Weather predictions need to capture the full range of possibilities — including worst case scenarios, which are the most important to plan for.
WeatherNext 2 can predict hundreds of possible weather outcomes from a single starting point. Each prediction takes less than a minute on a single TPU; it would take hours on a supercomputer using physics-based models.
Our model is also highly skillful and capable of higher-resolution predictions, down to the hour. Overall, WeatherNext 2 surpasses our previous state-of-the-art WeatherNext model on 99.9% of variables (e.g. temperature, wind, humidity) and lead times (0-15 days), enabling more useful and accurate forecasts.
This improved performance is enabled by a new AI modelling approach called a Functional Generative Network (FGN), which injects ‘noise’ directly into the model architecture so the forecasts it generates remain physically realistic and interconnected.
This approach is particularly useful for predicting what meteorologists refer to as “marginals” and “joints.” Marginals are individual, standalone weather elements: the precise temperature at a specific location, the wind speed at a certain altitude or the humidity. What’s novel about our approach is that the model is only trained on these marginals. Yet, from that training, it learns to skillfully forecast ‘joints’ — large, complex, interconnected systems that depend on how all those individual pieces fit together. This ‘joint’ forecasting is required for our most useful predictions, such as identifying entire regions affected by high heat, or expected power output across a wind farm.
