nvidia/StormScope-NSRDB

Earth-2 Checkpoints: StormScope-Solar-GOES-NSRDB

Description:

StormScope-Solar (NSRDB) is a generative AI model that estimates surface solar irradiance, specifically Global Horizontal Irradiance (GHI), from geostationary satellite (GOES) imagery over the continental US (CONUS). The model is the solar component of the StormScope nowcasting system: it pairs with the StormScope-GOES model, which autoregressively predicts future GOES imagery, to nowcast GHI at short lead times. Each predicted (or observed) GOES frame is mapped to a GHI field on the 3 km HRRR model grid over CONUS.

The model uses a deterministic regression prediction that is optionally refined with an EDM diffusion denoiser via SDEdit (warm-started diffusion), and is conditioned on the most recent GOES state. It supports clearness-index normalization, where GHI is normalized by the top-of-atmosphere insolation, so that predictions remain physically consistent with the solar geometry.

For inference see NVIDIA Earth2Studio.

This model is ready for commercial or non-commercial use.

License/Terms of Use:

Governing Terms: Use of this model is governed by the Linux Foundation OpenMDW License Agreement, version 1.1.

Deployment Geography:

Global

Use Case:

High-resolution nowcasting of surface solar irradiance (GHI) over the continental US (CONUS) from geostationary satellite imagery, for solar energy forecasting, grid operations, and atmospheric/renewable-energy research.

Release Date:

Hugging Face [08/31/2026] via https://huggingface.co/nvidia/stormscope-NSRDB

References(s):

• StormScope-GOES-MRMS model card: https://huggingface.co/nvidia/stormscope-goes-mrms
• NVIDIA Earth2Studio: https://github.com/NVIDIA/earth2studio
• NSRDB (National Solar Radiation Database): https://nsrdb.nrel.gov/

Model Architecture

Architecture Type: Diffusion-based image-to-image regression model. A deterministic regression network produces an initial GHI estimate that is refined by an EDM (elucidated diffusion model) denoiser using SDEdit (warm-started diffusion).

Network Architecture: Diffusion denoiser with optional regression backbone

Number of model parameters: ~3M per network (SongUNet regression + SongUNet diffusion denoiser; model_channels=64, num_blocks=2 each).

Input:

Input Type(s):

• Tensor (GOES state variables, used as conditioning for the GHI estimate)
• DateTime (NumPy Array), used to compute solar insolation / clearness-index normalization

Input Format(s): PyTorch Tensor / NumPy array

Input Parameters:

• Five Dimensional (5D) (batch, lead time, variable, height, width)

• Input DateTime (1D)

Other Properties Related to Input:

• Input grid (height/width) dimensions are that of the HRRR model grid (3 km CONUS).
• Conditioning GOES variables: abi01c, abi02c, abi03c, abi07c, abi08c, abi09c, abi10c, abi13c.
• Conditioning is regridded from the raw GOES satellite grid onto the model grid; pixels with no GOES coverage are flagged invalid and zeroed after normalization.
• A static cosine-of-solar-zenith / insolation field is computed from the input DateTime for clearness-index normalization.

For variable naming information, review the GOES Lexicon in Earth2Studio.

Output:

Output Type(s): Tensor (surface Global Horizontal Irradiance, ghi)

Output Format: PyTorch Tensors

Output Parameters: Five Dimensional (5D) (batch, lead time, variable, latitude, longitude)

Other Properties Related to Output:

• Output grid (height/width) dimensions are that of the HRRR/NSRDB 3 km model grid over CONUS.
• Output variable: ghi (Global Horizontal Irradiance, W/m²).
• Output lead time is of size 1 (model estimates GHI at the GOES observation time).

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Software Integration

Runtime Engine(s): PhysicsNeMo; NVIDIA Earth2Studio

Acceleration Engine: Not Applicable (the model runs in native PyTorch; no TensorRT / TensorRT-LLM / Triton optimization is applied)

Supported Hardware Microarchitecture Compatibility:

• NVIDIA Ampere

• NVIDIA Blackwell

• NVIDIA Hopper

Supported Operating System(s):

• Linux

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.

Model Version(s):

Model Version: v1 (stormscope_nsrdb)

Training, Testing, and Evaluation Datasets:

Training Dataset:

Link: NSRDB

*Data Collection Method by dataset:*

• Automatic/Sensors

*Labeling Method by dataset:*

• Automatic/Sensors

*Data Modality:*

• Gridded geophysical time series

*Data Size:*

• ~2.5 TB on disk (gridded NSRDB GHI on the 3 km HRRR CONUS grid, 5-min cadence: 627 GB for 2020 and ~625 GB each for 2021–2023)

Properties: NSRDB (National Solar Radiation Database) surface solar irradiance estimates used as the GHI target. NSRDB provides modeled solar radiation derived from satellite observations over CONUS. Training period: 2020–2023 (held-out validation year: 2024).

Link: GOES

*Data Collection Method by dataset*

• Automatic/Sensors

*Labeling Method by dataset*

• Automatic/Sensors

*Data Modality:*

• Gridded geophysical time series

*Data Size:*

• ~25 TB logical (uncompressed) for the 8 conditioning bands, 2020–2023: ~6.3 TB/year...