Plan your future, historically.
An advanced weather analytics dashboard powered by NASA Earth Observation Data.
Planning outdoor events — be it a vacation, hike, wedding, or local parade — months in advance is a gamble against the weather.
Standard weather forecasts are only reliable for a few days, leaving long-term plans uncertain.
While historical averages exist, they rarely answer specific questions like:
- “What’s the actual chance of a dangerously hot day for my July beach trip?”
- “Which week in September has the highest probability of sunny, cool, and dry weather?”
- “Should I book my ski trip in early or late December?”
WeatherProb transforms uncertainty into quantifiable probability.
It’s not a forecast — it’s a historical odds calculator.
By analyzing decades of NASA’s daily global weather data, WeatherProb gives you clear, data-backed insights for any location and any time of year.
Our mission:
➡️ Empower users to move beyond guesswork and make data-driven decisions for long-term planning.
WeatherProb is packed with tools designed to make weather analysis intuitive and insightful.
- 🌍 Interactive 3D Globe & Map — Select any location with ease using Vanta.js and Leaflet.
- 📊 Single Analysis Dashboard — View probabilities, averages, records, and trends in one place.
- ⚖️ Location Comparison Tool — Compare two locations side by side for better planning.
- 🏆 “Perfect Day” Finder — Define your ideal weather and discover the best dates to plan your activity.
- 🗺️ High-Performance Heatmap Explorer — Visualize global weather probability hotspots.
- ⚙️ Personalized Thresholds — Customize what “hot,” “cold,” or “windy” means for you.
- 📈 Interactive Trend Charts — Explore long-term data for temperature, humidity, and precipitation.
- 💾 Data Export — Download your analysis as JSON or CSV for external use.
| Category | Technologies |
|---|---|
| Frontend | React, TypeScript, Vite, Tailwind CSS, shadcn/ui, Recharts, Leaflet, Vanta.js |
| Backend | Python, Flask, Pandas, NumPy |
| Data Source | NASA POWER API (Daily Point & Regional Services) |
| Deployment | Vercel (Frontend), Render (Backend) |
# Clone the repository
git clone https://github.com/Mohitmhatre32/Weatherprob.git
cd Weatherprob/backend
# Create a virtual environment
python -m venv venv
# Activate it
# On Windows:
.\venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate
# Install dependencies
pip install -r requirements.txt
# Run the Flask server
python app.py
# Backend runs at http://127.0.0.1:5000
# Navigate to frontend
cd ../frontend
# Install dependencies
npm install
# Run the Vite dev server
npm run dev
# Frontend runs at http://localhost:5173
☀️ Data Source
This project uses the NASA POWER (Prediction of Worldwide Energy Resources) API. It provides global, analysis-ready data derived from NASA’s MERRA-2 and GEOS-5 datasets.
We use:
Daily Point API for single-location analysis
Daily Regional API for generating the global heatmap feature
🔗 Explore the data source: NASA POWER Data Access Viewer
🔮 Future Vision
WeatherProb’s roadmap includes exciting next steps:
🗺️ Regional Polygon Analysis — Draw areas on the map to aggregate weather stats.
🎯 Activity-Based Recommendations — Automatically find the best dates for skiing, hiking, or beach days.
🔗 Public API — Expose the analysis engine to developers for open data use.
🧠 About the Challenge :
Hackathon: NASA Space Apps Challenge 2025
Challenge Name: “Will It Rain on My Parade?”
👥 Authors
Mohit Mhatre
GitHub: https://github.com/Mohitmhatre32
LinkedIn: www.linkedin.com/in/mohitmhatre
Yukta Chaudhari
GitHub: https://github.com/yuktac1011
LinkedIn:https://www.linkedin.com/in/yukta-chaudhari-725065303/
🪪 License
This project is licensed under the MIT License . Feel free to use, modify, and share — just credit the authors.