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Field	Response
Participation considerations from adversely impacted groups protected classes in model design and testing:	Not Applicable (N/A)
Bias Metric (If Measured) :	N/A
Which characteristic (feature) show(s) the greatest difference in performance?:	N/A

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Earth-2 Checkpoints: FourCastNet 3

Description:

FourCastNet 3 advances global weather modeling by implementing a scalable, geometric\nmachine learning (ML) approach to probabilistic ensemble forecasting. The approach is\ndesigned to respect spherical geometry and to accurately model the spatially\ncorrelated probabilistic nature of the problem, resulting in stable spectra and\nrealistic dynamics across multiple scales. FourCastNet 3 delivers forecasting accuracy\nthat surpasses leading conventional ensemble models and rivals the best diffusion-based\nmethods, while producing forecasts 8 to 60 times faster than these approaches. In\ncontrast to other ML approaches, FourCastNet 3 demonstrates excellent probabilistic\ncalibration and retains realistic spectra, even at extended lead times of up to 60 days.\nAll of these advances are realized using a purely convolutional neural network\narchitecture specifically tailored for spherical geometry. Scalable and eﬃcient\nlarge-scale training on 1024 GPUs and more is enabled by a novel training paradigm for\ncombined model- and data-parallelism, inspired by domain decomposition methods in\nclassical numerical models. Additionally, FourCastNet 3 enables rapid inference on a\nsingle GPU, producing a 60-day global forecast at 0.25°, 6-hourly resolution in under\n4 minutes. Its computational efficiency, medium-range probabilistic skill, spectral\nfidelity, and rollout stability at subseasonal timescales make it a strong candidate\nfor improving meteorological forecasting and early warning systems through large\nensemble predictions.

$\"FCN3$

This model is ready for commercial/non-commercial use.

License/Terms of Use:

Apache 2.0 license

Deployment Geography:

Global

Use Case:

Industry, academic, and government research teams interested in medium-range and\nsubseasonal-to-seasonal weather forecasting, and climate modeling.

Release Date:

NGC 07/18/2025

Reference:

Papers:

Code:

\n
Makani
\n
\n
PhysicsNeMo
\n
\n
Earth2Studio
\n
\n
torch-harmonics
\n

Model Architecture:

Architecture Type: Spherical Neural Operator. A fully convolutional architecture\nbased on group convolutions defined on the sphere. Leverages both local and global\nconvolutions. For details regarding the architecture refer to the\nFourCastNet 3 paper.

Network Architecture: N/A

Number of model parameters: 710,867,670

Model datatype: We recommend that the model is run in AMP with bf16, however, the\ninputs and outputs are typically float32.

Input:

Input Type:

Tensor (72 surface and pressure-level variables)

Input Format: PyTorch Tensor
\nInput Parameters:

Six Dimensional (6D) (batch, time, lead time, variable, latitude, longitude)

Other Properties Related to Input:

Input equi-rectangular latitude/longitude grid: 0.25 degree 721 x 1440
Input state weather variables: u10m, v10m, u100m, v100m, t2m, msl,\ntcwv, u50, u100, u150, u200, u250, u300, u400, u500, u600, u700,\nu850, u925, u1000, v50, v100, v150, v200, v250, v300, v400, v500,\nv600, v700, v850, v925, v1000, z50, z100, z150, z200, z250, z300,\nz400, z500, z600, z700, z850, z925, z1000, t50, t100, t150, t200,\nt250, t300, t400, t500, t600, t700, t850, t925, t1000, q50, q100,\nq150, q200, q250, q300, q400, q500, q600, q700, q850, q925, q1000
Time: datetime64

For variable name information, review the Lexicon at Earth2Studio.

Output:

Output Type: Tensor (72 surface and pressure-level variables)
\nOutput Format: Pytorch Tensor
\nOutput Parameters: Six Dimensional (6D) (batch, time, lead time, variable,\nlatitude, longitude)
\nOther Properties Related to Output:

Output latitude/longitude grid: 0.25 degree 721 x 1440, same as input.
Output state weather variables: same as above.

