PhysicsNeMo Checkpoints: DLESyM-V1-ERA5

Description:

DLESyM-V1-ERA5 is an ensemble forecast model for global earth system modeling.\nThis model includes an atmosphere and ocean component, using atmospheric\nvariables as well as the sea-surface temperature on a HEALPix nside=64\n(approximately 1 degree) resolution grid. This model package includes several\nindividual trained checkpoints for the atmosphere and ocean components, which\ncan be used to improve model variability in ensembles. The model architecture\nis a U-Net with padding operations modified to support using the HEALPix grid.

This model is for research and development only.

License/Terms of Use:

Governing Terms: Use of this model is governed by the NVIDIA Community Model License.

Deployment Geography:

Global

Use Case:

Industry, academic, and government research teams interested in subseasonal-to-seasonal weather forecasting, and climate modeling.

Release Date:

NGC 05/12/2025

Reference:

Model Architecture:

Architecture Type: DLESyM uses two UNet architectures adapted to the HEALPix grid,\none for each of the atmosphere and ocean components.
\nNetwork Architecture: UNet

Input:

Input Type:

Tensor (9 surface and pressure-level variables)

Input Format: PyTorch Tensor
\nInput Parameters:

Six Dimensional (6D) (batch, lead time, variable, face, height, width)

Other Properties Related to Input:

Input latitude/longitude grid: 0.25 degree 721 x 1440, regridded to HEALPix nside=64\ngrid in \"XY\" format with a north origin and clockwise order\n
- See HEALPIX_PAD_XY in the earth2grid package\nfor specific details
\n
Input state weather variables: z500, tau300-700, z1000, t2m, tcwv, t850,\nz250, ws10m, sst\n
- tau300-700 (geopotential thickness) is defined as the difference between z300\nand z700 geopotential levels.
- ws10m (wind speed at 10m above surface) is defined as the square root of the sum\nof the squared zonal and meridional wind components, i.e. sqrt(u10m **2 + v10m **2).
\n

For variable name information, review the HRRR Lexicon at Earth2Studio.\nReview the config.yaml provided in the model package for information on the input lead\ntimes required by the model.

Output:

Output Type: Tensor (9 surface and pressure-level variables)
\nOutput Format: Pytorch Tensor
\nOutput Parameters: Six Dimensional (6D) (batch, lead time, variable, face, height,\nwidth)
\nOther Properties Related to Output:

Output latitude/longitude grid: 0.25 degree 721 x 1440, regridded to HEALPix nside=64\ngrid in \"XY\" format with a north origin and clockwise order.\n
- See HEALPIX_PAD_XY in the earth2grid package\nfor specific details
\n
Output state weather variables: z500, tau300-700, z1000, t2m, tcwv, t850,\nz250, ws10m, sst
\n
- tau300-700 (geopotential thickness) is defined as the difference between z300\nand z700 geopotential levels.
- ws10m (wind speed at 10m above surface) is defined as the square root of the sum\nof the squared zonal and meridional wind components, i.e. sqrt(u10m **2 + v10m **2).
\n

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

Software Integration

Runtime Engine: Pytorch
\nSupported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Hopper
NVIDIA Turing

Supported Operating System:

Linux

Model Version:

Model Version: v1

Training, Testing, and Evaluation Datasets:

Total size (in number of data points): 110,960
\nTotal number of datasets: 1
\nDataset partition: training 90%, testing 5%, validation 5%

Training Dataset:

Link: ERA5

Data Collection Method by dataset

Automatic/Sensors

Labeling Method by dataset

Automatic/Sensors

Properties:\nERA5 data for the period 1980-2015. 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. We re-grid to a healpix-64 grid that\ncorresponds to an approximate 1 degree lat/lon grid.

Testing Dataset:

Link: ERA5

Data Collection Method by dataset

Automatic/Sensors

Labeling Method by dataset

Automatic/Sensors

Properties:\nERA5 data for the period 2016-2017. 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. We re-grid to a healpix-64 grid that\ncorresponds to an approximate 1 degree lat/lon grid.

Evaluation Dataset:

Link: ERA5

Data Collection Method by dataset

Automatic/Sensors

Labeling Method by dataset

Automatic/Sensors

Properties:\nERA5 data for the period 2018-2019. 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. We re-grid to a healpix-64 grid that\ncorresponds to an approximate 1 degree lat/lon grid.

Inference:

Acceleration 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.

Please report security vulnerabilities or NVIDIA AI Concerns here.

