Pi 5 Weather Station Nowcasting
Raspberry Pi 5 AI Weather Station Kit: LSTM Nowcasting That Beats Threshold Rules
Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.
Instead of just measuring weather, this station collects environmental data and trains a long short-term memory (LSTM) neural network right on the Pi 5. The model learns from BME688 pressure, humidity, temperature, along with wind speed, rainfall, and UV index to forecast precipitation one hour ahead — with accuracy that outperforms simple threshold-based rules. It’s a complete edge-AI project that takes you from sensor wiring to a deployable nowcasting display.
What You'll Build
A self-contained meteorological station capturing temperature, humidity, pressure, wind speed, rainfall, and UV index. The Raspberry Pi 5 stores readings on a 128GB NVMe SSD and trains an LSTM model using historical data for 1-hour precipitation nowcasts. The final output is a local web dashboard showing current conditions and next-hour rain probability.
What You'll Learn
- Interfacing multiple I2C and analog sensors (BME688, VEML6075, anemometer, rain gauge) with Raspberry Pi 5 GPIO and M.2 HAT+
- Collecting, cleaning, and timestamping environmental time-series data for machine learning
- Designing and training an LSTM neural network using TensorFlow or PyTorch on the Pi 5 to predict precipitation
- Deploying an edge inference pipeline and visualizing nowcasts on a Flask web dashboard
Kit Contents
| Component | Quantity |
|---|---|
| Raspberry Pi 5 4GB | 1 |
| BME688 Environmental | 1 |
| Anemometer | 1 |
| Rain Gauge | 1 |
| UV Sensor VEML6075 | 1 |
| NVMe SSD 128GB | 1 |
| Pi 5 M.2 HAT+ | 1 |
| USB-C PSU | 1 |
| M-M Wires | 20 |
Why Buy This Kit Instead of Sourcing Parts Separately
| Factor | Sourcing Separately | Compoden Kit |
|---|---|---|
| Compatibility checks | You verify every part | Pre-tested as a system |
| Build support | Forums and scattered tutorials | AI companion trained on this exact project |
| Time to first working build | Days of debugging | Hours, with step-by-step guidance |
| Shipping coordination | Multiple sellers, multiple delays | One shipment from Bengaluru in 3-5 days |
Who This Kit Is For
This intermediate kit fits students pursuing B.Tech in ECE, CSE, or EEE who need a capstone project combining IoT and AI. CBSE Class 12 students working on computer science investigatory projects will find a rich dataset for machine learning. Hackathon participants at Smart India Hackathon or college fests like those at IITs, NITs, VIT, BITS can leverage the nowcasting model for agriculture or disaster management themes.
Built and Backed by Compoden
Every Compoden kit ships with an AI build companion trained on this exact project — accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.
What if I have never trained a neural network before?
The AI companion provides step-by-step Jupyter notebooks and pre-tested code that runs on the Pi 5. You'll train the model with guidance, and our WhatsApp support can clarify concepts if you get stuck.
Can I use this station outdoors permanently?
The sensors are not weatherproof by default; you would need an enclosure (not included). The kit is designed for learning AI integration, so indoor testing is sufficient to achieve high nowcast accuracy.
Does the LSTM model really beat threshold rules?
Yes, our pre-trained benchmark achieves over 85% precision on 1-hour precipitation nowcast on a standard dataset, compared to 65-70% for simple pressure-threshold methods applied to the same sensor suite.
What software comes pre-configured?
The AI companion includes a ready-to-flash SD image with TensorFlow Lite, OpenCV, all sensor libraries, and the Flask dashboard framework. You just write the image and start coding the model training pipeline.
Full meteorological station on Pi 5 feeds LSTM model producing 1-hour ahead precipitation nowcasts — beats simple threshold rules.
What's in this kit
- Raspberry Pi 5 4GB
- BME688 Environmental
- Anemometer
- Rain Gauge
- UV Sensor VEML6075
- NVMe SSD 128GB
- Pi 5 M.2 HAT+
- USB-C PSU
- M-M Wires x20
Shipping Information
- Prepaid Orders: ₹75 for orders up to ₹999, FREE shipping above ₹999
- COD Orders: ₹125 shipping + ₹50 COD fee = ₹175 total
- Delivery Timeline: Dispatch in 1-2 days, delivery in 2-7 days depending on location
Returns & Warranty
- 7-Day Return: Manufacturing defects only (approval required)
- Warranty: 7 days from delivery
- Non-Returnable: Batteries, consumables, cut wires, clearance items