Details

This NGC asset is a Spherical Fourier Neural Operator model checkpoint package for predicting atmospheric dynamics. Model checkpoint package refers to the set of artifacts needed to run inference using pre-trained model which includes the model checkpoint and required metadata and arrays.

$\"SFNO$

Architecture

Please refer to the reference paper to learn about the model architecture. In training this checkpoint, Spherical Fourier Neural Operator (SFNO) is applied to forecasting atmospheric dynamics, and demonstrates stable autoregressive rollouts for a year of simulated time (1,460 steps), while retaining physically plausible dynamics. The SFNO has important implications for machine learning-based simulation of climate dynamics that could eventually help accelerate our response to climate change.

This model predicts the following variables surface variables: \"u10m\", \"v10m\", \"u100m\", \"v100m\", \"t2m\", \"sp\", \"msl\", \"tcwv\" and the variables u, v, t, z, q at pressure levels 1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50. See the model Lexicon for more details.

Training

The model is trained on a 73-channel subset of the ERA5 reanalysis data on single levels and pressure levels that is pre-processed and stored into HDF5 files. This model was trained in Modulus-Makani

How to use?

This model package can be used with Modulus, Modulus-Makani, and Earth-2 Studio.

References

Modulus-Makani

Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere

License

This checkpoint is distributed under the Apache 2.0 license.

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This is a deep learning weather model that predicts 73 atmospheric/surface variables in an autoregressive fashion.","## Details\nThis NGC asset is a Spherical Fourier Neural Operator model checkpoint package for predicting atmospheric dynamics. Model checkpoint package refers to the set of artifacts needed to run inference using pre-trained model which includes the model checkpoint and required metadata and arrays. \n\n![SFNO Rollout](https://github.com/NVIDIA/makani/raw/main/images/sfno_rollout.gif)\n\n## Architecture\nPlease refer to the [reference paper](https://arxiv.org/abs/2306.03838) to learn about the model architecture. In training this checkpoint, Spherical Fourier Neural Operator (SFNO) is applied to forecasting atmospheric dynamics, and demonstrates stable autoregressive rollouts for a year of simulated time (1,460 steps), while retaining physically plausible dynamics. The SFNO has important implications for machine learning-based simulation of climate dynamics that could eventually help accelerate our response to climate change.\n\nThis model predicts the following variables surface variables: `\"u10m\", \"v10m\", \"u100m\", \"v100m\", \"t2m\", \"sp\", \"msl\", \"tcwv\"` and the variables `u, v, t, z, q` at pressure levels `1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50`. See the model [Lexicon](https://github.com/NVIDIA/earth2studio/blob/main/earth2studio/lexicon/base.py) for more details.\n\n## Training\n\nThe model is trained on a 73-channel subset of the [ERA5 reanalysis](https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5) data on single levels and pressure levels that is pre-processed and stored into HDF5 files. This model was trained in [Modulus-Makani](https://github.com/NVIDIA/modulus-makani)\n\n## How to use?\n\nThis model package can be used with [Modulus](https://github.com/NVIDIA/modulus), [Modulus-Makani](https://github.com/NVIDIA/modulus-makani), and [Earth-2 Studio](https://github.com/NVIDIA/earth2studio).\n\n## References\n\n[Modulus-Makani](https://github.com/NVIDIA/modulus-makani)\n\n[Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere](https://arxiv.org/abs/2306.03838)\n\n## License\nThis checkpoint is distributed under the [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0).","displayName","Modulus Checkpoints: Spherical Fourier Neural Operator",["D",1715961513335],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/modulus/models/sfno_73ch_small","rawImageUrl","https://pbs.twimg.com/profile_images/1145524454170062848/U4lxVYEw_400x400.png","canGuestDownload","latestVersionSizeInBytes",6471450224,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","Field | Response\n:---------------------------------------------------------------------------------------------------|:---------------\nParticipation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None of the Above\nMeasures taken to mitigate against unwanted bias: | None","explainability","Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Application & Domain: | Global Weather Forecasting\nModel Type: | Spherical Fourier Neural Operator\nIntended User: | Climate and weather scientists accelerating weather prediction with AI.\nOutput: | Global forecast prediction of surface and atmosphere variables.\nDescribe how the model works: | Neural operator auto-regressively predicts a time-series forecast from an initial state.\nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable\nVerified to have met prescribed quality standards: | Yes\nPerformance Metrics: | Error, Accuracy, Throughput and Latency\nPotential Known Risks: | None Known\nLicensing: | [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0).","privacy","Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------\nGeneratable or reverse engineerable personally-identifiable information (PII)? | Neither\nWas consent obtained for any PII used? | Not Applicable (N/A)\nProtected class data used to create this model? | None\nHow often is dataset reviewed? | Before Release\nIs a mechanism in place to honor data subject right of access or deletion of personal data? | No\nIf PII collected for the development of the model, was it collected directly by NVIDIA? | N/A\nIf PII collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | N/A\nIf PII collected for the development of this AI model, was it minimized to only what was required? | N/A\nIs there provenance for all datasets used in training? | Yes\nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes\nIs data compliant with data subject requests for data correction or removal, if such a request was made? | N/A","safetyAndSecurity","Field | Response\n:---------------------------------------------------|:----------------------------------\nModel Application(s): | Global Weather Forecasting\nDescribe the life critical impact (if present). | None Known\nModel and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. 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