PhysicsNeMo Checkpoints: AFNO_DX_SR-V1-ERA5 (ERA5 AFNO Solar Radiation Diagnostic)

Description:

This is a surface solar irradiance (SSI) diagnostic model checkpoint.\nThis deep learning model, based on the Adaptive Fourier Neural Operator framework\n(AFNO), predicts 6-hour accumulated surface solar irradiance given 24 atmospheric\nvariables plus invariants. For a given atmospheric state at time t, the model\npredicts accumulated SSI 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):

Advancing Solar Irradiance Prediction with NVIDIA Earth-2
\nData-driven Surface Solar Irradiance Estimation using Neural Operators at Global Scale

Model Architecture:

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

Input:

Input Type(s):

Tensor (3 surface variables and 3 pressure-level variables at 7 pressure levels for a total of 24 total 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: t2m, sp, tcwv, z50, z300, z500, z700, z850, z925, z1000, t50, t300, t500, t700, t850, t925, t1000, q50, q300, q500, q700, q850, q925, q1000

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

Output:

Output Type(s): Tensor (1 variable - 6-hour accumulated Surface Solar Irradiance)
\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: ssi

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.

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\n\n## Reference(s):\n\n[Advancing Solar Irradiance Prediction with NVIDIA Earth-2](https://resources.nvidia.com/en-us-energy-surfaces/advancing-solar-irra)
\n[Data-driven Surface Solar Irradiance Estimation using Neural Operators at Global Scale](https://arxiv.org/abs/2411.08843)\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 (3 surface variables and 3 pressure-level variables at 7 pressure levels for a total of 24 total 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: `t2m`, `sp`, `tcwv`, `z50`, `z300`, `z500`, `z700`, `z850`, `z925`, `z1000`, `t50`, `t300`, `t500`, `t700`, `t850`, `t925`, `t1000`, `q50`, `q300`, `q500`, `q700`, `q850`, `q925`, `q1000`\n\nFor lexicon information, review the `ERA5` Lexicon at [Earth2Studio](https://github.com/NVIDIA/earth2studio/blob/main/earth2studio/lexicon/era5.py). `t`, `z` and `q` refer to temperature, geopotential, and specific humidity (respectively). \n\n## Output:\n\n**Output Type(s):** Tensor (1 variable - 6-hour accumulated Surface Solar Irradiance)
\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: `ssi`
\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_SR-V1-ERA5",["D",1748549043145],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/earth-2/models/afno_dx_sr-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 Solar Irradiance).\nDescribe how the model works: | AFNO decodes input variables into a latent space and learns coupling with the output variable - surface solar irradiance.\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 surface solar irradiance 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. Restrictions enforce dataset access during training, and dataset license constraints adhered to.","featureLabels",[],"displayLabels",[245,246,247,248,249,250,251,252],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_210":281,"_254":281,"_256":282,"_258":259},{"_210":277,"_254":278,"_256":279,"_258":259},{"_210":274,"_254":274,"_256":275,"_258":259},{"_210":271,"_254":272,"_256":273,"_258":259},{"_210":269,"_254":269,"_256":270,"_258":259},{"_210":265,"_254":266,"_256":267,"_258":259},{"_210":262,"_254":262,"_256":263,"_258":259},{"_210":253,"_254":255,"_256":257,"_258":259},"Simulation and