Details

This NGC asset is a FourCastNet v2 SFNO model checkpoint package. Model checkpoint package refers to the set of artifacts needed to run inference using pre-trained model which includes the model checkpoint, set of sample inputs, inference script.

Architecture

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

Training

The model is trained on a 73-channel subset of the ERA5 reanalysis data on single levels and\npressure levels that is pre-processed and stored into HDF5 files.

How to use?

A minimal inference script is provided in this model checkpoint package to get you started easily.

To run inference on this checkpoint, you can follow the below steps (all the files needed are included in the zip file):

Launch Modulus docker container

docker run --rm --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 --runtime nvidia -v ${PWD}:/examples -it nvcr.io/nvidia/modulus/modulus:23.08\n

Download and install earth2mip.

git clone https://github.com/NVIDIA/earth2mip.git\ncd earth2mip && pip install .   \n

Download this checkpoint zip file, and unzip it

wget 'https://api.ngc.nvidia.com/v2/models/nvidia/modulus/modulus_fcnv2_sm/versions/v0.2/files/fcnv2_sm.zip'\nunzip fcnv2_sm.zip\n

Run the simple inference

cd fcnv2_sm/\npython simple_inference.py\n

You can also use the model checkpoint from this model checkpoint package to evaluate it with the earth2mip framework.

References

Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere

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FourCastNet V2 is a deep learning model for weather prediction that uses Spherical Harmonics Neural Operators.","## Details\nThis NGC asset is a FourCastNet v2 SFNO model checkpoint package. Model checkpoint package refers to the set of artifacts needed to run inference using pre-trained model which includes the model checkpoint, set of sample inputs, inference script.\n\n## Architecture\n\nPlease refer to the [reference paper](https://arxiv.org/abs/2306.03838) to learn about\nthe model architecture. In training this checkpoint, Spherical Fourier Neural Operator (SFNO) is applied to forecasting atmospheric dynamics,\nand demonstrates stable autoregressive rollouts for a year of simulated time\n(1,460 steps), while retaining\nphysically plausible dynamics. The SFNO has important implications for machine\nlearning-based simulation of climate dynamics that could eventually help accelerate\nour response to climate change.\n\n\n## Training\n\nThe model is trained on a 73-channel subset of the ERA5 reanalysis data on single levels and\npressure levels that is pre-processed and stored into HDF5 files.\n\n## How to use?\n\nA minimal inference script is provided in this model checkpoint package to get you started easily.\n\nTo run inference on this checkpoint, you can follow the below steps (all the files needed are included in the zip file):\n\n1. Launch Modulus docker container\n\n ```\n docker run --rm --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 --runtime nvidia -v ${PWD}:/examples -it nvcr.io/nvidia/modulus/modulus:23.08\n ```\n2. Download and install earth2mip.\n ```\n git clone https://github.com/NVIDIA/earth2mip.git\n cd earth2mip && pip install . \n ```\n3. Download this checkpoint zip file, and unzip it\n\n ```\n wget 'https://api.ngc.nvidia.com/v2/models/nvidia/modulus/modulus_fcnv2_sm/versions/v0.2/files/fcnv2_sm.zip'\n unzip fcnv2_sm.zip\n ```\n3. Run the simple inference\n\n ```\n cd fcnv2_sm/\n python simple_inference.py\n ```\nYou can also use the model checkpoint from this model checkpoint package to evaluate it with the earth2mip framework.\n\n## References\n\n[Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere](https://arxiv.org/abs/2306.03838)","displayName","Modulus Checkpoints: FourCastNet V2 Small",["D",1694190001636],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/modulus/models/modulus_fcnv2_sm","rawImageUrl","https://pbs.twimg.com/profile_images/1145524454170062848/U4lxVYEw_400x400.png","canGuestDownload","latestVersionSizeInBytes",3150493864,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[242,243,244,245,246,247,248],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_209":276,"_250":277,"_252":278,"_254":255},{"_209":272,"_250":273,"_252":274,"_254":255},{"_209":268,"_250":269,"_252":270,"_254":255},{"_209":265,"_250":266,"_252":267,"_254":255},{"_209":261,"_250":262,"_252":263,"_254":255},{"_209":258,"_250":258,"_252":259,"_254":255},{"_209":249,"_250":251,"_252":253,"_254":255},"Time Series","value","soln_time_series","filter",{"_256":257,"_250":251},"category","label","field","technology","PyTorch",{"_256":260,"_250":258},"framework","PhysicsNeMo","pltfm_modulus",{"_256":264,"_250":262},"platform","Physics and Dynamics Simulation","soln_physics_and_dynamics_simulation",{"_256":257,"_250":266},"HPC / Supercomputing","indus_hpc_supercomputing",{"_256":271,"_250":269},"industry","High Performance Computing","uscs_high_performance_computing",{"_256":275,"_250":273},"application","Forecasting","uscs_forecasting",{"_256":275,"_250":277},{"_120":1256,"_122":1257},"ngcApps",[1209],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1190":1191,"_1192":1193,"_1194":1195,"_1196":1197,"_1198":290,"_151":314,"_1199":1200,"_1201":59,"_1202":99,"_1203":99,"_1204":1205,"_1206":99,"_1207":1208},"amplitude",{"_1187":1188,"_826":-7,"_1189":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1181":1182,"_1183":1184,"_1185":1186},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1066":227,"_1067":1068,"_1069":1070,"_1071":1072},"announcements",[],"consentState",{"_352":1056,"_1057":99,"_1058":34,"_1059":34,"_1060":1061},"consentConfig",{"_334":335,"_336":337,"_338":339,"_340":341,"_342":343,"_344":345,"_346":347,"_348":349,"_350":351,"_352":353},"showContactAdmin",1785615969268,[],[],[328,329,330,331,332],{"_48":63,"_50":74,"_52":115,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":115,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":115,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":53,"_54":55,"_56":333,"_58":59},{"_48":49,"_50":51,"_52":115,"_54":-5,"_56":-5,"_58":59},4,"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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