support for additional radar color standards (European, Australian)
• Batch processing for multiple radar regions
• Historical radar data analysis
• Machine learning-based color prediction
• Real-time streaming updates

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Run tests: python test_reclassification.py
4. Submit a pull request

📄 License

GPL-3.0 License - see LICENSE file for details

🙏 Acknowledgments

• Environment and Climate Change Canada for radar data
• National Weather Service for color standard references
• Gradio team for the excellent interface framework

---

Built with ❤️ for the meteorological community