AI model achieves breakthrough in forecasting cyclones

This post details the advancements in the WeatherNext AI model for cyclone forecasting, achieving state-of-the-art accuracy and providing an extra day of predictive accuracy. Key technical contributions include: the model's ability to bridge the gap between global atmospheric currents and localized thermodynamic processes with a single AI model; co-training on global weather dynamics and historical cyclone observations (IBTrACS database); the use of Functional Generative Networks (FGNs) for efficient ensemble prediction generation, enabling the capture of rare but consequential scenarios; and achieving high accuracy with a coarser input resolution (28x28km) than traditional models, which remains an open research question. The post also announces the open-sourcing of WeatherNext 2, WeatherNext Cyclones, and WeatherNext 2-mini models.