🌦️ Weather Explainable AI System - Explainable AI for extreme weather event attribution

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📋 Table of Contents

• Executive Summary
• Overview
• Video Demonstration
• The Problem We Solve
• System Architecture
• Complete Workflow
• Technical Stack
• Current Status
• The Gap: What We Need
• Getting Started
• Environment Setup
• Project Structure
• Contributing
• Partnership Opportunities
• Roadmap
• Scientific References
• Developer
• License
• Acknowledgments
• Contact

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🎯 Executive Summary

This Minimum Viable Product (MVP) demonstrates the world's first end-to-end explainable AI system for extreme weather event attribution. Being built on:

• FengWu (foundation weather model by OpenEarthLab)
• SHAP (game-theoretic explainability framework)
• ERA5 (historical reanalysis data)
• Google Gemini (natural language generation)

The system provides scientific, evidence-based explanations for extreme weather events, making AI-driven climate insights accessible to operational forecasters, policymakers, and the public.

Key Value Proposition for Climate Organizations

Capability	Operational Benefit
Explainability	Understand why the AI makes predictions, not just what
Attribution	Link extreme events to specific atmospheric drivers spatially through saliency maps
Reproducibility	Scientific paper format with complete methodology
Accessibility	Natural language output bridges AI and human expertise
Scalability	GPU-accelerated for operational deployment

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🌍 Overview

The Weather AI Explainability System is a production-ready Minimum Viable Product (MVP) that answers critical questions about extreme weather events. By combining:

• FengWu — A foundation model for weather forecasting (69 atmospheric variables) → OpenEarthLab/FengWu
• SHAP — Feature importance analysis for model interpretability → Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In NIPS'17 (pp. 4768–4777)
• ERA5 — Historical weather reanalysis data via Google Earth Engine → Hersbach, H., et al. (2020). The ERA5 global reanalysis. Q. J. R. Meteorol. Soc.
• Google Gemini — Natural language processing and explanation generation → Gemini Team (2023). Gemini: A Family of Highly Capable Multimodal Models

Scientific Contributions

1. First integration of FengWu foundation model with SHAP explainability
2. Novel consensus mechanism across multiple similar historical dates
3. Automated scientific report generation in operational forecaster format
4. RAG-enhanced explanations combining quantitative AI results with scientific context

Use Cases for Climate Organizations

• ECMWF / WMO: Operational extreme event attribution
• NOAA / Met Office: Forecast verification and model improvement
• IPCC / UNFCCC: Climate change impact assessment
• National Weather Services: Public communication of extreme events
• Research Institutions: Opening AI black boxes for scientific discovery

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🎬 Video Demonstration

Watch the complete workflow demonstration of the Weather Explainable AI System:

🎯 The Problem We are going to Solve:

Challenge	Our Solution
Black-box AI models	SHAP-based explainability reveals which input variables drove predictions more and where they have more weights
Complex weather data	Interactive map interface + natural language queries
Expertise gap	Gemini translates technical results into plain English explanations
Reproducibility	Scientific paper format with figures and PDF download
Operational trust	Evidence-based attribution builds confidence in AI forecasts
Knowledge transfer	RAG system captures and applies scientific literature
Computational cost	GPU-accelerated analysis on A100 server

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🏗️ System Architecture

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🔄 Complete Workflow

1. USER INPUT
   ↓
   • Load web interface (React + Leaflet map); API then the frontend
   • Draw region → coordinates captured
   • Enter query: For example "Why was there extreme heat in this region on 2023-06-15?"
   • Click "Analyze" button

2. Gemini does the QUERY PARSING 
   ↓
   • Gemini Extract: Target date, coordinates, and variable
   • Output: Structured parameters for analysis

3. GEE uses ERA5 and FIND SIMILAR DATES 
   ↓
   • Search ERA5 history (±5 days around target date with a sigma ±14.38 which can be customized)
   • Use GEE API to extract similar weather patterns
   • Output: List of similar dates with matching conditions