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 95%, testing 2.5%, validation 2.5%

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.

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.

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.

Inference:

Acceleration Engine: Pytorch
\nTest Hardware:

A100
H100
L40S

Ethical Considerations:

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

For more detailed information on ethical considerations for this model, please see the\nModel Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.

Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

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The approach is\ndesigned to respect spherical geometry and to accurately model the spatially\ncorrelated probabilistic nature of the problem, resulting in stable spectra and\nrealistic dynamics across multiple scales. FourCastNet 3 delivers forecasting accuracy\nthat surpasses leading conventional ensemble models and rivals the best diffusion-based\nmethods, while producing forecasts 8 to 60 times faster than these approaches. In\ncontrast to other ML approaches, FourCastNet 3 demonstrates excellent probabilistic\ncalibration and retains realistic spectra, even at extended lead times of up to 60 days.\nAll of these advances are realized using a purely convolutional neural network\narchitecture specifically tailored for spherical geometry. Scalable and eﬃcient\nlarge-scale training on 1024 GPUs and more is enabled by a novel training paradigm for\ncombined model- and data-parallelism, inspired by domain decomposition methods in\nclassical numerical models. Additionally, FourCastNet 3 enables rapid inference on a\nsingle GPU, producing a 60-day global forecast at 0.25°, 6-hourly resolution in under\n4 minutes. Its computational efficiency, medium-range probabilistic skill, spectral\nfidelity, and rollout stability at subseasonal timescales make it a strong candidate\nfor improving meteorological forecasting and early warning systems through large\nensemble predictions.\n\n![FCN3 15 member ensemble](https://raw.githubusercontent.com/NVIDIA/makani/main/images/fcn3_ens15.gif)\n\nThis model is ready for commercial/non-commercial use.\n\n### License/Terms of Use:\n\n[Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0)\n\n### Deployment Geography:\n\nGlobal\n\n### Use Case:\n\nIndustry, academic, and government research teams interested in medium-range and\nsubseasonal-to-seasonal weather forecasting, and climate modeling.\n\n### Release Date:\n\nNGC 07/18/2025\n\n## Reference:\n\n**Papers**:\n\n- [FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale](https://arxiv.org/abs/2507.12144v2)\n- [Neural Operators with Localized Integral and Differential Kernels](https://arxiv.org/abs/2402.16845)\n- [Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operators](https://arxiv.org/abs/2408.03100)\n- [Huge Ensembles Part II: Properties of a Huge Ensemble of Hindcasts Generated with Spherical Fourier Neural Operators](https://arxiv.org/abs/2408.01581)\n- [Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere](https://arxiv.org/abs/2306.03838)\n\n**Code**:\n\n- [Makani](https://github.com/NVIDIA/makani)\n\n- [PhysicsNeMo](https://github.com/NVIDIA/physicsnemo)\n\n- [Earth2Studio](https://github.com/NVIDIA/earth2studio)\n\n- [torch-harmonics](https://github.com/NVIDIA/torch-harmonics)\n\n## Model Architecture:\n\n**Architecture Type:** Spherical Neural Operator. A fully convolutional architecture\nbased on group convolutions defined on the sphere. Leverages both local and global\nconvolutions. For details regarding the architecture refer to the\n[FourCastNet 3 paper](https://arxiv.org/abs/2507.12144v1).
\n\n**Network Architecture:** N/A
\n\n**Number of model parameters:** 710,867,670\n\n**Model datatype:** We recommend that the model is run in AMP with bf16, however, the\ninputs and outputs are typically float32.\n\n## Input:\n\n**Input Type:**\n\n- Tensor (72 surface and pressure-level variables)\n\n**Input Format:** PyTorch Tensor
\n**Input Parameters:**\n\n- Six Dimensional (6D) (batch, time, lead time, variable, latitude, longitude)
