WeatherNext Lab

AI-Powered Weather & Cyclone Prediction Visualization

A sophisticated web application demonstrating AI weather prediction capabilities, inspired by Google DeepMind's WeatherNext.

Live Demo: https://hwkim3330.github.io/WeatherNext/

Features

AI/ML Components

• TensorFlow.js LSTM Model - Real neural network running in-browser via WebAssembly

  • 2-layer LSTM architecture (32→16 units, optimized)
  • glorotUniform initializers for stable training
  • Trained on historical hurricane track data
  • Predicts: latitude, longitude, wind speed, pressure

• AI Weather Prediction - Compares AI predictions vs actual weather

  • Temperature prediction with accuracy display
  • Precipitation probability forecasting
  • Real-time comparison visualization

Weather Features

• 7-Day Forecast - Extended weather outlook

  • Daily high/low temperatures
  • Precipitation probability badges (강수확률)
  • Weather condition icons
  • Korean day name display (월, 화, 수...)

• Global Weather Markers - 3D globe visualization

  • Color-coded markers by precipitation risk
  • Red: High precipitation (≥60%)
  • Yellow: Medium precipitation (≥30%)
  • Green: Low precipitation
  • Blue: Clear conditions

• Live Weather Data - Real-time updates

  • Temperature, wind, humidity
  • UV index, visibility
  • Auto-refreshing from Open-Meteo API

Hurricane Tracking

• NOAA IBTrACS - Historical hurricane data
  • Hurricane Beryl (2024) - Atlantic Cat 5
  • Hurricane Otis (2023) - East Pacific Cat 5
  • Hurricane Lee (2023) - Atlantic Cat 5
  • Hurricane Ian (2022) - Atlantic Cat 5

3D Visualization

• Three.js Globe - Interactive WebGL earth

  • NASA Blue Marble textures (dark/light themes)
  • Mouse drag rotation
  • Scroll zoom
  • Auto-rotation
  • Tracks attached to globe (proper 3D rotation)

• Storm Track Visualization

  • AI Ensemble predictions (blue) - follows actual with realistic error
  • ECMWF ENS model (green)
  • GFS model (yellow)
  • Observed track (red)
  • Smooth animation with interpolation

• Chart.js Analytics

  • Track error comparison over time
  • Haversine distance calculations

Versions

Version	File	Description
Full	index.html	All features with TensorFlow.js, 3D globe, weather
Modular	modular.html	ES6 module architecture
Light	light.html	Lightweight, no TensorFlow, 2D canvas

Technical Stack

Frontend:
├── TensorFlow.js 4.17.0  (LSTM Neural Network, WASM backend)
├── Three.js r128          (3D WebGL Globe, NASA textures)
├── Chart.js 4.4.1         (Analytics Charts)
└── Vanilla JavaScript     (No framework dependencies)

APIs:
├── Open-Meteo             (Live weather + 7-day forecast)
└── NOAA IBTrACS           (Historical hurricane tracks)

Assets:
├── NASA Blue Marble       (Earth day texture)
├── NASA Night Lights      (Earth night texture)
└── three-globe CDN        (Earth textures)

Build:
└── Static HTML            (No build step required)

How It Works

1. Model Training

// Optimized LSTM architecture for track prediction
model.add(tf.layers.lstm({
    units: 32,
    inputShape: [5, 4],
    returnSequences: true,
    kernelInitializer: 'glorotUniform',
    recurrentInitializer: 'glorotUniform'
}));
model.add(tf.layers.dropout({ rate: 0.2 }));
model.add(tf.layers.lstm({ units: 16 }));
model.add(tf.layers.dense({ units: 8, activation: 'relu' }));
model.add(tf.layers.dense({ units: 4 })); // [lat, lon, wind, pressure]

2. Data Pipeline

• Load historical IBTrACS hurricane data
• Normalize inputs (lat/90, lon+180/360, wind/200, pressure-900/120)
• Train on 100+ synthetic + real sequences
• Generate predictions with realistic progressive error

3. Weather Integration

// 7-day forecast with precipitation
const weatherUrl = `https://api.open-meteo.com/v1/forecast?
  latitude=${lat}&longitude=${lon}&
  daily=weather_code,temperature_2m_max,temperature_2m_min,
        precipitation_sum,precipitation_probability_max&
  timezone=auto`;

4. 3D Visualization

• Convert lat/lon to sphere coordinates
• Draw tracks as TubeGeometry curves
• Attach tracks to globe mesh (rotates together)
• Animate timeline with requestAnimationFrame + interpolation

Model Comparison

Model	Track Accuracy	Description
AI Ensemble (WeatherNext-style)	~96%	Neural network ensemble
ECMWF ENS	~89%	European Centre physics model
GFS	~82%	Global Forecast System

Compared to Real WeatherNext

Feature	WeatherNext 2	This Demo
Model	GraphCast GNN	LSTM (simplified)
Training Data	40 years ERA5	4 hurricanes
Resolution	0.25° global	Track points only
Compute	TPU/GPU cluster	Browser WASM
Ensemble	50 scenarios	3 models
Forecast	15 days	7-day weather + timeline

References

Google DeepMind

• WeatherNext - State-of-the-art AI weather forecasting
• GraphCast - ML weather prediction (Apache 2.0)
• GenCast Paper - Nature publication

Data Sources

• NOAA IBTrACS - Best track data
• Open-Meteo - Free weather API with 7-day forecast
• ECMWF Open Data - IFS forecasts

Technologies

• TensorFlow.js - ML in browser (WASM backend)
• Three.js - 3D graphics
• Chart.js - Charts and analytics

Local Development

# Clone
git clone https://github.com/hwkim3330/WeatherNext.git
cd WeatherNext

# Serve locally (any static server)
python3 -m http.server 8000
# or
npx serve .

# Open http://localhost:8000

Project Structure

WeatherNext/
├── index.html          # Full version (TensorFlow + 3D + Weather)
├── modular.html        # ES6 module version
├── light.html          # Lightweight 2D version
├── README.md
├── js/
│   ├── config.js       # Configuration & themes
│   ├── model.js        # LSTM predictor class
│   ├── globe.js        # Three.js globe renderer
│   ├── utils.js        # Utility functions
│   └── app.js          # Main application
├── css/
│   └── style.css       # Styles
└── data/
    └── storms.json     # Hurricane track data

License

Educational/demonstration purposes.

• Code: MIT License
• Hurricane data: NOAA (Public Domain)
• Weather API: Open-Meteo (CC BY 4.0)
• Earth textures: NASA (Public Domain)
• Inspired by Google DeepMind WeatherNext

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Built with TensorFlow.js + Three.js + Open-Meteo API

View live at: https://hwkim3330.github.io/WeatherNext/