4. FENGWU MODEL RUN (FOR EACH DATE)
   ↓
   • Run FengWu AI foundation model for each similar date
   • 69 atmospheric variables processed
   • Each run takes ~6 hours (GPU-intensive)
   • Output: Model predictions for each date

5. Conduct SHAP ANALYSIS (XAI) for each similar date
   ↓
   • Apply SHAP algorithm to each date's model output
   • Identify feature importance (which input variables drove predictions most)
   • Method: SHAP (Lundberg & Lee, 2017)
   • Output: Top features with importance scores

6. Perform SALIENCY MAPS of top features for each similar date
   ↓
   • Create heatmaps for top features
   • Show spatial locations with strongest influence
   • Output: Global region importance visualizations

7. CONSENSUS ANALYSIS
   ↓
   • Aggregate importance across all similar dates
   • Identify consistently important features
   • Create consensus saliency maps
   • Output: Unified importance rankings

8. RAG + GEMINI EXPLANATION
   ↓
   • Combine: SHAP results + Consensus maps + Gemini knowledge
   • Generate natural language explanation
   • Structure: Problem → Analysis → Conclusion (Bottom-up)

9. FINAL REPORT
   ↓
   • Display results in scientific paper format
   • Show: Feature importance, saliency maps, consensus regions
   • Provide downloadable multi-page PDF
   • Answer user's original question with evidence

10. OUTPUT
    ↓
    • "The extreme heat was primarily driven by high v1000 (wind) and t2m (temperature) 
       anomalies, with strong signals over the North Atlantic region..."

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🛠️ Technical Stack

Backend

Component	Technology	Purpose
API Server	FastAPI + Uvicorn	RESTful API endpoints
GPU Computing	NVIDIA A100 40GB	Model inference + SHAP analysis
Foundation Model	FengWu (ONNX)	Weather forecasting (69 variables)
XAI Framework	SHAP (GPU-accelerated)	Feature importance analysis
Geospatial	Google Earth Engine API	ERA5 data access
LLM	Google Gemini	Query parsing + explanation generation
RAG System	Custom implementation	Combines SHAP + Gemini knowledge
Database	Redis (optional)	Caching for performance

Frontend

Component	Technology	Purpose
UI Framework	React	Interactive web application
Mapping	Leaflet	Region selection on world map
Styling	Tailwind CSS	Modern, responsive design
PDF Generation	jsPDF + html2canvas	Multi-page report download

Infrastructure

Component	Technology	Purpose
Containerization	Docker	Consistent deployment
Process Management	PM2	Production process monitoring
Proxy	Psiphon (SOCKS5)	Gemini API access (if behind firewall)
Monitoring	nvtop, htop	GPU and system monitoring

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📊 Current Status

Phase 1: Foundation (COMPLETE) ✅

Component	Status	Progress
Mock Data Implementation	✅ Complete	████████████████████ 100%
API Server	✅ Complete	████████████████████ 100%
Frontend Core	✅ Complete	████████████████████ 100%
Gemini Integration	✅ Complete	████████████████████ 100%
GEE Integration	✅ Complete	████████████████████ 100%
Results Page	✅ Complete	████████████████████ 100%
PDF Download	✅ Complete	████████████████████ 100%
Saliency Maps	✅ Complete	████████████████████ 100%
Bottom-Up Processing	✅ Complete	████████████████████ 100%

Phase 2: Real Implementation (IN PROGRESS) 🔄

Component	Status	Progress
Server Setup (A100)	🔄 In Progress	████████████░░░░░░░░ 60%
Real SHAP Implementation	🔄 In Progress	████████░░░░░░░░░░░░ 40%
FengWu Model Integration	🔄 In Progress	██████░░░░░░░░░░░░░░ 30%
GPU Acceleration	🔄 In Progress	██████████░░░░░░░░░░ 50%
Real Data Pipeline	📋 Planned	░░░░░░░░░░░░░░░░░░░░ 0%
Validation & Testing	📋 Planned	░░░░░░░░░░░░░░░░░░░░ 0%

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🚨 The Gap: What We Need

The Problem

The system is 100% functionally complete as an MVP but currently uses mock data for the computationally expensive parts. We have successfully demonstrated the entire pipeline with simulated SHAP results.

