MetNet-3
Google's MetNet-3 is a state-of-the-art neural weather model extending lead time and variable coverage well beyond prior observation-based models.
Ask this paper
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.