MetNet-3

Dense + sparse sensors: Learns jointly from dense sensor data (radar, satellite) and sparse in-situ station data, combining signals that were typically used separately.

24-hour forecasts: Produces predictions up to 24 hours ahead, a meaningful lead-time extension for observation-based weather modeling.

Multi-variable output: Predicts precipitation, wind, temperature, and dew point from the same model, rather than requiring per-variable systems.

Operational relevance: Demonstrates the neural-weather-model pattern that would dominate 2024 forecasting research - observation-driven, end-to-end neural pipelines replacing traditional numerical systems.