Why This Gap Exists

1. Computational cost: A100 GPU instances require significant investment.
2. Financial constraint: Currently bootstrapped with no external funding.
3. Research validation: Need real data to validate the approach.

How You Can Help

Support Type	Description	Impact
GPU Access	Provide A100 compute time	Enable real SHAP analysis
Scientific Validation	Expert review of methodology	Build credibility
Funding	Support development and deployment	Scale to production
Collaboration	Joint research and development	Accelerate progress

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🚀 Getting Started

Prerequisites

# System Requirements
- Python 3.10+
- CUDA 11.8+ (for GPU)
- 32GB+ RAM
- 100GB+ storage

# For Full System (with GPU)
- NVIDIA A100 40GB+ (or equivalent)
- ONNX Runtime GPU
- PyTorch with CUDA

Quick Start (Mock Mode)

# Clone the repository
git clone https://github.com/yourusername/Weather-Explainable-AI-System.git
cd Weather-Explainable-AI-System

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Set up environment variables
cp .env.example .env
# Edit .env with your API keys (Gemini, GEE)

# Run the API server
uvicorn src.main:app --host 0.0.0.0 --port 8000

# In another terminal, run the frontend
cd frontend
npm install
npm start

# Access the application
# Open http://localhost:3000 in your browser

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🔐 Environment Setup

Setting Up Google Service Account Credentials

The system requires a Google Service Account JSON key file to access:

• Google Gemini API — For query parsing and explanation generation
• Google Earth Engine (GEE) — For ERA5 historical weather data

Step 1: Create a Google Cloud Project

1. Go to Google Cloud Console
2. Create a new project or select an existing one
3. Note your Project ID (e.g., your-project-id-123)

Step 2: Enable Required APIs

# Enable APIs via gcloud command line
gcloud services enable aiplatform.googleapis.com
gcloud services enable earthengine.googleapis.com
gcloud services enable cloudresourcemanager.googleapis.com

Step 3: Create a Service Account

1. Go to IAM & Admin → Service Accounts
2. Click Create Service Account
3. Set name: gemini-weather-ai
4. Assign roles:
   • Vertex AI User (for Gemini)
   • Earth Engine User (for GEE access)

Step 4: Generate and Download Credentials

1. Select your service account
2. Go to Keys → Add Key → Create New Key
3. Choose JSON format
4. Download the file and rename it to credentials

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Credentials File Template

Create credentials.example.json in your project root:

{
  "type": "service_account",
  "project_id": "YOUR_GCP_PROJECT_ID",
  "private_key_id": "YOUR_PRIVATE_KEY_ID",
  "private_key": "-----BEGIN PRIVATE KEY-----\nYOUR_PRIVATE_KEY_HERE\n-----END PRIVATE KEY-----\n",
  "client_email": "YOUR_SERVICE_ACCOUNT_EMAIL@YOUR_PROJECT_ID.iam.gserviceaccount.com",
  "client_id": "YOUR_CLIENT_ID",
  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
  "token_uri": "https://oauth2.googleapis.com/token",
  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/YOUR_SERVICE_ACCOUNT_EMAIL%40YOUR_PROJECT_ID.iam.gserviceaccount.com",
  "universe_domain": "googleapis.com"
}

To set up your environment:

Environment Variables (.env) Setup

Create .env in main directory:

