This deep learning model, based on the Adaptive Fourier Neural Operator framework (AFNO), predicts 6-hour accumulated surface solar irradiance given 24 atmospheric variables plus invariants.
PhysicsNeMo Checkpoints: AFNO_DX_SR-V1-ERA5 (ERA5 AFNO Solar Radiation Diagnostic)
Description:
This is a surface solar irradiance (SSI) diagnostic model checkpoint. This deep learning model, based on the Adaptive Fourier Neural Operator framework (AFNO), predicts 6-hour accumulated surface solar irradiance given 24 atmospheric variables plus invariants. For a given atmospheric state at time t, the model predicts accumulated SSI 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):
Advancing Solar Irradiance Prediction with NVIDIA Earth-2
Data-driven Surface Solar Irradiance Estimation using Neural Operators at Global Scale
Model Architecture:
Architecture Type: This model uses an Adaptive Fourier Neural Operator architecture.
Network Architecture: AFNO
Input:
Input Type(s):
- Tensor (3 surface variables and 3 pressure-level variables at 7 pressure levels for a total of 24 total 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:
t2m,sp,tcwv,z50,z300,z500,z700,z850,z925,z1000,t50,t300,t500,t700,t850,t925,t1000,q50,q300,q500,q700,q850,q925,q1000
For lexicon information, review the ERA5 Lexicon at Earth2Studio. t, z and q refer to temperature, geopotential, and specific humidity (respectively).
Output:
Output Type(s): Tensor (1 variable - 6-hour accumulated Surface Solar Irradiance)
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:
ssi
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
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