Modeling","value","uscs_simulation_and_modeling","filter",{"_260":261,"_254":255},"category","label","field","application","PyTorch",{"_260":264,"_254":262},"framework","PhysicsNeMo","pltfm_modulus",{"_260":268,"_254":266},"platform","NSPECT-NXWP-BI4N",{"_260":259,"_254":269},"Forecasting","uscs_forecasting",{"_260":261,"_254":272},"English",{"_260":276,"_254":274},"language","Energy","indus_energy",{"_260":280,"_254":278},"industry","Earth-2",{"_260":259,"_254":281},{"_121":1263,"_123":1264},"ngcApps",[1216],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1197":1198,"_1199":1200,"_1201":1202,"_1203":1204,"_1205":294,"_152":320,"_1206":1207,"_1208":59,"_1209":89,"_1210":89,"_1211":1212,"_1213":89,"_1214":1215},"amplitude",{"_1194":1195,"_833":-7,"_1196":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1188":1189,"_1190":1191,"_1192":1193},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1789299682397,"1.15.0","defaultSearchSuggestions",{"_1073":401,"_1074":1075,"_1076":1077,"_1078":1079},"announcements",[],"consentState",{"_358":1063,"_1064":89,"_1065":34,"_1066":34,"_1067":1068},"consentConfig",{"_340":341,"_342":343,"_344":345,"_346":347,"_348":349,"_350":351,"_352":353,"_354":355,"_356":357,"_358":359},"showContactAdmin",1789299682398,[],[],[334,335,336,337,338,339],{"_48":63,"_50":74,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":68,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":116,"_54":-5,"_56":-5,"_58":59},"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. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. 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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.",[590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614],[561,562,563,564,565],{"_399":475,"_210":475,"_181":401,"_372":585},{"_399":449,"_210":449,"_181":401,"_372":580},{"_399":575,"_210":575,"_181":401,"_372":576},{"_399":570,"_210":570,"_181":401,"_372":571},{"_399":566,"_210":566,"_181":401,"_372":567},"nvidia.partners",[568],{"_78":569,"_406":566,"_407":34,"_181":443},"edge","app.hushly.com",[572],{"_78":573,"_406":570,"_407":34,"_181":574},"*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",[577],{"_78":578,"_406":575,"_407":34,"_181":579},"_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.",[673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701],[653,654,655,656],{"_399":668,"_210":668,"_181":401,"_372":669},{"_399":663,"_210":663,"_181":401,"_372":664},{"_399":462,"_210":462,"_181":401,"_372":660},{"_399":575,"_210":575,"_181":401,"_372":657},[658],{"_78":659,"_406":575,"_407":89,"_181":579},"_gh_sess",[661],{"_78":662,"_406":462,"_407":89,"_181":469},"YSC","bizible.com",[665],{"_78":666,"_406":663,"_407":34,"_181":667},"_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",[670],{"_78":671,"_406":668,"_407":34,"_181":672},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_78":743,"_406":521,"_407":34,"_181":744},{"_78":728,"_406":742,"_407":89,"_181":729},{"_78":711,"_406":524,"_407":34,"_181":636},{"_78":711,"_406":624,"_407":34,"_181":636},{"_78":740,"_406":521,"_407":34,"_181":741},{"_78":728,"_406":527,"_407":89,"_181":729},{"_78":738,"_406":521,"_407":34,"_181":739},{"_78":728,"_406":524,"_407":89,"_181":729},{"_78":736,"_406":623,"_407":34,"_181":737},{"_78":728,"_406":645,"_407":89,"_181":729},{"_78":708,"_406":524,"_407":34,"_181":710},{"_78":734,"_406":521,"_407":89,"_181":735},{"_78":732,"_406":521,"_407":34,"_181":733},{"_78":730,"_406":521,"_407":89,"_181":731},{"_78":728,"_406":619,"_407":89,"_181":729},{"_78":726,"_406":521,"_407":34,"_181":727},{"_78":724,"_406":521,"_407":34,"_181":725},{"_78":722,"_406":521,"_407":89,"_181":723},{"_78":720,"_406":628,"_407":34,"_181":721},{"_78":708,"_406":619,"_407":34,"_181":710},{"_78":718,"_406":521,"_407":34,"_181":719},{"_78":716,"_406":521,"_407":89,"_181":717},{"_78":714,"_406":521,"_407":34,"_181":715},{"_78":712,"_406":521,"_407":34,"_181":713},{"_78":711,"_406":521,"_407":34,"_181":636},{"_78":708,"_406":709,"_407":34,"_181":710},{"_78":706,"_406":521,"_407":34,"_181":707},{"_78":704,"_406":521,"_407":89,"_181":705},{"_78":702,"_406":521,"_407":34,"_181":703},"_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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