NVIDIA Model Card

EDM-Chaos Overview

cBottle

Description:

cBottle is an diffusion model that generates atmospheric states at kilometer resolution using a cascaded diffusion architecture.

This model is for research and development only.

License/Terms of Use:

Use of this model is governed by the NVIDIA Software and Model Evaluation License Agreement.

Deployment Geography:

Global

Use Case:

Researchers and developers in the field of climate modeling and Earth system science would use this model to generate random images of the Earth's atmosphere, leveraging the techniques described in the paper 'Climate in a Bottle: A generative foundation model for the kilometer-scale atmosphere'.

Release Date:

NGC: 05/12/2025

Model Architecture:

Architecture Type:

Convolutional Neural Network (CNN)

Network Architecture:

Song-UNet on HEALPix geometry

cBottle-3d has 150M parameters.
cBottle-video has 282M parameters.
cBottle-SR has 330M parameters.
cBottle-3d-tc adds only two convolutional layers to the lowest level of the UNet in cBottle-3d, has 150M parameters.

Input:

Input Type(s):

Tensor (dataset label, one-hot encoded)
Tensor (day of year)
Tensor (second of day)
Tensor (monthly mean sea surface temperature, SST)
Tensor (Torpical Cyclone location map, at HPX8 resolution, if using TC guidance)

Input Format(s):

PyTorch Tensor
PyTorch Tensor
PyTorch Tensor
PyTorch Tensor
PyTorch Tensor

Input Parameters:

Tensor: 2D (batch, time_window)
Tensor: 2D (batch, time_window)
Tensor: 4D (batch, num_in_channels, time_window, cell)
Tensor: 4D (batch, 1024)
Tensor: 4D (batch, 1024)

\n\n

dataset label. ERA5 = 1, ICON =0.
day of year in days (0-365)
second of day in seconds (0-86399)
Monthly mean SST input is on the HEALPix 64 Grid
TC location map on HPX8 Grid

Output:

Output Type(s):

Tensor

Output Format:

PyTorch Tensor

Output Parameters:

Four dimensional (batch, channel, time window, cell)

\n\n

Coarse model outputs to HPX 64 grid
SR model outputs to HPX 1024 grid.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Software Integration (Required For NVIDIA Models Only):

Runtime Engine(s):

PyTorch

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Blackwell
NVIDIA Hopper
NVIDIA Turing

[Preferred/Supported] Operating System(s):

['Linux']

Model Version(s):

Model version: v1.2

Training, Testing, and Evaluation Datasets:

Total Number of Datasets:

4 Datasets:

ICON Cycle 3
inputs4MIPs
ERA5
IBTrACS

Dataset Partition:

Training:

ICON: 2020-01-20 03:00:00 through 2024-03-06 12:00:00 (inclusive)
ERA5/inputs4MIPS/IBTrACS: Years 1980--2017 (inclusive)

Validation:

ICON: 2020-01-20 03:00:00 through 2024-03-06 12:00:00 (inclusive)
ERA5/inputs4MIPs/IBTrACS: Years 2018

Evaluation

ICON: no independent data withheld for evaluation
ERA5/inputs4MIPS/IBTrACS: all other years before 1980 and after 2018.

Data Processing Description:

ERA5 For pre-processing, we retrieved hourly ERA5 data for 1980--2018 (inclusive) via the data lake at the National Energy Research Computing Center (NERSC) at 0.25 degree resolution on the lat-lon grid. The 3d atmospheric states are available on pressure levels. We then regridded this using bilinear interpolation to the HEALPix grid with Nside=256. For training the coarse-resolution models, we coarsened ERA5 by pooling to Nside=64 and transformed to zarr format.

ICON We obtained O(PB) of ICON data in zarr format from MPI-M. This dataset\nwas stored on the HEALPix grid with Nside = 1024. Prior to our handling of the data it was\ninterpolated using nearest neighbors from the native icosahedral grid. Five years\nof this data are available at 3-hour (3D) and 30-minute (2D) resolution in time.\nConveniently, this data-set featured pre-coarsened data (again using averaging\npooling) at our coarse-resolution of Nside = 64. We interpolated this coarse\ndata to fixed pressure levels using linear interpolation in the vertical direction.\nTo fill in values at pressure level locations that are below the surface, we use\nextrapolation based on hydrostatic balance constraints and an assumption of a\nconstant temperature lapse rate of 6.5 K/km for temperature and geo-potential.\nFor all other variables, we use constant extrapolation down from the surface.\nThis procedure approximately reproduces ERA5’s undocumented procedure for\nfilling-in below-surface levels.

