PhysicsNeMo Checkpoints: AFNO_DX_TP-V1-ERA5 (ERA5 AFNO Accumulated Total Precipitation Diagnostic Model)

Description:

This is a 6-hour accumulated surface precipitation diagnostic model checkpoint.\nThis deep learning model, based on the Adaptive Fourier Neural Operator framework\n(AFNO), predicts 6-hour accumulated surface precipitation given 20 atmospheric\nvariables plus invariants. For a given atmospheric state at time t, the model\npredicts accumulated precipitation over (t-6h, t).

For training recipes, see NVIDIA PhysicsNeMo. For inference, see NVIDIA Earth2Studio.

This model is ready for commercial use.

License/Terms of Use:

Use of this model is governed by the NVIDIA Community Model License. By pulling and using this model, you accept the terms and conditions of this license.

Reference(s):

FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators

Model Architecture:

Architecture Type: This model uses an Adaptive Fourier Neural Operator architecture.
\nNetwork Architecture: AFNO

Input:

Input Type(s):

Tensor (6 surface variables and 14 pressure-level variables.)
DateTime (NumPy Array)

Input Format(s): PyTorch Tensor / NumPy Array
\nInput Parameters:

Four Dimensional (4D) (batch, variable, latitude, longitude)
DateTime (1D)

Other Properties Related to Input:

Input latitude/longitude grid: 0.25 degree resolution regular latitude/longitude grid (720 x 1440 pixels).
Input state weather variables: u10m, v10m, t2m, sp, msl, t850, u1000, v1000, z1000, u850, v850, z850, u500, v500, z500, t500, z50, q500, q850, tcwv,

For lexicon information, review the ERA5 Lexicon at Earth2Studio. u, v, t, z and q refer to eastward and northward wind components, temperature, geopotential, and specific humidity (respectively).

Output:

Output Type(s): Tensor (1 variable - 6-hour accumulated surface accumulation)
\nOutput Format: Pytorch Tensors
\nOutput Parameters: Four Dimensional (4D) (batch, variable, latitude, longitude)
\nOther Properties Related to Output:

Output latitude/longitude grid: 0.25 degree resolution regular latitude/longitude grid (720 x 1440 pixels).
Output state weather variables: tp06

Software Integration

Runtime Engine(s): PyTorch
\nSupported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Hopper
NVIDIA Turing

Supported Operating System(s):

Linux

Model Version(s):

Model Version: v1

Training, Testing, and Evaluation Datasets:

Training Dataset:

Link: ERA5

** Data Collection Method by dataset

Automatic/Sensors

** Labeling Method by dataset

Automatic/Sensors

Properties:\nERA5 data for the period January 1980 - December 2016. ERA5 provides hourly estimates of various\natmospheric, land, and oceanic climate variables. The data covers the Earth on a 30km\ngrid and resolves the atmosphere at 137 levels.

For more information about ERA5 and a list of known and resolved issues, see the associated documentation and\nreference publication which provides more detailed information about the quality of the ERA5 reanalysis. The ERA5\nreanalysis, and therefore any model trained using it as a data source, is an imperfect representation of the state of global weather, with correlated errors as a function of\nregion and time. Furthermore, the limited spatial and vertical resolution of the dataset means that physical behavior near or below that resolution scale are not accurately resolvable.\nReview ECMWF's publications for further information about the quality of its forecasts.

Testing Dataset:

Link: ERA5

** Data Collection Method by dataset

Automatic/Sensors

** Labeling Method by dataset

Automatic/Sensors

Properties:\nERA5 data for the date range of January 2017 - December 2018. ERA5 provides hourly estimates of various\natmospheric, land, and oceanic climate variables. The data covers the Earth on a 30km\ngrid and resolves the atmosphere at 137 levels.

Evaluation Dataset:

Link: ERA5

** Data Collection Method by dataset

Automatic/Sensors

** Labeling Method by dataset

Automatic/Sensors

Properties:\nERA5 data for the date range of January 2019 - December 2020. ERA5 provides hourly estimates of various\natmospheric, land, and oceanic climate variables. The data covers the Earth on a 30km\ngrid and resolves the atmosphere at 137 levels.

