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NVIDIA
PhysicsNeMo Checkpoints: AFNO_DX_FI-V1-ERA5
Model
NVIDIA
NVIDIA
PhysicsNeMo Checkpoints: AFNO_DX_FI-V1-ERA5

This deep learning model, based on the Adaptive Fourier Neural Operator framework (AFNO), interpolates a prognostic forecast model to a shorter time-step size (by default from 6 h to 1 h).

This model is backed by NVIDIA's Plus Plus (++) Promise
to learn more about the quality of the datasets used to train this model.
FieldResponse
Intended Application & Domain:Weather, Energy Forecasting
Model Type:Adaptive Fourier Neural Operator (AFNO)
Intended User:Weather and energy scientists or practitioners accelerating predictions using AI.
Output:Tensor (1-hourly interpolated forecasts of a 73-channel 6-hour forecast).
Describe how the model works:AFNO decodes input variables into a latent space and learns a interpolating function.
Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of:N/A
Technical Limitations:The model may perform poorly for systems that are not similar to those in the training data, namely for rare weather phenomena or weather behavior outside of the 1980-2016 training dataset. There is no mechanism to enforce physical consistency for predictions.
Verified to have met prescribed NVIDIA quality standards:Yes
Performance Metrics:Root Mean Square Error (RMSE), Accuracy (ACC), and Bias (mean error).
Potential Known Risks:This model may inaccurately predict interpolations given technical limitations noted above.
Licensing:NVIDIA Community Model License