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\n - [A Deep Learning Earth System Model for Efficient Simulation of the Observed Climate](https://arxiv.org/abs/2409.16247)
\n\n## Model Architecture: \n\n**Architecture Type:** DLESyM uses two UNet architectures adapted to the HEALPix grid,\none for each of the atmosphere and ocean components.
\n**Network Architecture:** UNet
\n\n## Input:\n\n**Input Type:**\n\n- Tensor (9 surface and pressure-level variables)\n\n**Input Format:** PyTorch Tensor
\n**Input Parameters:**\n\n- Six Dimensional (6D) (batch, lead time, variable, face, height, width)
\n\n**Other Properties Related to Input:** \n\n- Input latitude/longitude grid: 0.25 degree 721 x 1440, regridded to HEALPix nside=64\ngrid in \"XY\" format with a north origin and clockwise order\n - See `HEALPIX_PAD_XY` in the [earth2grid package](https://github.com/NVlabs/earth2grid)\n for specific details\n- Input state weather variables: `z500`, `tau300-700`, `z1000`, `t2m`, `tcwv`, `t850`,\n`z250`, `ws10m`, `sst`\n - `tau300-700` (geopotential thickness) is defined as the difference between `z300`\n and `z700` geopotential levels.\n - `ws10m` (wind speed at 10m above surface) is defined as the square root of the sum\n of the squared zonal and meridional wind components, i.e. `sqrt(u10m **2 + v10m **2)`.\n\nFor variable name information, review the `HRRR` Lexicon at [Earth2Studio](https://github.com/NVIDIA/earth2studio).\nReview the `config.yaml` provided in the model package for information on the input lead\ntimes required by the model.\n\n## Output:\n\n**Output Type:** Tensor (9 surface and pressure-level variables)
\n**Output Format:** Pytorch Tensor
\n**Output Parameters:** Six Dimensional (6D) (batch, lead time, variable, face, height,\nwidth)
\n**Other Properties Related to Output:**\n\n- Output latitude/longitude grid: 0.25 degree 721 x 1440, regridded to HEALPix nside=64\ngrid in \"XY\" format with a north origin and clockwise order.\n - See `HEALPIX_PAD_XY` in the [earth2grid package](https://github.com/NVlabs/earth2grid)\n for specific details\n- Output state weather variables: `z500`, `tau300-700`, `z1000`, `t2m`, `tcwv`, `t850`,\n`z250`, `ws10m`, `sst`
\n - `tau300-700` (geopotential thickness) is defined as the difference between `z300`\n and `z700` geopotential levels.\n - `ws10m` (wind speed at 10m above surface) is defined as the square root of the sum\n of the squared zonal and meridional wind components, i.e. `sqrt(u10m **2 + v10m **2)`.\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems.\nBy leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA\nlibraries), the model achieves faster training and inference times compared to\nCPU-only solutions.\n\n## Software Integration\n\n**Runtime Engine:** Pytorch
\n**Supported Hardware Microarchitecture Compatibility:**
\n* NVIDIA Ampere
\n* NVIDIA Hopper
\n* NVIDIA Turing
\n\n\n**Supported Operating System:**\n* Linux
\n\n\n## Model Version: \n\n**Model Version:** v1
\n\n# Training, Testing, and Evaluation Datasets: \n\n**Total size (in number of data points):** 110,960
\n**Total number of datasets:** 1
\n**Dataset partition:** training 90%, testing 5%, validation 5%
\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 1980-2015. 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. We re-grid to a healpix-64 grid that\ncorresponds to an approximate 1 degree lat/lon grid.
\n\n\n## Testing Dataset:\n\n**Link:** [ERA5](https://cds.climate.copernicus.eu/)
\n\n**Data Collection Method by dataset**
\n\n* Automatic/Sensors
\n\n**Labeling Method by dataset**
\n* Automatic/Sensors
\n\n**Properties:**\nERA5 data for the period 2016-2017. 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. We re-grid to a healpix-64 grid that\ncorresponds to an approximate 1 degree lat/lon grid.
\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 period 2018-2019. 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. We re-grid to a healpix-64 grid that\ncorresponds to an approximate 1 degree lat/lon grid.
