Details

This NGC asset is a DLWP Cubesphere 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

DLWP is a light-weight deep learning model for weather prediction that uses Cube-sphere grid. Re-implemented from: https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2021MS002502.

The DLWP model can be used to predict the state of the atmosphere given a previous\natmospheric state. You can infer a 320-member ensemble set of six-week forecasts at 1.4°\nresolution within a couple of minutes, demonstrating the potential of AI in developing\nnear real-time digital twins for weather prediction.

Please refer Sub-Seasonal Forecasting With a Large Ensemble of Deep-Learning Weather Prediction Models\npaper for additional details.

Training

The model is trained on 7-channel subset of ERA5 Data that is mapped onto a cubed sphere\ngrid with a resolution of 64x64 grid cells.\nThe model uses years 1980-2015 for training, 2016-2017 for validation\nand 2018 for out of sample testing. The training scripts for the problem can be found at: https://github.com/NVIDIA/modulus-launch/tree/main/examples/weather/dlwp

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:23.09\n

Download this checkpoint zip file, and unzip it

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

\n
Run the simple inference
\n
```
cd dlwp/\npython simple_inference.py\n
```
\n

Yu can also use the model checkpoint from this model checkpoint package to evalue it with the earth2mip framework.

References

Sub-Seasonal Forecasting With a Large Ensemble of Deep-Learning Weather Prediction Models

Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere

Arbitrary-Order Conservative and Consistent Remapping and a Theory of Linear Maps: Part 1

Arbitrary-Order Conservative and Consistent Remapping and a Theory of Linear Maps, Part 2

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DLWP is a light-weight deep learning model for weather prediction that uses Cube-sphere grid.","## Details\nThis NGC asset is a DLWP Cubesphere 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\nDLWP is a light-weight deep learning model for weather prediction that uses Cube-sphere grid. Re-implemented from: https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2021MS002502.\n\nThe DLWP model can be used to predict the state of the atmosphere given a previous\natmospheric state. You can infer a 320-member ensemble set of six-week forecasts at 1.4°\nresolution within a couple of minutes, demonstrating the potential of AI in developing\nnear real-time digital twins for weather prediction.\n\nPlease refer [Sub-Seasonal Forecasting With a Large Ensemble of Deep-Learning Weather Prediction Models](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021MS002502)\npaper for additional details.\n\n## Training\n\nThe model is trained on 7-channel subset of ERA5 Data that is mapped onto a cubed sphere\ngrid with a resolution of 64x64 grid cells.\nThe model uses years 1980-2015 for training, 2016-2017 for validation\nand 2018 for out of sample testing. The training scripts for the problem can be found at: https://github.com/NVIDIA/modulus-launch/tree/main/examples/weather/dlwp\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:23.09\n ```\n2. Download this checkpoint zip file, and unzip it\n\n ```\n wget 'https://api.ngc.nvidia.com/v2/models/nvidia/modulus/modulus_dlwp_cubesphere/versions/v0.2/files/dlwp_cubesphere.zip'\n unzip dlwp_cubesphere.zip\n ```\n3. Run the simple inference\n\n ```\n cd dlwp/\n python simple_inference.py\n ```\nYu can also use the model checkpoint from this model checkpoint package to evalue it with the earth2mip framework.\n\n## References\n\n[Sub-Seasonal Forecasting With a Large Ensemble of Deep-Learning Weather Prediction Models](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021MS002502)\n\n[Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020MS002109)\n\n[Arbitrary-Order Conservative and Consistent Remapping and a Theory of Linear Maps: Part 1](https://journals.ametsoc.org/view/journals/mwre/143/6/mwr-d-14-00343.1.xml)\n\n[Arbitrary-Order Conservative and Consistent Remapping and a Theory of Linear Maps, Part 2](https://journals.ametsoc.org/view/journals/mwre/144/4/mwr-d-15-0301.1.xml)","displayName","Modulus Checkpoints: DLWP Cubesphere",["D",1695749414321],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/modulus/models/modulus_dlwp_cubesphere","rawImageUrl","https://pbs.twimg.com/profile_images/1145524454170062848/U4lxVYEw_400x400.png","canGuestDownload","latestVersionSizeInBytes",64666084,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[243,244,245,246,247,248,249],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_209":277,"_251":278,"_253":279,"_255":256},{"_209":273,"_251":274,"_253":275,"_255":256},{"_209":269,"_251":270,"_253":271,"_255":256},{"_209":266,"_251":267,"_253":268,"_255":256},{"_209":262,"_251":263,"_253":264,"_255":256},{"_209":259,"_251":259,"_253":260,"_255":256},{"_209":250,"_251":252,"_253":254,"_255":256},"Time Series","value","soln_time_series","filter",{"_257":258,"_251":252},"category","label","field","technology","PyTorch",{"_257":261,"_251":259},"framework","PhysicsNeMo","pltfm_modulus",{"_257":265,"_251":263},"platform","Physics and Dynamics Simulation","soln_physics_and_dynamics_simulation",{"_257":258,"_251":267},"HPC / Supercomputing","indus_hpc_supercomputing",{"_257":272,"_251":270},"industry","High Performance Computing","uscs_high_performance_computing",{"_257":276,"_251":274},"application","Forecasting","uscs_forecasting",{"_257":276,"_251":278},{"_106":1454,"_108":1455},"ngcApps",[1407],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1388":1389,"_1390":1391,"_1392":1393,"_1394":1395,"_1396":291,"_138":315,"_1397":1398,"_1399":55,"_1400":85,"_1401":85,"_1402":1403,"_1404":85,"_1405":1406},"amplitude",{"_1385":1386,"_988":-7,"_1387":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1379":1380,"_1381":1382,"_1383":1384},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1263":228,"_1264":1265,"_1266":1267,"_1268":1269},"announcements",[],"consentState",{"_353":1253,"_1254":85,"_1255":30,"_1256":30,"_1257":1258},"consentConfig",{"_335":336,"_337":338,"_339":340,"_341":342,"_343":344,"_345":346,"_347":348,"_349":350,"_351":352,"_353":354},"showContactAdmin",1784102685969,[],[],[329,330,331,332,333,334],{"_44":58,"_46":69,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":67,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":65,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":59,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":101,"_50":-5,"_52":-5,"_54":55},"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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These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",4,[626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646],[591,592,593,594,595],{"_393":485,"_209":485,"_167":228,"_367":617},{"_393":451,"_209":451,"_167":228,"_367":612},{"_393":605,"_209":605,"_167":228,"_367":606},{"_393":601,"_209":601,"_167":228,"_367":602},{"_393":596,"_209":596,"_167":228,"_367":597},"nvcrm.my.site.com",[598],{"_77":599,"_399":596,"_400":30,"_167":600},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[603],{"_77":604,"_399":601,"_400":30,"_167":434},"edge","github.com",[607,608],{"_77":611,"_399":605,"_400":30,"_167":610},{"_77":609,"_399":605,"_400":30,"_167":610},"logged_in","This domain is owned by Github. 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