Inference:

Engine: PyTorch
\nTest Hardware:

A100
H100
L40S

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.

For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards [Insert Link to Model Card++ here].

Please report security vulnerabilities or NVIDIA AI Concerns here.

","resourceVersions",[193,194],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_176":28,"_177":-5,"_52":80,"_77":-5,"_81":178,"_83":179,"_180":79,"_90":91,"_86":87,"_181":-5,"_85":34,"_182":-5,"_183":34,"_184":-5,"_185":34,"_186":-5,"_187":34,"_188":-5,"_189":34,"_190":-5,"_191":34,"_88":192},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/earth-2/models/afno_dx_tp-v1-era5/-","version","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/earth-2/afno_dx_tp-v1-era5:v0.1.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/earth-2/models/afno_dx_tp-v1-era5/v0.1.0/version-history","accessCheckResult",{"_175":87},"hasRelatedCollections","context",{"_171":172,"_173":174},[],[],[165,166,167,168,169,170],{"_48":63,"_50":72,"_52":65,"_54":66,"_56":73,"_58":59},{"_48":63,"_50":70,"_52":65,"_54":66,"_56":67,"_58":59},{"_48":63,"_50":68,"_52":65,"_54":66,"_56":69,"_58":59},{"_48":63,"_50":64,"_52":65,"_54":66,"_56":67,"_58":59},{"_48":60,"_50":61,"_52":114,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":57,"_58":59},"orgName","nvidia","teamName","earth-2","allowed","versionId","description",["D",1743781794376],["D",1743781804238],"totalSizeInBytes","malwareScanStatus","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance",[],{"_75":196,"_76":197,"_77":-5,"_86":87,"_85":34,"_88":195,"_183":34},{"_75":28,"_76":28,"_77":-5,"_86":87,"_85":34,"_88":195,"_183":34},[],"-","Latest (v0.1.0)","nvidia/earth-2/afno_dx_tp-v1-era5","key","nvidia/earth-2/models/afno_dx_tp-v1-era5","resourceId","afno_dx_tp-v1-era5","shortDescription","This deep learning model, based on the Adaptive Fourier Neural Operator framework\n(AFNO), predicts 6-hour accumulated surface precipitation given 20 atmospheric\nvariables plus invariants.","# PhysicsNeMo Checkpoints: AFNO_DX_TP-V1-ERA5 (ERA5 AFNO Accumulated Total Precipitation Diagnostic Model)\n\n## Description:\n\nThis is a 6-hour accumulated surface precipitation diagnostic model checkpoint.\nThis deep learning model, based on the Adaptive Fourier Neural Operator framework \n(AFNO), predicts 6-hour accumulated surface precipitation given 20 atmospheric \nvariables plus invariants. For a given atmospheric state at time t, the model\npredicts accumulated precipitation over (t-6h, t).\n\nFor training recipes, see [NVIDIA PhysicsNeMo](https://github.com/NVIDIA/PhysicsNeMo/tree/main/examples/weather/diagnostic). For inference, see [NVIDIA Earth2Studio](https://github.com/NVIDIA/earth2studio).\n\nThis model is ready for commercial use.\n\n### License/Terms of Use: \n\nUse of this model is governed by the [NVIDIA Community Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/). By pulling and using this model, you accept the terms and conditions of this license.
\n\n## Reference(s):\n\n[FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators](https://arxiv.org/abs/2202.11214)\n\n## Model Architecture: \n\n**Architecture Type:** This model uses an Adaptive Fourier Neural Operator architecture.
\n**Network Architecture:** AFNO
\n\n## Input:\n\n**Input Type(s):**\n\n- Tensor (6 surface variables and 14 pressure-level variables.)\n- DateTime (NumPy Array)
\n\n**Input Format(s):** PyTorch Tensor / NumPy Array
\n**Input Parameters:**\n\n- Four Dimensional (4D) (batch, variable, latitude, longitude)
\n- DateTime (1D)\n\n**Other Properties Related to Input:** \n\n- Input latitude/longitude grid: 0.25 degree resolution regular latitude/longitude grid (720 x 1440 pixels).\n- Input state weather variables: `u10m`, `v10m`, `t2m`, `sp`, `msl`, `t850`, `u1000`, `v1000`, `z1000`, `u850`, `v850`, `z850`, `u500`, `v500`, `z500`, `t500`, `z50`, `q500`, `q850`, `tcwv`,\n\nFor lexicon information, review the `ERA5` Lexicon at [Earth2Studio](https://github.com/NVIDIA/earth2studio/blob/main/earth2studio/lexicon/era5.py). `u`, `v`, `t`, `z` and `q` refer to eastward and northward wind components, temperature, geopotential, and specific humidity (respectively).\n\n## Output:\n\n**Output Type(s):** Tensor (1 variable - 6-hour accumulated surface accumulation)
