nvidia/corrdiff-cosmo-era5

Model Overview

Description

corrdiff-cosmo-era5 is a downscaling model in NVIDIA's CorrDiff family. It performs spatial downscaling of global ERA5 reanalysis data to high-resolution regional reanalysis over Europe, reproducing the COSMO-REA regional reanalyses produced by the University of Bonn and the German Meteorological Service (DWD). The model takes coarse-resolution ERA5 atmospheric fields (0.25°) and predicts high-resolution surface, near-surface, and lower-atmosphere variables on the native rotated-pole COSMO grid.

Two resolutions are provided, each a self-contained model in the same package:

• REA6: downscales ERA5 to the COSMO-REA6 6 km grid (824 × 848), predicting

45 variables (surface, radiative/turbulent fluxes, and the lowest six model levels of wind, temperature, humidity, and turbulent kinetic energy, plus the planetary boundary-layer height).

• REA2: downscales ERA5 to the COSMO-REA2 2.2 km grid (780 × 724),

predicting 22 variables (surface fields plus the lowest six model levels of the 3D wind components and the boundary-layer height).

Each resolution offers two inference modes from the same training pipeline:

• a deterministic (regression) mode that predicts the conditional mean field, and
• a generative (diffusion) ensemble mode that samples stochastic realizations,

enabling uncertainty quantification and sharper fields.

The model allows the prediction of high-resolution regional reanalysis fields from coarse global reanalysis that would otherwise require an expensive limited-area numerical weather prediction simulation.

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. See LICENSE file for governing terms. The files rea6/invariants_rea6_ext.nc and rea2/invariants_rea2_ext.nc are licensed under CC BY 4.0, with attribution obligations listed in ATTRIBUTION.md.

Deployment Geography:

Global

Use Case:

High-resolution regional climate and weather analysis for the wind- and solar-energy sector (resource assessment, hub-height wind), climate-risk and hydrology research, renewable-energy planning, and weather and climate researchers who need kilometre-scale regional reanalysis fields derived from coarse global reanalysis at a fraction of the cost of a dynamical limited-area model. Outputs are statistical estimates and are not a substitute for validated risk models; they should not be the sole basis for safety-critical decisions (see Ethical Considerations). The model is validated only in-domain on the native COSMO-REA grids; use over extended margins or other regions is unvalidated.

Release Date

Hugging Face: 07/27/2026 via https://huggingface.co/nvidia/corrdiff-cosmo-era5

Reference(s)

• COSMO-REA6 regional reanalysis (DWD)

• COSMO-REA2 regional reanalysis (DWD)

• ERA5 reanalysis (ECMWF)

• GLOBE v1 elevation (NOAA NGDC)

• ESA WorldCover 2021 v200

• Diffusion-based downscaling / CorrDiff

• Diffusion model preconditioning (EDM)

Codebase

• Earth2Studio

• PhysicsNeMo

Model Architecture

Architecture Type: Transformer (Diffusion Transformer, DiT)

Network Architecture: Diffusion Transformer (DiT) with axial 2D rotary position embeddings (RoPE), localized neighborhood attention (NATTEN, kernel size 23), and a patch-size-2 tokenizer. The deterministic (mean) model is a regression DiT (98M parameters) with a convolutional detokenizer head; the generative (ensemble) model is an EDM-preconditioned diffusion DiT (174M parameters) with the standard patch detokenizer, sampled with an 18-step deterministic (Heun) sampler. Both resolutions (REA6, REA2) share this architecture. Note: although part of the CorrDiff downscaling family, this model uses a Diffusion Transformer (not a U-Net), and its generative model predicts the target field directly rather than a residual correction to a deterministic prediction as in the original CorrDiff.

This model was developed based on: the Earth2Studio / PhysicsNeMo Diffusion Transformer downscaling stack.

Number of model parameters: 9.8 × 10^7 (regression / mean mode); 1.7 × 10^8 (diffusion / ensemble mode).

Input(s):

Input Type(s): Tensor (47 ERA5 surface and pressure-level variables) + grid-tied static invariants + sine/cosine of latitude and longitude + cosine of the solar zenith angle. The static-invariant set differs by resolution: REA6 uses surface elevation, land fraction, surface roughness length, terrain slope (east/north), 50 km continentality (land fraction within 50 km), and distance to coast; REA2 uses a reduced set (surface elevation, land fraction, surface roughness length, terrain slope (east/north)), without continentality or distance to coast. The static invariants are derived from open external datasets: surface elevation and terrain slope from GLOBE v1 (NOAA NGDC, public domain); land fraction, surface roughness length, continentality, and distance to coast from ESA WorldCover 2021 v200 (CC BY 4.0).

Input Format(s): PyTorch Tensor.

Input Parameters:

• Four-Dimensional (4D) (batch, variable, latitude, longitude)

Other Properties Related to Input:

• 0.25° regular latitude/longitude ERA5 grid covering the European domain

(plus a margin around the target grid).

• ERA5 input variables (47): u10m, v10m, u100m, v100m, t2m, sp,

msl, and u/v/z/t/q at the 300, 400, 500, 600, 700, 850, 925, and 1000 hPa pressure levels.

• For variable-name conventions, see the Lexicon in Earth2Studio.

Output(s)

Output Type(s): Tensor (high-resolution COSMO-REA variables on the native rotated-pole grid).

Output Format(s): PyTorch Tensor.

Output Parameters:

• Five-Dimensional (5D) (batch, samples, variable, latitude, longitude). The

sample dimension is...