\n\n**Other Properties Related to Input:**\n\n- Input equi-rectangular latitude/longitude grid: 0.25 degree 721 x 1440\n- Input state weather variables: `u10m`, `v10m`, `u100m`, `v100m`, `t2m`, `msl`,\n`tcwv`, `u50`, `u100`, `u150`, `u200`, `u250`, `u300`, `u400`, `u500`, `u600`, `u700`,\n`u850`, `u925`, `u1000`, `v50`, `v100`, `v150`, `v200`, `v250`, `v300`, `v400`, `v500`,\n`v600`, `v700`, `v850`, `v925`, `v1000`, `z50`, `z100`, `z150`, `z200`, `z250`, `z300`,\n`z400`, `z500`, `z600`, `z700`, `z850`, `z925`, `z1000`, `t50`, `t100`, `t150`, `t200`,\n`t250`, `t300`, `t400`, `t500`, `t600`, `t700`, `t850`, `t925`, `t1000`, `q50`, `q100`,\n`q150`, `q200`, `q250`, `q300`, `q400`, `q500`, `q600`, `q700`, `q850`, `q925`, `q1000`\n- Time: datetime64\n\nFor variable name information, review the Lexicon at [Earth2Studio](https://github.com/NVIDIA/earth2studio).\n\n## Output:\n\n**Output Type:** Tensor (72 surface and pressure-level variables)
\n**Output Format:** Pytorch Tensor
\n**Output Parameters:** Six Dimensional (6D) (batch, time, lead time, variable,\n latitude, longitude)
\n**Other Properties Related to Output:**\n\n- Output latitude/longitude grid: 0.25 degree 721 x 1440, same as input.\n- Output state weather variables: same as above.\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\n- NVIDIA Ampere
\n- NVIDIA Hopper
\n- NVIDIA Turing
\n\n**Supported Operating System:**\n\n- Linux
\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 95%, testing 2.5%, validation 2.5%
\n\n## Training 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\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.
\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\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.
\n\n## Evaluation 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\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.
\n\n## Inference:\n\n**Acceleration Engine:** Pytorch
\n**Test Hardware:**\n\n- A100
\n- H100
\n- L40S
\n\n## Ethical Considerations:\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established\npolicies and practices to enable development for a wide array of AI applications.\nWhen downloaded or used in accordance with our terms of service, developers should\nwork with their internal model team to ensure this model meets requirements for the\nrelevant industry and use case and addresses unforeseen product misuse.\n\nFor more detailed information on ethical considerations for this model, please see the\nModel Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.\n\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","Earth-2 FourCastNet 3",["D",1753808924738],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/earth-2/models/fourcastnet3","rawImageUrl","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: | Not Applicable (N/A)\nBias Metric (If Measured) : | N/A\nWhich characteristic (feature) show(s) the greatest difference in performance?: | N/A","explainability","Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Task/Domain: | Weather Forecasting and Simulation\nModel Type: | Spherical Neural Operator\nIntended Users: | Weather Forecasters, Researchers, Related Industrial users.\nOutput: | Tensor representing 72 atmospheric variables\nDescribe how the model works: | Input variables are fed through a spherical convolutional architecture to produce the weather state in the next 6 hours.\nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable (N/A)\nTechnical Limitations & Mitigation: | 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: | Accuracy (ACC), Error (RMSE), and Probabilistic Calibration (CRPS)\nPotential Known Risks: | This model may incorrectly predict future weather states and phenomenon\nLicensing: | [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0).","privacy","Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------\nGeneratable or reverse engineerable personal data? | No\nPersonal data used to create this model? | No\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\nIs data compliant with data subject requests for data correction or removal, if such a request was made? | No, not possible with externally-sourced data.\nApplicable Privacy Policy: | https://www.nvidia.com/en-us/about-nvidia/privacy-policy/","safetyAndSecurity","Field | Response\n:---------------------------------------------------|:----------------------------------\nModel Application Field(s): | Climate\nDescribe the life critical impact (if present). | FourCastNet 3 achieves state of the art forecasting accuracy and can be used to forecast, among other events, extreme weather. Like any weather model, it is important to understand the accuracy and limitations - please see [FourCastNet 3 paper](https://arxiv.org/abs/2507.12144v1) to learn more.\nUse Case Restrictions: | Abide by [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0).\nModel and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. 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