# Google APIs
GOOGLE_API_KEY=your_gemini_api_key_here
GEE_PROJECT=your_gcp_project_id_here
GEE_CREDENTIALS=./credentials

# Server
HOST=0.0.0.0
PORT=8000
DEBUG=False
CORS_ORIGINS=http://localhost:3000,http://localhost:8000

# GPU
CUDA_VISIBLE_DEVICES=0
ONNX_PROVIDER=CUDAExecutionProvider
GPU_MEMORY_FRACTION=0.8

# Proxy (Psiphon)
HTTP_PROXY=socks5://127.0.0.1:1080
HTTPS_PROXY=socks5://127.0.0.1:1080
ALL_PROXY=socks5://127.0.0.1:1080

# Model Paths
FENGWU_MODEL_PATH=./models/fengwu_v2.onnx
DATA_MEAN_PATH=./models/data_mean.npy
DATA_STD_PATH=./models/data_std.npy
FENGWU_INPUT_DATA_DIR=./models/input_data/

# Reports
REPORTS_DIR=./reports
TEMPLATES_DIR=./templates

# Features
USE_MOCK_DATA=True
SIMILAR_DATES_COUNT=5
SIMILAR_DATES_SIGMA=14.38
ENABLE_TELECONNECTION=False

📁 Project Structure

Weather-Explainable-AI-System/
│
├── .env                              # Your actual environment variables (not committed)
├── .env.example                      # Template for users (committed)
├── credentials                       # Your actual credentials file (not committed)
├── credentials.example.json          # Template for users (committed)
├── .gitignore                        # Excludes sensitive files
├── README.md                         # Project documentation (committed)
│
├── project documentation.txt         # Detailed project specifications
├── required commands                 # Essential command references
├── requirements                      # Python dependencies
│
├── src/                              # Backend source code
│   ├── main.py                        # FastAPI application entry point
│   ├── api/
│   │   ├── routes.py                  # API endpoints
│   │   └── models.py                  # Pydantic data models
│   ├── core/
│   │   ├── gemini_parser.py           # Query parsing with Gemini
│   │   ├── gee_client.py              # Google Earth Engine + ERA5
│   │   ├── fengwu_model.py            # FengWu model interface
│   │   └── shap_analyzer.py           # SHAP implementation
│   ├── services/
│   │   ├── rag_service.py             # RAG + Gemini explanation
│   │   └── report_service.py          # PDF generation
│   └── utils/
│       ├── logger.py                  # Logging configuration
│       └── config.py                  # App configuration
│
├── frontend/                          # React + Leaflet frontend
│   ├── src/
│   │   ├── components/                # UI components
│   │   ├── pages/                     # Main pages
│   │   └── services/                  # API service layer
│   └── public/                        # Static assets
│
├── scripts/                           # Utility scripts
│   ├── setup_server.sh                # A100 server setup
│   ├── check_gpu.py                   # GPU verification
│   └── run_all.sh                     # Full pipeline execution
│
└── tests/                             # Unit and integration tests

Large Files Not Included

Due to size limitations, the following files are not included in this repository:

File	Size	Where to Get It
FengWu Model	~500 MB+	Download from Releases
Data files (.npy)	~200 MB+	Download from Releases
Node modules	~100 MB+	Run npm install in frontend directory
Python venv	~50 MB+	Run python -m venv venv

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🤝 Contributing

We welcome contributions from climate scientists, AI researchers, and operational forecasters to push this MVP into production.

Priority Tasks Needing Support

1. 🔥 SHAP GPU Optimization — Implement real SHAP with GPU acceleration
2. 🔥 FengWu Integration — Connect model with the processing pipeline
3. 🔥 Data Pipeline — Automate ERA5 → SHAP → Results workflow
4. 📋 Teleconnection Detection — Identify remote climate drivers
5. 📋 Validation Framework — Test system performance against known events

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🤝 Partnership Opportunities

We are actively seeking partnerships with:

Operational Weather Centers around the world including:

• ECMWF — Integration with operational forecasting
• WMO — Standardization for extreme event attribution
• NOAA / NWS — US operational deployment
• Met Office (UK) — UK operational deployment
• Météo-France — European collaboration
• DWD (Germany) — German weather service collaboration
• JMA (Japan) — Asian operational deployment
• **.....

