PhysicsNeMo Checkpoints: AFNO_DX_TP-V1-ERA5 (ERA5 AFNO Accumulated Total Precipitation Diagnostic Model)

Description:

This is a 6-hour accumulated surface precipitation diagnostic model checkpoint. This deep learning model, based on the Adaptive Fourier Neural Operator framework (AFNO), predicts 6-hour accumulated surface precipitation given 20 atmospheric variables plus invariants. For a given atmospheric state at time t, the model predicts accumulated precipitation over (t-6h, t).

For training recipes, see NVIDIA PhysicsNeMo. For inference, see NVIDIA Earth2Studio.

This model is ready for commercial use.

License/Terms of Use:

Use of this model is governed by the NVIDIA Community Model License. By pulling and using this model, you accept the terms and conditions of this license.

Reference(s):

FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators

Model Architecture:

Architecture Type: This model uses an Adaptive Fourier Neural Operator architecture.
Network Architecture: AFNO

Input:

Input Type(s):

• Tensor (6 surface variables and 14 pressure-level variables.)
• DateTime (NumPy Array)
  

Input Format(s): PyTorch Tensor / NumPy Array
Input Parameters:

• Four Dimensional (4D) (batch, variable, latitude, longitude)
  
• DateTime (1D)

Other Properties Related to Input:

• Input latitude/longitude grid: 0.25 degree resolution regular latitude/longitude grid (720 x 1440 pixels).
• Input state weather variables: u10m, v10m, t2m, sp, msl, t850, u1000, v1000, z1000, u850, v850, z850, u500, v500, z500, t500, z50, q500, q850, tcwv,

For lexicon information, review the ERA5 Lexicon at Earth2Studio. u, v, t, z and q refer to eastward and northward wind components, temperature, geopotential, and specific humidity (respectively).

Output:

Output Type(s): Tensor (1 variable - 6-hour accumulated surface accumulation)
Output Format: Pytorch Tensors
Output Parameters: Four Dimensional (4D) (batch, variable, latitude, longitude)
Other Properties Related to Output:

• Output latitude/longitude grid: 0.25 degree resolution regular latitude/longitude grid (720 x 1440 pixels).
• Output state weather variables: tp06
  

Software Integration

Runtime Engine(s): PyTorch
Supported Hardware Microarchitecture Compatibility:

• NVIDIA Ampere
  
• NVIDIA Hopper
  
• NVIDIA 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: ERA5 data for the period January 1980 - December 2016. 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.

For more information about ERA5 and a list of known and resolved issues, see the associated documentation and reference publication which provides more detailed information about the quality of the ERA5 reanalysis. The ERA5 reanalysis, and therefore any model trained using it as a data source, is an imperfect representation of the state of global weather, with correlated errors as a function of region and time. Furthermore, the limited spatial and vertical resolution of the dataset means that physical behavior near or below that resolution scale are not accurately resolvable. Review ECMWF's publications for further information about the quality of its forecasts.

Testing Dataset:

Link: ERA5

** Data Collection Method by dataset

• Automatic/Sensors
  

** Labeling Method by dataset

• Automatic/Sensors
  

Properties: ERA5 data for the date range of January 2017 - December 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.

For more information about ERA5 and a list of known and resolved issues, see the associated documentation and reference publication which provides more detailed information about the quality of the ERA5 reanalysis. The ERA5 reanalysis, and therefore any model trained using it as a data source, is an imperfect representation of the state of global weather, with correlated errors as a function of region and time. Furthermore, the limited spatial and vertical resolution of the dataset means that physical behavior near or below that resolution scale are not accurately resolvable. Review ECMWF's publications for further information about the quality of its forecasts.

Evaluation Dataset:

Link: ERA5

** Data Collection Method by dataset

• Automatic/Sensors
  

** Labeling Method by dataset

• Automatic/Sensors
  

Properties: ERA5 data for the date range of January 2019 - December 2020. 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.

For more information about ERA5 and a list of known and resolved issues, see the associated documentation and reference publication which provides more detailed information about the quality of the ERA5 reanalysis. The ERA5 reanalysis, and therefore any model trained using it as a data source, is an imperfect representation of the state of global weather, with correlated errors as a function of region and time. Furthermore, the limited spatial and vertical resolution of the dataset means that physical behavior near or below that resolution scale are not accurately resolvable. Review ECMWF's publications for further information about the quality of its forecasts.

Inference:

Engine: PyTorch
Test Hardware:

• A100
  
• H100
  
• L40S
  

Ethical Considerations:

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