Weather simulation model that predicts global atmosphere at ~5 km resolution in 10-minute steps with only 332M parameters. Released on Hugging Face (1.6 GB) under Apache 2.
STRATA at a glance
- What is STRATA?
- STRATA is a model from Nvidia. Nvidia's 332M-parameter AI model emulating the SCREAM global storm-resolving atmosphere simulation at ~4.9 km, 10-minute steps.
- Who makes STRATA?
- STRATA is made by Nvidia.
- Does STRATA work in the EU?
- Available in the EU. Open weights downloadable from Hugging Face under OpenMDW 1.1 with deployment geography listed as "Global"; no EU exclusion found in the license or model card. Last verified 14 Nov 2026.
- How much does STRATA cost?
- Open source. Free to download; the model card states it is ready for commercial or non-commercial use. Licence: OpenMDW-1.1.
- Where was STRATA shown?
- In 1 creator video by AI Search. Most recently by AI Search on 8 Nov 2026, at 5:09 in “Weight space programming, new 3D models, Gemini Omni 1.2, GPT Image 2.6: AI NEWS”.
Sources: the vendor's own pages (linked above) and creator videos at the linked timestamps. EU status is information, not legal advice; see how we check.
STRATA (Storm-resolving Tile-based autoRegressive Atmosphere Transformer Architecture) is a global, kilometer-scale AI atmosphere model from Nvidia, published as part of the Earth-2 checkpoints. It emulates the SCREAM (Simple Cloud-Resolving E3SM Atmosphere Model) storm-resolving simulation, autoregressively predicting the 3D atmospheric state on SCREAM's ne1024pg2 cubed-sphere grid (about 25.2 million columns, ~4.9 km spacing) with a 10-minute time step.
The prognostic state covers potential temperature, zonal and meridional wind, geopotential height, vertical pressure velocity and specific humidity on 24 model levels, plus 2-m air temperature. Surface pressure and surface precipitation fluxes are diagnosed at each step. The architecture is a two-stage "PixelDiT" transformer with neighborhood attention (331.7M parameters), a deterministic single-step predictor trained with a regression loss and fine-tuned with 4-step autoregressive rollouts; no diffusion sampling is used.
Inference is tile-based: a single GPU can advance any part of the globe, and multi-GPU domain decomposition advances the full globe. Intended uses are weather and climate research, such as short-range forecasting of the SCREAM state, emulation of storm-resolving simulations, and experiments in coupling or downscaling AI emulators. Weights are on Hugging Face under the OpenMDW 1.1 license; training recipes and inference code are in the NVlabs/STRATA repository, and the architecture is in NVIDIA PhysicsNeMo.
- Global atmosphere emulation at ~4.9 km (ne1024pg2 cubed-sphere grid)
- 10-minute autoregressive time step
- 331.7M-parameter two-stage transformer with neighborhood attention
- Tile-based inference on a single GPU or multi-GPU domain decomposition
- Weights released under OpenMDW 1.1; code under Apache-2.0
As seen on YouTube
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