Public Datasets:

ERA5
ICON
inputs4MIPs (tosbcs)
IBTrACS

Training, Testing, and Evaluation Datasets:

The following datasets were used for Training, Testing, and Evaluation:

Link:

ERA5: https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5
ICON Cycle 3: https://www.wdc-climate.de/ui/entry?acronym=nextGEMS_cyc3
inputs4MIP: https://pcmdi.llnl.gov/mips/input4MIPs/
IBTrACS: https://www.ncei.noaa.gov/products/international-best-track-archive

Data Collection Method by dataset

Automatic/sensors

Labeling Method by dataset

Automatic/sensors

Properties (Quantity, Dataset Descriptions, Sensor(s)):

ERA

ERA5 is a commonly used dataset that represent the best guess of the global atmospheric state at a coarse resolution of 25 km. Briefly, it is created by running a data assimilation algorithm that incorporates observations into the European Center for Medium Range Weather Forecasting's (ECMWF) numerical model. However, it is not available at a high enough resolution in space or time to resolve meso-scale motions on kilometer scale, and in particular deep convection. The ICON simulation fills in this gap since it is available at a global 5km resolution, but is not paired with reality. See Tab. \\ref{tab:datasets} for a detailed overview of the datasets. We retreived data from 1980--2018.

ICON Cycle 3\nThe ICON data is a free-running simulation with the ICON atmospheric model coupled to a dynamic ocean and land. The atmospheric component solves the nonhydrostatic fluid mechanics equations on a global ico-sahedral mesh with a resolution of around 5 km. Unlike ERA5 the model explicitly resolves certain convective motions so a convection parameterization is not used. So even beyond the simple increase in resolution, we expect this to increase the fidelity of the precipitation and cloud fields in this dataset relative to ERA5 where these processes are parameterized. Furthermore, because the ocean and land are dynamically coupled to the atmosphere, we expect this run to obey conservation of heat, momentum and moisture between these different components, unlike in the ERA5 where the observations (rather than conservations) are king.

Testing Dataset:

Same details as Training Dataset. This period was reserved for tuning the model hyperparameters

ICON: 2020-01-20 03:00:00 through 2024-03-06 12:00:00 (inclusive)
ERA5/inputs4MIPs: Years 2018

Evaluation Dataset:

ICON: no independent data withheld for evaluation
ERA5/inputs4MIPS: all other years before 1980 and after 2018.

Inference:

Engine:

PyTorch

Test Hardware:

A100, H100

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please report security vulnerabilities or NVIDIA AI Concerns here.

