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Intended Task/Domain: | Kilometer-scale global atmosphere emulation (weather and climate research)
Model Type: | Transformer with neighborhood attention (two-stage "PixelDiT": semantic backbone + pixel refinement)
Intended Users: | Weather and climate ML researchers / developers building or evaluating km-scale global atmosphere emulators
Output: | 148 channels on the SCREAM ne1024pg2 cubed-sphere grid (6 × 2048 × 2048 columns, ~4.9 km): potential temperature, zonal/meridional wind, geopotential height, vertical pressure velocity and specific humidity on 24 model levels, 2-m temperature, surface pressure and liquid/ice surface precipitation fluxes
Describe how the model works: | STRATA autoregressively predicts the SCREAM atmospheric state 10 minutes ahead. The current state (145 channels) plus four forcing channels (solar zenith angle, surface geopotential, sub-grid orography, land fraction) are normalized and processed tile by tile on the cubed-sphere; a semantic transformer on 4×4-pixel patches captures mesoscale structure and a pixel-resolution transformer refines the output at native resolution. The network predicts a mixed state/tendency target which is de-normalized and added to the input state; longer forecasts are produced by feeding predictions back as inputs.
Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable
Technical Limitations & Mitigation: | The model emulates one specific simulation (SCREAM v1) rather than observations, so it inherits SCREAM's biases and is not calibrated against real-world data. The model was trained on only 17 simulated days from three SCREAM runs, so it has seen a limited range of seasons and climate states. Forecast skill degrades with lead time (multi-day) and small-scale features such as individual convective cells decorrelate within hours; tile-based inference can introduce faint tile-boundary artifacts that are mitigated with overlapping halos and sub-patch jitter. Users should validate on their own use case.
Verified to have met prescribed NVIDIA quality standards: | Yes
Performance Metrics: | Area-weighted Root Mean Square Error (RMSE) and bias per variable and level against SCREAM, radially averaged power spectra, precipitation statistics
Potential Known Risks: | The model may mispredict the location, intensity or timing of severe weather (e.g. convective systems, tropical cyclones, heavy precipitation). It is a research model and should not be used as the sole basis for safety-critical decisions.
Licensing: | [Linux Foundation OpenMDW License Agreement, version 1.1](https://github.com/OpenMDW/OpenMDW/blob/main/1.1/LICENSE.OpenMDW-1.1)
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