\n**Output Format:** Pytorch Tensors
\n**Output Parameters:** Four Dimensional (4D) (batch, variable, latitude, longitude)
\n**Other Properties Related to Output:**\n\n- Output latitude/longitude grid: 0.25 degree resolution regular latitude/longitude grid (720 x 1440 pixels).\n- Output state weather variables: `tp06`
\n\n## Software Integration\n\n**Runtime Engine(s):** PyTorch
\n**Supported Hardware Microarchitecture Compatibility:**
\n* NVIDIA Ampere
\n* NVIDIA Hopper
\n* NVIDIA Turing
\n\n\n**Supported Operating System(s):**\n* Linux
\n\n\n## Model Version(s): \n\n**Model Version:** v1
\n\n# Training, Testing, and Evaluation Datasets: \n\n## Training Dataset:\n\n**Link:** [ERA5](https://cds.climate.copernicus.eu/)
\n\n** Data Collection Method by dataset
\n* Automatic/Sensors
\n\n** Labeling Method by dataset
\n* Automatic/Sensors
\n\n**Properties:**\nERA5 data for the period January 1980 - December 2016. ERA5 provides hourly estimates of various\natmospheric, land, and oceanic climate variables. The data covers the Earth on a 30km\ngrid and resolves the atmosphere at 137 levels.
\n\nFor more information about ERA5 and a list of known and resolved issues, see the associated [documentation](https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation) and\n[reference publication](https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.3803) which provides more detailed information about the quality of the ERA5 reanalysis. The ERA5\nreanalysis, and therefore any model trained using it as a data source, is an imperfect representation of the state of global weather, with correlated errors as a function of \nregion and time. Furthermore, the limited spatial and vertical resolution of the dataset means that physical behavior near or below that resolution scale are not accurately resolvable. \nReview [ECMWF's publications](https://www.ecmwf.int/en/forecasts/quality-our-forecasts) for further information about the quality of its forecasts.
\n\n## Testing Dataset:\n\n**Link:** [ERA5](https://cds.climate.copernicus.eu/)
\n\n** Data Collection Method by dataset
\n* Automatic/Sensors
\n\n** Labeling Method by dataset
\n* Automatic/Sensors
\n\n**Properties:**\nERA5 data for the date range of January 2017 - December 2018. ERA5 provides hourly estimates of various\natmospheric, land, and oceanic climate variables. The data covers the Earth on a 30km\ngrid and resolves the atmosphere at 137 levels.
\n\nFor more information about ERA5 and a list of known and resolved issues, see the associated [documentation](https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation) and\n[reference publication](https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.3803) which provides more detailed information about the quality of the ERA5 reanalysis. The ERA5\nreanalysis, and therefore any model trained using it as a data source, is an imperfect representation of the state of global weather, with correlated errors as a function of \nregion and time. Furthermore, the limited spatial and vertical resolution of the dataset means that physical behavior near or below that resolution scale are not accurately resolvable. \nReview [ECMWF's publications](https://www.ecmwf.int/en/forecasts/quality-our-forecasts) for further information about the quality of its forecasts.
\n\n## Evaluation Dataset:\n\n**Link:** [ERA5](https://cds.climate.copernicus.eu/)
\n\n** Data Collection Method by dataset
\n* Automatic/Sensors
\n\n** Labeling Method by dataset
\n* Automatic/Sensors
\n\n**Properties:**\nERA5 data for the date range of January 2019 - December 2020. ERA5 provides hourly estimates of various\natmospheric, land, and oceanic climate variables. The data covers the Earth on a 30km\ngrid and resolves the atmosphere at 137 levels.
