Model Overview

Description

FourCastNet V2 uses Spherical Fourier Neural Operator (SFNO) to predict a collection of surface and atmospheric variables such as wind speed, temperature and pressure and is applied to forecasting global atmospheric dynamics.

FourCastNet is a data-driven model that provides accurate short to medium-range global predictions at a time-step size of 6 hours with predictive stability for over a year of simulated time (1,460 steps), while retaining physically plausible dynamics.

This model is ready for commercial use.

Reference(s)

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

Model Architecture

Architecture Type: Neural Operator
Network Architecture: FourCastNet SFNO

Input

Input Type(s):

• Tensor (73 Surface & Atmospheric Variables)
• DateTime
  

Input Format(s): NumPy
Input Parameters:

• Four Dimensional (4D) (batch, variable, latitude, longitude)
  
• Input DateTime
  

Other Properties Related to Input:

• 0.25 degree latitude-longitude grid
• Input resolution: [721, 1440]
• Latitude Coordinates: [90, 89.75, 89.5, ..., -89.5, -89.75, -90]
• Longitude Coordinates: [0, 0.25, 0.5, ..., 359.25, 359.5, 359.75]
• Input weather variables: "u10m", "v10m", "u100m", "v100m", "t2m", "sp", "msl", "tcwv", "u50", "u100", "u150", "u200", "u250", "u300", "u400", "u500", "u600", "u700", "u850", "u925", "u1000", "v50", "v100", "v150", "v200", "v250", "v300", "v400", "v500", "v600", "v700", "v850", "v925", "v1000", "z50", "z100", "z150", "z200", "z250", "z300", "z400", "z500", "z600", "z700", "z850", "z925", "z1000", "t50", "t100", "t150", "t200", "t250", "t300", "t400", "t500", "t600", "t700", "t850", "t925", "t1000", "q50", "q100", "q150", "q200", "q250", "q300", "q400", "q500", "q600", "q700", "q850", "q925", "q1000"
  

Output

Output Type(s):

• Tensor (73 Surface & Atmospheric Variables)

Output Format(s): NumPy
Output Parameters:

• Four Dimensional (4D) (batch, variable, latitude, longitude)
  

Other Properties Related to Output:

• Time-delta of 6 hours from input array
• 0.25 degree latitude-longitude grid
• Output resolution: [721, 1440]
• Latitude Coordinates: [90, 89.75, 89.5, ..., -89.5, -89.75, -90]
• Longitude Coordinates: [0, 0.25, 0.5, ..., 359.25, 359.5, 359.75]
• Output weather variables: "u10m", "v10m", "u100m", "v100m", "t2m", "sp", "msl", "tcwv", "u50", "u100", "u150", "u200", "u250", "u300", "u400", "u500", "u600", "u700", "u850", "u925", "u1000", "v50", "v100", "v150", "v200", "v250", "v300", "v400", "v500", "v600", "v700", "v850", "v925", "v1000", "z50", "z100", "z150", "z200", "z250", "z300", "z400", "z500", "z600", "z700", "z850", "z925", "z1000", "t50", "t100", "t150", "t200", "t250", "t300", "t400", "t500", "t600", "t700", "t850", "t925", "t1000", "q50", "q100", "q150", "q200", "q250", "q300", "q400", "q500", "q600", "q700", "q850", "q925", "q1000"
  

Software Integration

Runtime Engine(s): Not Applicable
Supported Hardware Microarchitecture Compatibility:

• Ampere
  
• Hopper
  
• Turing
  

Supported Operating System(s):

• Linux
  

Model Version(s)

Model version: v1

Training, Testing, and Evaluation Datasets:

Training Dataset

Link: ERA5

Data Collection Method by dataset

• Automatic/Sensors
  

Labeling Method by dataset

• Automatic/Sensors
  

Properties (Quantity, Dataset Descriptions, Sensor(s)): ERA5 data for the years of 1979-2017. 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 (Quantity, Dataset Descriptions, Sensor(s)): ERA5 data for the year of 2018. 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
  
• RTX6000
  

Ethical Considerations:

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License

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