README.md

---
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**:
   ```bash
   git clone <repository-url>
   cd RadarRedo
   ```

2. **Install dependencies**:
   ```bash
   pip install -r requirements.txt
   ```

3. **Run the application**:
   ```bash
   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:

```bash
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**