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ERA5 = 1, ICON =0. \n- day of year in days (0-365)\n- second of day in seconds (0-86399)\n- Monthly mean SST input is on the HEALPix 64 Grid\n- TC location map on HPX8 Grid\n\n\n\n## Output:\n\n### Output Type(s):\n\n- Tensor\n\n### Output Format:\n\n- PyTorch Tensor\n\n### Output Parameters:\n\n- Four dimensional (batch, channel, time window, cell)\n\n### Other Properties Related to Output:\n\n- Coarse model outputs to HPX 64 grid\n- SR model outputs to HPX 1024 grid.\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.\n\n## Software Integration (Required For NVIDIA Models Only):\n\n### Runtime Engine(s):\n\nPyTorch\n\n### Supported Hardware Microarchitecture Compatibility:\n\n- NVIDIA Ampere\n- NVIDIA Blackwell\n- NVIDIA Hopper\n- NVIDIA Turing\n\n### [Preferred/Supported] Operating System(s):\n\n['Linux']\n\n## Model Version(s):\n\n- **Model version**: v1.2\n\n## Training, Testing, and Evaluation Datasets:\n\n#### Total Number of Datasets:\n\n4 Datasets:\n\n- ICON Cycle 3\n- inputs4MIPs\n- ERA5\n- IBTrACS\n\n##### Dataset Partition:\n\n##### Training:\n\n- ICON: 2020-01-20 03:00:00 through 2024-03-06 12:00:00 (inclusive)\n- ERA5/inputs4MIPS/IBTrACS: Years 1980--2017 (inclusive)\n\n##### Validation:\n\n- ICON: 2020-01-20 03:00:00 through 2024-03-06 12:00:00 (inclusive)\n- ERA5/inputs4MIPs/IBTrACS: Years 2018\n\n##### Evaluation\n\n- ICON: no independent data withheld for evaluation\n- ERA5/inputs4MIPS/IBTrACS: all other years before 1980 and after 2018.\n\n#### Data Processing Description:\n\n\n**ERA5** For pre-processing, we retrieved hourly ERA5 data for 1980--2018 (inclusive) via the data lake at the National Energy Research Computing Center (NERSC) at 0.25 degree resolution on the lat-lon grid. The 3d atmospheric states are available on pressure levels. We then regridded this using bilinear interpolation to the HEALPix grid with Nside=256. For training the coarse-resolution models, we coarsened ERA5 by pooling to Nside=64 and transformed to zarr format.\n\n**ICON** We obtained O(PB) of ICON data in zarr format from MPI-M. This dataset\nwas stored on the HEALPix grid with Nside = 1024. Prior to our handling of the data it was\ninterpolated using nearest neighbors from the native icosahedral grid. Five years\nof this data are available at 3-hour (3D) and 30-minute (2D) resolution in time.\nConveniently, this data-set featured pre-coarsened data (again using averaging\npooling) at our coarse-resolution of Nside = 64. We interpolated this coarse\ndata to fixed pressure levels using linear interpolation in the vertical direction.\nTo fill in values at pressure level locations that are below the surface, we use\nextrapolation based on hydrostatic balance constraints and an assumption of a\nconstant temperature lapse rate of 6.5 K/km for temperature and geo-potential.\nFor all other variables, we use constant extrapolation down from the surface.\nThis procedure approximately reproduces ERA5’s undocumented procedure for\nfilling-in below-surface levels. \n\n#### Public Datasets:\n\n- ERA5\n- ICON\n- inputs4MIPs (tosbcs)\n- IBTrACS\n\n#### Training, Testing, and Evaluation Datasets:\n\nThe following datasets were used for Training, Testing, and Evaluation:\n\n#### Link:\n\n- ERA5: https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5\n- ICON Cycle 3: https://www.wdc-climate.de/ui/entry?acronym=nextGEMS_cyc3\n- inputs4MIP: https://pcmdi.llnl.gov/mips/input4MIPs/ \n- IBTrACS: https://www.ncei.noaa.gov/products/international-best-track-archive\n\n#### Data Collection Method by dataset\n\nAutomatic/sensors\n\n#### Labeling Method by dataset\n\nAutomatic/sensors\n\n#### Properties (Quantity, Dataset Descriptions, Sensor(s)):\n\n**ERA**\n\nERA5 is a commonly used dataset that represent the best guess of the global atmospheric state at a coarse resolution of 25 km. Briefly, it is created by running a data assimilation algorithm that incorporates observations into the European Center for Medium Range Weather Forecasting's (ECMWF) numerical model. However, it is not available at a high enough resolution in space or time to resolve meso-scale motions on kilometer scale, and in particular deep convection. The ICON simulation fills in this gap since it is available at a global 5km resolution, but is not paired with reality. See Tab. \\ref{tab:datasets} for a detailed overview of the datasets. We retreived data from 1980--2018.\n\n\n**ICON Cycle 3**\nThe ICON data is a free-running simulation with the ICON atmospheric model coupled to a dynamic ocean and land. The atmospheric component solves the nonhydrostatic fluid mechanics equations on a global ico-sahedral mesh with a resolution of around 5 km. Unlike ERA5 the model explicitly resolves certain convective motions so a convection parameterization is not used. So even beyond the simple increase in resolution, we expect this to increase the fidelity of the precipitation and cloud fields in this dataset relative to ERA5 where these processes are parameterized. Furthermore, because the ocean and land are dynamically coupled to the atmosphere, we expect this run to obey conservation of heat, momentum and moisture between these different components, unlike in the ERA5 where the observations (rather than conservations) are king.