README.md

metadata

title: Tufts Jumbo Weather Forecast
emoji: 🌤
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 5.23.0
python_version: '3.12'
app_file: app.py
pinned: false
license: mit
models:
  - jeffliulab/weather-forecasting-v1

Tufts Jumbo — 24h Weather Forecast

Real-time deep-learning weather prediction for the Jumbo Statue at Tufts University.

How It Works

1. Fetches the latest HRRR 3 km analysis data from NOAA (42 atmospheric channels, 450x449 grid covering the US Northeast)
2. Runs a trained CNN through the spatial snapshot
3. Predicts 6 weather variables 24 hours ahead at a single target point (Jumbo Statue, Medford MA)

Models

Model	Parameters	Architecture
CNN Baseline	11.3M	6 residual blocks, progressive spatial downsampling
ResNet-18	11.2M	Modified torchvision ResNet-18 (42-channel input)

Input Channels (42)

Surface: 2m temperature, 2m humidity, 10m U/V wind, surface gust, solar radiation, 1hr precipitation. Atmospheric: CAPE, dew point (5 levels), geopotential height (5 levels), temperature (5 levels), U-wind (6 levels), V-wind (6 levels), cloud cover (4 layers), precipitable water, relative humidity, VIL.

Output Variables

Temperature (K), Relative Humidity (%), U-Wind (m/s), V-Wind (m/s), Wind Gust (m/s), Precipitation (mm).

Data Source

HRRR (High-Resolution Rapid Refresh) — NOAA's 3 km hourly weather model, fetched in real-time from AWS S3 via Herbie.

Links

• Model Weights — CNN Baseline + ResNet-18 checkpoints
• GitHub Repository — Full project code

Course

Tufts CS 137 — Deep Neural Networks, Spring 2026