Model Overview

Description:

FourCastNet (Fourier Forecasting Neural Network) forecasts high-resolution, fast-timescale variables like surface wind speed, precipitation, and atmospheric water vapor. FourCastNet is a global, data-driven weather forecasting model that provides accurate short to medium-range global predictions at a time-step size of 6 hours with predictive stability. This model is for research and development only.

Reference(s):

• Spherical Fourier Neural Operator Paper
  
• FourCastNet Paper
  
• Codebase
  
• The ERA5 global reanalysis
  

Model Architecture:

Architecture Type: Neural Operator

Network Architecture: Spherical Fourier Neural Operator

Input:

Input Type(s): Tensor (73 Surface, Atmospheric Variables)

Input Format(s): NumPy

Input Parameters: Four Dimensional (4D)

Other Properties Related to Input: .25 Degree Resolution Latitude-Longitude Grid

Output:

Output Type(s): Tensor (73 Surface, Atmospheric Variables)

Output Format: NumPy

Output Parameters: 4D

Other Properties Related to Output: .25 Degree Resolution Latitude-Longitude Grid; 6 Hours from Input

Software Integration:

Runtime(s): N/A

Supported Hardware Platform(s):

• Ampere
  
• Hopper
  
• Turing
  

Supported Operating System(s): Linux

Model Version(s):

Model version: v2

Training & Evaluation:

Dataset:

Link: ERA5

** Data Collection Method by dataset

• Automatic/Sensors
  

** Labeling Method by dataset

• Automatic/Sensors
  

Properties (Quantity, Dataset Descriptions, Sensor(s)):

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:

Engine: Triton

Test Hardware:

• A100
  
• H100
  
• L40S
  

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