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  • Displaying 6 results
    PhysicsNeMo Checkpoints: AFNO_DX_FI-V1-ERA5
    NVIDIA
    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).
    Model
    PhysicsNeMo Checkpoints: AFNO_DX_TP-V1-ERA5
    NVIDIA
    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.
    Model
    PhysicsNeMo Checkpoints: AFNO_DX_SR-V1-ERA5
    NVIDIA
    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.
    Model
    PhysicsNeMo Checkpoints: DLESyM-V1-ERA5
    NVIDIA
    DLESyM-V1-ERA5 is an ensemble forecast model for global earth system modeling, including atmosphere and ocean components. The model operates with 6 hour temporal resolution, forecasting 8 atmospheric variables and 1 oceanic variable.
    Model
    PhysicsNeMo Checkpoints: StormCast-V1-ERA5-HRRR
    NVIDIA
    StormCast-V1-ERA5-HRRR is a mesoscale machine learning AI model that autoregressively predicts 99 state variables at km scale using a 1-hour time step, with dense vertical resolution in the atmosphere boundary layer.
    Model
    PhysicsNeMo Checkpoints: AFNO_DX_WG-V1-ERA5
    NVIDIA
    This deep learning model, based on the Adaptive Fourier Neural Operator framework (AFNO), predicts 6-hour maximum 3-second wind gusts given 20 atmospheric variables plus invariants.
    Model

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