# Earth-2 Checkpoints: FourCastNet 3
## Description:
FourCastNet 3 advances global weather modeling by implementing a scalable, geometric
machine learning (ML) approach to probabilistic ensemble forecasting. The approach is
designed to respect spherical geometry and to accurately model the spatially
correlated probabilistic nature of the problem, resulting in stable spectra and
realistic dynamics across multiple scales. FourCastNet 3 delivers forecasting accuracy
that surpasses leading conventional ensemble models and rivals the best diffusion-based
methods, while producing forecasts 8 to 60 times faster than these approaches. In
contrast to other ML approaches, FourCastNet 3 demonstrates excellent probabilistic
calibration and retains realistic spectra, even at extended lead times of up to 60 days.
All of these advances are realized using a purely convolutional neural network
architecture specifically tailored for spherical geometry. Scalable and efficient
large-scale training on 1024…
