nvidia/fourcastnet1

Earth-2 Checkpoints: FourCastNet

FourCastNet, short for Fourier Forecasting Neural Network, is a global data-driven weather forecasting model that provides accurate short to medium-range global predictions. FourCastNet accurately forecasts high-resolution, fast-timescale variables such as the surface wind speed, precipitation, and atmospheric water vapor. FourCastNet uses the the Adaptive Fourier Neural Operator (AFNO) archiecture. This particular neural network architecture is appealing as it is specifically designed for high-resolution inputs and synthesizes several key recent advances in DL into one model.

Release Date: October 25, 2023

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

License/Terms of Use:

Apache 2.0 license

Deployment Geography:

Global

Use Case:

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

References:

Papers:

• FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
• Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers
• The ERA5 global reanalysis

Code:

• PhysicsNeMo
• Earth2Studio

Model Architecture:

Architecture Type: Neural Operator

Network Architecture: Adaptive Fourier Neural Operator

Input:

Input Type:

• Tensor (26 surface and pressure-level variables)

Input Format: PyTorch Tensor
Input Parameters:

• Six Dimensional (6D) (batch, time, lead time, variable, latitude, longitude)
  

Other Properties Related to Input:

• Input equi-rectangular latitude/longitude grid: 0.25 degree 720 x 1440 (south-pole excluding)
• Input state weather variables: u10m, v10m, t2m, sp, msl, t850, u1000, v1000, z1000, u850, v850, z850, u500, v500, z500, t500, z50, r500,r850,tcwv,u100m,v100m,u250,v250,z250,t250
• Time: datetime64

For variable name information, review the Lexicon at Earth2Studio.

Output:

Output Type: Tensor (26 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:

• Output latitude/longitude grid: 0.25 degree 72 x 1440, same as input.
• Output state weather variables: same as above.

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.

Software Integration:

Runtime Engine: Pytorch
Supported Hardware Microarchitecture Compatibility:

• NVIDIA Ampere
  
• NVIDIA Hopper
  
• NVIDIA Turing
  

Supported Operating System:

• Linux
  

Model Version:

Model Version: v1

Training Dataset:

Link: ERA5

Data Collection Method by dataset

• Automatic/Sensors
  

Labeling Method by dataset

• Automatic/Sensors
  

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.

Testing Dataset:

Link: ERA5

Data Collection Method by dataset

• Automatic/Sensors
  

Labeling Method by dataset

• Automatic/Sensors
  

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.

Evaluation Dataset:

Link: ERA5

Data Collection Method by dataset

• Automatic/Sensors
  

Labeling Method by dataset

• Automatic/Sensors
  

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.

Inference:

Acceleration Engine: Pytorch
Test Hardware:

• A100
  
• H100
  
• L40S
  

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.

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