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+# Earth-2 Checkpoints: FourCastNet 3
+
+## Description:
+
+FourCastNet 3 advances global weather modeling by implementing a scalable, geometric
+machine learning (ML) approach to probabilistic ensemble forecasting. The approach is
+designed to respect spherical geometry and to accurately model the spatially
+correlated probabilistic nature of the problem, resulting in stable spectra and
+realistic dynamics across multiple scales. FourCastNet 3 delivers forecasting accuracy
+that surpasses leading conventional ensemble models and rivals the best diffusion-based
+methods, while producing forecasts 8 to 60 times faster than these approaches. In
+contrast to other ML approaches, FourCastNet 3 demonstrates excellent probabilistic
+calibration and retains realistic spectra, even at extended lead times of up to 60 days.
+All of these advances are realized using a purely convolutional neural network
+architecture specifically tailored for spherical geometry. Scalable and efficient
+large-scale training on 1024 GPUs and more is enabled by a novel training paradigm for
+combined model- and data-parallelism, inspired by domain decomposition methods in
+classical numerical models. Additionally, FourCastNet 3 enables rapid inference on a
+single GPU, producing a 60-day global forecast at 0.25°, 6-hourly resolution in under
+4 minutes. Its computational efficiency, medium-range probabilistic skill, spectral
+fidelity, and rollout stability at subseasonal timescales make it a strong candidate
+for improving meteorological forecasting and early warning systems through large
+ensemble predictions.
+
+![FCN3 15 member ensemble](https://raw.githubusercontent.com/NVIDIA/makani/main/images/fcn3_ens15.gif)
+
+This model is ready for commercial/non-commercial use.
+
+### License/Terms of Use:
+
+[Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0)
+
+### Deployment Geography:
+
+Global
+
+### Use Case:
+
+Industry, academic, and government research teams interested in medium-range and
+subseasonal-to-seasonal weather forecasting, and climate modeling.
+
+### Release Date:
+
+NGC 07/18/2025
+
+## Reference:
+
+**Papers**:
+
+- [FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale](https://arxiv.org/abs/2507.12144v2)
+- [Neural Operators with Localized Integral and Differential Kernels](https://arxiv.org/abs/2402.16845)
+- [Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operators](https://arxiv.org/abs/2408.03100)
+- [Huge Ensembles Part II: Properties of a Huge Ensemble of Hindcasts Generated with Spherical Fourier Neural Operators](https://arxiv.org/abs/2408.01581)
+- [Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere](https://arxiv.org/abs/2306.03838)
+
+**Code**:
+
+- [Makani](https://github.com/NVIDIA/makani)
+
+- [PhysicsNeMo](https://github.com/NVIDIA/physicsnemo)
+
+- [Earth2Studio](https://github.com/NVIDIA/earth2studio)
+
+- [torch-harmonics](https://github.com/NVIDIA/torch-harmonics)
+
+## Model Architecture:
+
+**Architecture Type:** Spherical Neural Operator. A fully convolutional architecture
+based on group convolutions defined on the sphere. Leverages both local and global
+convolutions. For details regarding the architecture refer to the
+[FourCastNet 3 paper](https://arxiv.org/abs/2507.12144v1). <br>
+
+**Network Architecture:** N/A <br>
+
+**Number of model parameters:** 710,867,670
+
+**Model datatype:** We recommend that the model is run in AMP with bf16, however, the
+inputs and outputs are typically float32.
+
+## Input:
+
+**Input Type:**
+
+- Tensor (72 surface and pressure-level variables)
+
+**Input Format:** PyTorch Tensor  <br>
+**Input Parameters:**
+
+- Six Dimensional (6D) (batch, time, lead time, variable, latitude, longitude) <br>
+
+**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`,
+`tcwv`, `u50`, `u100`, `u150`, `u200`, `u250`, `u300`, `u400`, `u500`, `u600`, `u700`,
+`u850`, `u925`, `u1000`, `v50`, `v100`, `v150`, `v200`, `v250`, `v300`, `v400`, `v500`,
+`v600`, `v700`, `v850`, `v925`, `v1000`, `z50`, `z100`, `z150`, `z200`, `z250`, `z300`,
+`z400`, `z500`, `z600`, `z700`, `z850`, `z925`, `z1000`, `t50`, `t100`, `t150`, `t200`,
+`t250`, `t300`, `t400`, `t500`, `t600`, `t700`, `t850`, `t925`, `t1000`, `q50`, `q100`,
+`q150`, `q200`, `q250`, `q300`, `q400`, `q500`, `q600`, `q700`, `q850`, `q925`, `q1000`
+- Time: datetime64
+
+For variable name information, review the Lexicon at [Earth2Studio](https://github.com/NVIDIA/earth2studio).
+
+## Output:
+
+**Output Type:** Tensor (72 surface and pressure-level variables) <br>
+**Output Format:** Pytorch Tensor <br>
+**Output Parameters:** Six Dimensional (6D) (batch, time, lead time, variable,
+ latitude, longitude)  <br>
+**Other 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.
+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
+
+**Runtime Engine:** Pytorch <br>
+**Supported Hardware Microarchitecture Compatibility:** <br>
+
+- NVIDIA Ampere <br>
+- NVIDIA Hopper <br>
+- NVIDIA Turing <br>
+
+**Supported Operating System:**
+
+- Linux <br>
+
+## Model Version:
+
+**Model Version:** v1 <br>
+
+## Training, Testing, and Evaluation Datasets:
+
+**Total size (in number of data points):** 110,960 <br>
+**Total number of datasets:** 1<br>
+**Dataset partition:** training 95%, testing 2.5%, validation 2.5% <br>
+
+## Training Dataset:
+
+**Link:** [ERA5](https://cds.climate.copernicus.eu/) <br>
+
+**Data Collection Method by dataset** <br>
+
+- Automatic/Sensors <br>
+
+**Labeling Method by dataset** <br>
+
+- Automatic/Sensors <br>
+
+**Properties:**
+ERA5 data for the period 1980-2015. ERA5 provides hourly estimates of various
+atmospheric, land, and oceanic climate variables. The data covers the Earth on a 30km
+grid and resolves the atmosphere at 137 levels. <br>
+
+## Testing Dataset:
+
+**Link:** [ERA5](https://cds.climate.copernicus.eu/) <br>
+
+**Data Collection Method by dataset** <br>
+
+- Automatic/Sensors <br>
+
+**Labeling Method by dataset** <br>
+
+- Automatic/Sensors <br>
+
+**Properties:**
+ERA5 data for the period 2016-2017. ERA5 provides hourly estimates of various
+atmospheric, land, and oceanic climate variables. The data covers the Earth on a 30km
+grid and resolves the atmosphere at 137 levels. <br>
+
+## Evaluation Dataset:
+
+**Link:** [ERA5](https://cds.climate.copernicus.eu/) <br>
+
+**Data Collection Method by dataset** <br>
+
+- Automatic/Sensors <br>
+
+**Labeling Method by dataset** <br>
+
+- Automatic/Sensors <br>
+
+**Properties:**
+ERA5 data for the period 2018-2019. ERA5 provides hourly estimates of various
+atmospheric, land, and oceanic climate variables. The data covers the Earth on a 30km
+grid and resolves the atmosphere at 137 levels. <br>
+
+## Inference:
+
+**Acceleration Engine:** Pytorch <br>
+**Test Hardware:**
+
+- A100 <br>
+- H100 <br>
+- L40S <br>
+
+## 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.
+
+Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).