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 GPUs and more is enabled by a novel training paradigm for combined model- and data-parallelism, inspired by domain decomposition methods in classical numerical models. Additionally, FourCastNet 3 enables rapid inference on a single GPU, producing a 60-day global forecast at 0.25°, 6-hourly resolution in under 4 minutes. Its computational efficiency, medium-range probabilistic skill, spectral fidelity, and rollout stability at subseasonal timescales make it a strong candidate for improving meteorological forecasting and early warning systems through large ensemble predictions.

This model is ready for commercial/non-commercial use.

Global

Industry, academic, and government research teams interested in medium-range and subseasonal-to-seasonal weather forecasting, and climate modeling.

NGC 07/18/2025

Papers:

Code:

Architecture Type: Spherical Neural Operator. A fully convolutional architecture based on group convolutions defined on the sphere. Leverages both local and global convolutions. For details regarding the architecture refer to the FourCastNet 3 paper.

Network Architecture: N/A

Number of model parameters: 710,867,670

Model datatype: We recommend that the model is run in AMP with bf16, however, the inputs and outputs are typically float32.

Input Format: PyTorch Tensor
Input Parameters:

For variable name information, review the Lexicon at Earth2Studio.

Output Type: Tensor (72 surface and pressure-level variables)
Output Format: Pytorch Tensor
Output Parameters: Six Dimensional (6D) (batch, time, lead time, variable, latitude, longitude)
Other Properties Related to Output:

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Runtime Engine: Pytorch
Supported Hardware Microarchitecture Compatibility:

Model Version: v1

Total size (in number of data points): 110,960
Total number of datasets: 1
Dataset partition: training 95%, testing 2.5%, validation 2.5%

Link: ERA5

Data Collection Method by dataset

Labeling Method by dataset

Properties: ERA5 data for the period 1980-2015. ERA5 provides hourly estimates of various atmospheric, land, and oceanic climate variables. The data covers the Earth on a 30km grid and resolves the atmosphere at 137 levels.

Link: ERA5

Data Collection Method by dataset

Labeling Method by dataset

Properties: ERA5 data for the period 2016-2017. ERA5 provides hourly estimates of various atmospheric, land, and oceanic climate variables. The data covers the Earth on a 30km grid and resolves the atmosphere at 137 levels.

Link: ERA5

Data Collection Method by dataset

Labeling Method by dataset

Properties: ERA5 data for the period 2018-2019. ERA5 provides hourly estimates of various atmospheric, land, and oceanic climate variables. The data covers the Earth on a 30km grid and resolves the atmosphere at 137 levels.

Acceleration Engine: Makani, Pytorch
Test Hardware:

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