\n\n## Inference:\n\n**Acceleration 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\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","PhysicsNeMo Checkpoints: DLESyM-V1-ERA5",["D",1747154688460],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/earth-2/models/dlesym-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","","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[247,248,249,250,251,252,253,254,255],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_214":286,"_257":287,"_259":288,"_261":262},{"_214":284,"_257":284,"_259":285,"_261":262},{"_214":281,"_257":282,"_259":283,"_261":262},{"_214":278,"_257":279,"_259":280,"_261":262},{"_214":274,"_257":275,"_259":276,"_261":262},{"_214":272,"_257":272,"_259":273,"_261":262},{"_214":268,"_257":269,"_259":270,"_261":262},{"_214":265,"_257":265,"_259":266,"_261":262},{"_214":256,"_257":258,"_259":260,"_261":262},"Simulation and Modeling","value","uscs_simulation_and_modeling","filter",{"_263":264,"_257":258},"category","label","field","application","PyTorch",{"_263":267,"_257":265},"framework","PhysicsNeMo","pltfm_modulus",{"_263":271,"_257":269},"platform","NSPECT-MZ0N-6PSM",{"_263":262,"_257":272},"HPC / Supercomputing","indus_hpc_supercomputing",{"_263":277,"_257":275},"industry","High Performance Computing","uscs_high_performance_computing",{"_263":264,"_257":279},"Forecasting","uscs_forecasting",{"_263":264,"_257":282},"Earth-2",{"_263":262,"_257":284},"AI","soln_ai",{"_263":289,"_257":287},"technology",{"_121":1421,"_123":1422},"ngcApps",[1374],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1355":1356,"_1357":1358,"_1359":1360,"_1361":1362,"_1363":301,"_152":327,"_1364":1365,"_1366":59,"_1367":89,"_1368":89,"_1369":1370,"_1371":89,"_1372":1373},"amplitude",{"_1352":1353,"_961":-7,"_1354":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0yJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1346":1347,"_1348":1349,"_1350":1351},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1793016314845,"1.28.1","defaultSearchSuggestions",{"_1231":231,"_1232":1233,"_1234":1235,"_1236":1237},"announcements",[],"consentState",{"_364":1221,"_1222":34,"_1223":34,"_1224":89,"_1225":1226},"consentConfig",{"_346":347,"_348":349,"_350":351,"_352":353,"_354":355,"_356":357,"_358":359,"_360":361,"_362":363,"_364":365},"showContactAdmin",1793016314846,[],[],[341,342,343,344,345],{"_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":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. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.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.","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.",[636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666],[602,603,604,605,606,607],{"_406":488,"_214":488,"_185":231,"_378":631},{"_406":463,"_214":463,"_185":231,"_378":626},{"_406":621,"_214":621,"_185":231,"_378":622},{"_406":616,"_214":616,"_185":231,"_378":617},{"_406":612,"_214":612,"_185":231,"_378":613},{"_406":608,"_214":608,"_185":231,"_378":609},"nvcrm.my.site.com",[610],{"_78":611,"_412":608,"_413":34,"_185":231},"BrowserId","nvidia.partners",[614],{"_78":615,"_412":612,"_413":34,"_185":449},"edge","app.hushly.com",[618],{"_78":619,"_412":616,"_413":34,"_185":620},"*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",[623],{"_78":624,"_412":621,"_413":34,"_185":625},"_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.",[736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779],[708,709,710,711,712],{"_406":731,"_214":731,"_185":231,"_378":732},{"_406":726,"_214":726,"_185":231,"_378":727},{"_406":476,"_214":476,"_185":231,"_378":723},{"_406":716,"_214":716,"_185":231,"_378":717},{"_406":621,"_214":621,"_185":231,"_378":713},[714],{"_78":715,"_412":621,"_413":89,"_185":625},"_gh_sess","statcounter.com",[718,719],{"_78":722,"_412":716,"_413":34,"_185":721},{"_78":720,"_412":716,"_413":34,"_185":721},"is_unique","This domain is owned by StatCounter.\nThe main business activity is: Real time visitor analytics","is_visitor_unique",[724],{"_78":725,"_412":476,"_413":89,"_185":482},"YSC","bizible.com",[728],{"_78":729,"_412":726,"_413":34,"_185":730},"_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",[733],{"_78":734,"_412":731,"_413":34,"_185":735},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_78":841,"_412":550,"_413":34,"_185":842},{"_78":839,"_412":550,"_413":89,"_185":840},{"_78":836,"_412":837,"_413":34,"_185":838},{"_78":814,"_412":827,"_413":89,"_185":815},{"_78":834,"_412":550,"_413":34,"_185":835},{"_78":788,"_412":553,"_413":34,"_185":684},{"_78":788,"_412":586,"_413":34,"_185":684},{"_78":832,"_412":550,"_413":34,"_185":833},{"_78":816,"_412":671,"_413":89,"_185":817},{"_78":814,"_412":556,"_413":89,"_185":815},{"_78":830,"_412":550,"_413":34,"_185":831},{"_78":828,"_412":550,"_413":34,"_185":829},{"_78":822,"_412":550,"_413":34,"_185":231},{"_78":813,"_412":827,"_413":34,"_185":457},{"_78":814,"_412":553,"_413":89,"_185":815},{"_78":825,"_412":586,"_413":34,"_185":826},{"_78":823,"_412":676,"_413":34,"_185":824},{"_78":814,"_412":700,"_413":89,"_185":815},{"_78":822,"_412":671,"_413":34,"_185