Correction Diffusion (CorrDiff) is a generative AI model that downscales surface and atmospheric variables to improve the accuracy and resolution of weather data.
Earth-2 CorrDiff NIM
Earth-2 Correction Diffusion (CorrDiff) NIM is a generative AI model that downscales surface and atmospheric variables to improve the accuracy and resolution of weather data. This NIM houses a CorrDiff model trained to map ERA5 data to HRRR CONUS data. CorrDiff is a two-step approach where the mean machine learning model is corrected by another diffusion model. CorrDiff exhibits skillful deterministic and probabilistic predictions and faithfully recovers spectra and distributions for extremes.
The container components are ready for commercial/non-commercial use.
License/Terms of Use
The NIM container is governed by the NVIDIA AI Product Agreement; and the use of this model is governed by the NVIDIA AI Foundation Models Community License.
Deployment Geography:
Global
Release Date:
1/20/2026 via NGC
Program Classes
The following models are housed within the Earth-2 CorrDiff NIM Container.
| Model Name & Link | Use Case | How to Pull the Model |
|---|---|---|
| US CorrDiff GEFS HRRR | Downscaling Atmosphere/Surface Variables | Automatically downloaded in container version |
Deployment Details
NVIDIA NIM, part of NVIDIA AI Enterprise, is a set of easy-to-use microservices designed to accelerate deployment of generative AI across cloud, data center, and workstations.
Benefits of self-hosted NIMs:
- Deploy anywhere and maintain control of generative AI applications and data
- Streamline AI application development with industry standard APIs and tools tailored for enterprise environments
- Prebuilt containers for the latest generative AI models, offering a diverse range of options and flexibility right out of the gate
- Industry-leading latency and throughput for cost-effective scaling
- Support for custom models out of the box so models can be trained on domain specific data
- Enterprise-grade software with dedicated feature branches, rigorous validation processes, and robust support structures
Getting Started with CorrDiff NIM
Please visit the Earth-2 NIM Documentation on how to get started.
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.
Changelog
Version v1.1.0
Performance Improvements
This version includes significant performance enhancements across all supported GPU platforms.
Speed Up over Earth-2 CorrDiff NIM v1.0.0
| Diffusion Steps | L40s | RTX6000 | A100 | H100 |
|---|---|---|---|---|
| 8 Steps | 4.0x | 3.2x | 2.75x | 3.6x |
| 18 Steps | 3.8x | 2.9x | 2.56x | 3.11x |
Security Common Vulnerabilities and Exposures (CVEs)
Please review the Security Scanning tab on NGC to view the latest security scan results.
For certain open-source vulnerabilities listed in the scan results, NVIDIA provides a response in the form of a Vulnerability Exploitability eXchange (VEX) document. The VEX information can be reviewed and downloaded from the Security Scanning tab.
Ethical Considerations:
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal developer team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.
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