\n\n#### Testing Dataset:\n\nSame details as Training Dataset. This period was reserved for tuning the model hyperparameters\n\n- ICON: 2020-01-20 03:00:00 through 2024-03-06 12:00:00 (inclusive)\n- ERA5/inputs4MIPs: Years 2018\n\n\n#### Evaluation Dataset:\n\n- ICON: no independent data withheld for evaluation\n- ERA5/inputs4MIPS: all other years before 1980 and after 2018.\n\n## Inference:\n\n### Engine:\n\nPyTorch\n\n### Test Hardware:\n\n- A100, H100\n\n\n## Ethical Considerations:\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","Climate in a Bottle",["D",1755554885533],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/earth-2/models/cbottle","rawImageUrl","https://gitlab-master.nvidia.com/nbrenowitz/edm-chaos/-/wikis/uploads/0ce64cd2604dad5640b503a93a2bea13/tas.jpeg","canGuestDownload","latestVersionSizeInBytes",11264294141,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[257],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_224":258,"_259":258,"_260":261,"_262":263},"NSPECT-VQQY-A552","value","filter",{"_264":263,"_259":258},"category","label","field",{"_119":1245,"_121":1246},"ngcApps",[1198],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1179":1180,"_1181":1182,"_1183":1184,"_1185":1186,"_1187":276,"_150":302,"_1188":1189,"_1190":56,"_1191":98,"_1192":98,"_1193":1194,"_1195":98,"_1196":1197},"amplitude",{"_1176":1177,"_813":-7,"_1178":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1170":1171,"_1172":1173,"_1174":1175},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1789398882355,"1.15.0","defaultSearchSuggestions",{"_1053":178,"_1054":1055,"_1056":1057,"_1058":1059},"announcements",[],"consentState",{"_340":1043,"_1044":98,"_1045":31,"_1046":31,"_1047":1048},"consentConfig",{"_322":323,"_324":325,"_326":327,"_328":329,"_330":331,"_332":333,"_334":335,"_336":337,"_338":339,"_340":341},"showContactAdmin",[],[],[315,316,317,318,319,320],{"_45":59,"_47":70,"_49":114,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":68,"_49":114,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":66,"_49":114,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":114,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":114,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":321,"_55":56},5,"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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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. 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.",[570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594],[541,542,543,544,545],{"_380":455,"_224":455,"_177":178,"_354":565},{"_380":429,"_224":429,"_177":178,"_354":560},{"_380":555,"_224":555,"_177":178,"_354":556},{"_380":550,"_224":550,"_177":178,"_354":551},{"_380":546,"_224":546,"_177":178,"_354":547},"nvidia.partners",[548],{"_80":549,"_386":546,"_387":31,"_177":423},"edge","app.hushly.com",[552],{"_80":553,"_386":550,"_387":31,"_177":554},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[557],{"_80":558,"_386":555,"_387":31,"_177":559},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681],[633,634,635,636],{"_380":648,"_224":648,"_177":178,"_354":649},{"_380":643,"_224":643,"_177":178,"_354":644},{"_380":442,"_224":442,"_177":178,"_354":640},{"_380":555,"_224":555,"_177":178,"_354":637},[638],{"_80":639,"_386":555,"_387":98,"_177":559},"_gh_sess",[641],{"_80":642,"_386":442,"_387":98,"_177":449},"YSC","bizible.com",[645],{"_80":646,"_386":643,"_387":31,"_177":647},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[650],{"_80":651,"_386":648,"_387":31,"_177":652},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_80":723,"_386":501,"_387":31,"_177":724},{"_80":708,"_386":722,"_387":98,"_177":709},{"_80":691,"_386":504,"_387":31,"_177":616},{"_80":691,"_386":604,"_387":31,"_177":616},{"_80":720,"_386":501,"_387":31,"_177":721},{"_80":708,"_386":507,"_387":98,"_177":709},{"_80":718,"_386":501,"_387":31,"_177":719},{"_80":708,"_386":504,"_387":98,"_177":709},{"_80":716,"_386":603,"_387":31,"_177":717},{"_80":708,"_386":625,"_387":98,"_177":709},{"_80":688,"_386":504,"_387":31,"_177":690},{"_80":714,"_386":501,"_387":98,"_177":715},{"_80":712,"_386":501,"_387":31,"_177":713},{"_80":710,"_386":501,"_387":98,"_177":711},{"_80":708,"_386":599,"_387":98,"_177":709},{"_80":706,"_386":501,"_387":31,"_177":707},{"_80":704,"_386":501,"_387":31,"_177":705},{"_80":702,"_386":501,"_387":98,"_177":703},{"_80":700,"_386":608,"_387":31,"_177":701},{"_80":688,"_386":599,"_387":31,"_177":690},{"_80":698,"_386":501,"_387":31,"_177":699},{"_80":696,"_386":501,"_387":98,"_177":697},{"_80":694,"_386":501,"_387":31,"_177":695},{"_80":692,"_386":501,"_387":31,"_177":693},{"_80":691,"_386":501,"_387":31,"_177":616},{"_80":688,"_386":689,"_387":31,"_177":690},{"_80":686,"_386":501,"_387":31,"_177":687},{"_80":684,"_386":501,"_387":98,"_177":685},{"_80":682,"_386":501,"_387":31,"_177":683},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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Name	Size	Updated	Actions
cBottle-3d.zip	1.66 GB	May 12, 2025 UTC	curl wget Download
cBottle-SR.zip	2.46 GB	May 12, 2025 UTC	curl wget Download
cBottle-video.zip	3.15 GB	May 12, 2025 UTC	curl wget Download