\n\nFor more information about ERA5 and a list of known and resolved issues, see the associated [documentation](https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation) and\n[reference publication](https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.3803) which provides more detailed information about the quality of the ERA5 reanalysis. The ERA5\nreanalysis, and therefore any model trained using it as a data source, is an imperfect representation of the state of global weather, with correlated errors as a function of \nregion and time. Furthermore, the limited spatial and vertical resolution of the dataset means that physical behavior near or below that resolution scale are not accurately resolvable. \nReview [ECMWF's publications](https://www.ecmwf.int/en/forecasts/quality-our-forecasts) for further information about the quality of its forecasts.
\n\n## Inference:\n\n**Engine:** PyTorch
\n**Test Hardware:**\n* A100
\n* H100
\n* L40S
\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\nFor more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards [Insert Link to Model Card++ here].\n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","PhysicsNeMo Checkpoints: AFNO_DX_TP-V1-ERA5",["D",1743781804240],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/earth-2/models/afno_dx_tp-v1-era5","rawImageUrl","https://www.nvidia.com/content/dam/en-zz/Solutions/about-nvidia/logo-and-brand/01-nvidia-logo-vert-500x200-2c50-p@2x.png","canGuestDownload","latestVersionSizeInBytes","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: | N/A\nMeasures taken to mitigate against unwanted bias: | N/A","explainability","Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Application & Domain: | Weather, Energy Forecasting\nModel Type: | Adaptive Fourier Neural Operator (AFNO)\nIntended User: | Weather and energy scientists or practitioners accelerating predictions using AI.\nOutput: | Tensor (1 variable - 6-hour accumulated surface accumulation).\nDescribe how the model works: | AFNO decodes input variables into a latent space and learns coupling with the output variable - accumulated precipitation.\nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | N/A\nTechnical Limitations: | The model may perform poorly for systems that are not similar to those in the training data, namely for rare weather phenomena or weather behavior outside of the 1980-2016 training dataset. There is no mechanism to enforce physical consistency for predictions.\nVerified to have met prescribed NVIDIA quality standards: | Yes\nPerformance Metrics: | Root Mean Square Error (RMSE), Accuracy (ACC), and Bias (mean error).\nPotential Known Risks: | This model may inaccurately predict accumulated precipitation given technical limitations noted above.\nLicensing: | [NVIDIA Community Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/)","privacy","Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------\nGeneratable or reverse engineerable personally-identifiable information (PII)? | None\nWas consent obtained for any personal data used? | Not Applicable (N/A)\nPersonal data used to create this model? | N/A\nHow often is dataset reviewed? | Before Release\nIs there provenance for all datasets used in training? | Yes\nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes","safetyAndSecurity","Field | Response\n:---------------------------------------------------|:----------------------------------\nModel Application(s): | Weather, Energy Forecasting\nDescribe the life critical impact (if present). | Should not be used for life-critical use cases.\nUse Case Restrictions: | Abide by [NVIDIA Community Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/).\nModel and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. 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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.",4,[586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610],[557,558,559,560,561],{"_394":470,"_206":470,"_177":396,"_368":581},{"_394":444,"_206":444,"_177":396,"_368":576},{"_394":571,"_206":571,"_177":396,"_368":572},{"_394":566,"_206":566,"_177":396,"_368":567},{"_394":562,"_206":562,"_177":396,"_368":563},"nvidia.partners",[564],{"_76":565,"_401":562,"_402":34,"_177":438},"edge","app.hushly.com",[568],{"_76":569,"_401":566,"_402":34,"_177":570},"*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",[573],{"_76":574,"_401":571,"_402":34,"_177":575},"_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. 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