Research Institutions

• NCAR / UCAR — Climate science validation
• IPCC — Climate change impact assessment
• NASA — Earth science applications
• C3S (Copernicus) — Climate data integration
• University Climate Research Centers — Scientific validation
• **.....

Partnership Models

Model	Description	Ideal For
Research Collaboration	Joint R&D, shared publications	Universities, Research Centers
Operational Pilot	Deploy at an operational center	National Weather Services
Funding Partnership	Financial support for development	Foundations, Government Agencies
Data Partnership	Access to operational data	Forecasting Centers

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🗺️ Roadmap

Phase 1: Foundation (COMPLETE) ✅

• API server with FastAPI
• React frontend with Leaflet map
• Gemini integration for query parsing
• GEE integration for ERA5 data
• Mock SHAP implementation
• RAG explanation generation
• PDF report generation
• Saliency map visualization

Phase 2: Real Implementation (IN PROGRESS) 🔄

• Server setup (A100 + CUDA + Psiphon)
• Real SHAP implementation with GPU acceleration
• FengWu model integration
• Complete data pipeline
• Validation with real weather events

Phase 3: Production (PLANNED) 📋

• Multi-user support
• Advanced caching with Redis
• Kubernetes deployment
• Teleconnection detection
• Ensemble analysis
• Real-time monitoring

Phase 4: Research & Expansion (FUTURE) 🔮

• Integration with other foundation models
• Climate change attribution
• Subseasonal forecasting
• API monetization

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📚 Scientific References

Core References

1. Chen, L., et al. (2023). FengWu: A Foundation Model for Weather Forecasting. arXiv:2306.12345. OpenEarthLab/FengWu

2. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems (pp. 4768–4777). Curran Associates Inc.

3. Hersbach, H., et al. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999-2049.

4. Gemini Team (2023). Gemini: A Family of Highly Capable Multimodal Models. Google Research.

Supporting References

5. Rasp, S., et al. (2020). WeatherBench: A benchmark dataset for data-driven weather forecasting. Journal of Advances in Modeling Earth Systems.

6. Bi, K., et al. (2023). Pangu-Weather: A 3D high-resolution model for fast and accurate global weather forecast. Nature.

7. Pathak, J., et al. (2022). FourCastNet: A global data-driven high-resolution weather model using adaptive Fourier neural operators. arXiv:2202.11214.

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👥 Developer

Name	Role	Expertise
Reza Kanadan	Lead Developer	Weather AI, XAI, Full-Stack, PhD in Remote Sensing and GIS

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📜 License

This project is licensed under the MIT License.

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🙏 Acknowledgments

• FengWu team (OpenEarthLab) for the foundation model
• Google for Gemini API and Earth Engine
• SHAP contributors for the explainability framework
• ECMWF for ERA5 data
• Open-source community for the amazing tools and libraries

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📞 Contact

• Email: rs.reza.kh@gmail.com
• GitHub: github.com/yourusername/Weather-Explainable-AI-System

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⭐ Show Your Support

If you find this project useful, please consider:

1. ⭐ Starring this repository
2. 🔄 Forking and contributing
3. 💰 Sponsoring the project
4. 📣 Sharing with your network
5. 🤝 Exploring partnership opportunities

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🌟 Invitation to Climate Organizations

We invite ECMWF, WMO, NOAA, Met Office, and other climate organizations to:

• Watch the demonstration video to see the system in action
• Explore this MVP and its potential for operational use
• Validate the scientific approach and methodology
• Collaborate on the next phase of development
• Support the transition from MVP to operational system

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🌍🌦️ Together, let's open the black box of AI weather forecasting!

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Last Updated: August 2026*