":231},{"_78":786,"_412":553,"_413":34,"_185":787},{"_78":813,"_412":553,"_413":34,"_185":457},{"_78":820,"_412":550,"_413":89,"_185":821},{"_78":818,"_412":550,"_413":34,"_185":819},{"_78":816,"_412":550,"_413":89,"_185":817},{"_78":814,"_412":671,"_413":89,"_185":815},{"_78":813,"_412":671,"_413":34,"_185":457},{"_78":811,"_412":550,"_413":34,"_185":812},{"_78":809,"_412":550,"_413":34,"_185":810},{"_78":807,"_412":550,"_413":89,"_185":808},{"_78":804,"_412":805,"_413":34,"_185":806},{"_78":786,"_412":671,"_413":34,"_185":787},{"_78":802,"_412":550,"_413":89,"_185":803},{"_78":800,"_412":550,"_413":34,"_185":801},{"_78":799,"_412":671,"_413":89,"_185":794},{"_78":797,"_412":550,"_413":89,"_185":798},{"_78":795,"_412":565,"_413":34,"_185":796},{"_78":793,"_412":671,"_413":89,"_185":794},{"_78":791,"_412":550,"_413":34,"_185":792},{"_78":789,"_412":550,"_413":34,"_185":790},{"_78":788,"_412":550,"_413":34,"_185":684},{"_78":786,"_412":565,"_413":34,"_185":787},{"_78":784,"_412":550,"_413":34,"_185":785},{"_78":782,"_412":550,"_413":89,"_185":783},{"_78":780,"_412":550,"_413":34,"_185":781},"_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","Datadog Cookie used to group all events generated from a unique user session across multiple pages. It contains the current session ID, whether the session is excluded due to sampling, and the expiration date of the session. The cookie is extended for an extra 15 minutes every time the user interacts with the website, up to the maximum user session duration (4 hours).","nvweb_A","_ga","This cookie name is associated with Google Universal Analytics - which is a significant update to Google's more commonly used analytics service. This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","s_tp","This cookie is associated with Adobe Analytics. The cookie is used for measuring a visitor’s scroll activity to see how much of a page they view before moving on to another page.","_an_uid","This cookie is likely used for analytics purposes to track user behavior and site usage, which may include unique user identification for tracking.","tp","This cookie is associated with Adobe Analytics site tracking. It registers if the website's scroll-depth detection is active. This function remembers how far the user has scrolled on the current session across the website's sub-pages.","s_ips","_hly_vid","This cookie _hly_vid is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.","s_plt","This cookie is used to measure the time taken for pages to load, allowing website owners to identify slow loading pages and improve user experience.","_session_id","resources.nvidia.com","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. It is used to persist the Hotjar User ID, unique to that site on the browser. This ensures that behavior in subsequent visits to the same site will be attributed to the same user ID.","__sec_*","_hjHasCachedUserAttributes","This cookie name is associated with Hotjar which is used to know whether the data set in _hjUserAttributes Local Storage item is up to date or not.","s_ppv","This cookie is associated with Adobe Analytics site tracking and is used to measure a user's scroll activity to see how much of a page they view before moving to another page.","_gid","This cookie name is associated with Google Universal Analytics. This appears to be a new cookie and as of Spring 2017 no information is available from Google. It appears to store and update a unique value for each page visited.","_hjTLDTest","When the Hotjar script executes we try to determine the most generic cookie path we should use, instead of the page hostname. This is done so that cookies can be shared across subdomains (where applicable). 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This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","nvidiaCart","This domain is owned by NVIDIA Corporation which provides information about the company, its products, solutions, industries, news, events, and career opportunities.","nv_co","__uxq412__ses.88c1","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","learn.learn.nvidia.com","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","This cookie is associated with Akamai. 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It is a temporary cookie used to determine if the browser accepts cookies.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","bm_ss","This cookie is used to help protect websites from automated bot traffic.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. 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