| # Earth-2 Checkpoints: FourCastNet |
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| FourCastNet, short for Fourier Forecasting Neural Network, is a global data-driven weather forecasting model that provides accurate short to medium-range global predictions. |
| FourCastNet accurately forecasts high-resolution, fast-timescale variables such as the surface wind speed, precipitation, and atmospheric water vapor. |
| FourCastNet uses the the Adaptive Fourier Neural Operator (AFNO) archiecture. |
| This particular neural network architecture is appealing as it is specifically designed for high-resolution inputs and synthesizes several key recent advances in DL into one model. |
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| Release Date: October 25, 2023 |
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| This model is ready for commercial/non-commercial use. |
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| ### License/Terms of Use: |
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| [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0) |
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| ### Deployment Geography: |
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| Global |
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| ### Use Case: |
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| Industry, academic, and government research teams interested in medium-range and |
| subseasonal-to-seasonal weather forecasting, and climate modeling. |
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| ### References: |
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| **Papers**: |
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| - [FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators](https://arxiv.org/abs/2202.11214) |
| - [Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers](https://arxiv.org/abs/2111.13587) |
| - [The ERA5 global reanalysis](https://doi.org/10.1002/qj.3803) |
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| **Code**: |
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| - [PhysicsNeMo](https://github.com/NVIDIA/physicsnemo) |
| - [Earth2Studio](https://github.com/NVIDIA/earth2studio) |
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| ## Model Architecture: |
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| **Architecture Type:** Neural Operator <br> |
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| **Network Architecture:** Adaptive Fourier Neural Operator <br> |
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| ## Input: |
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| **Input Type:** |
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| - Tensor (26 surface and pressure-level variables) |
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| **Input Format:** PyTorch Tensor <br> |
| **Input Parameters:** |
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| - Six Dimensional (6D) (batch, time, lead time, variable, latitude, longitude) <br> |
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| **Other Properties Related to Input:** |
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| - Input equi-rectangular latitude/longitude grid: 0.25 degree 720 x 1440 (south-pole excluding) |
| - Input state weather variables: `u10m`, `v10m`, `t2m`, `sp`, `msl`, `t850`, `u1000`, `v1000`, `z1000`, `u850`, `v850`, `z850`, `u500`, `v500`, `z500`, `t500`, `z50`, `r500`,`r850`,`tcwv`,`u100m`,`v100m`,`u250`,`v250`,`z250`,`t250` |
| - Time: datetime64 |
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| For variable name information, review the Lexicon at [Earth2Studio](https://github.com/NVIDIA/earth2studio). |
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| ## Output: |
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| **Output Type:** Tensor (26 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:** |
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| - Output latitude/longitude grid: 0.25 degree 72 x 1440, same as input. |
| - Output state weather variables: same as above. |
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| 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. |
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| ## Software Integration: |
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| **Runtime Engine:** Pytorch <br> |
| **Supported Hardware Microarchitecture Compatibility:** <br> |
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| - NVIDIA Ampere <br> |
| - NVIDIA Hopper <br> |
| - NVIDIA Turing <br> |
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| **Supported Operating System:** |
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| - Linux <br> |
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| ## Model Version: |
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| **Model Version:** v1 <br> |
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| ## Training Dataset: |
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| **Link:** [ERA5](https://cds.climate.copernicus.eu/) <br> |
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| **Data Collection Method by dataset** <br> |
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| - Automatic/Sensors <br> |
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| **Labeling Method by dataset** <br> |
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| - Automatic/Sensors <br> |
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| **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> |
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| ## Testing Dataset: |
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| **Link:** [ERA5](https://cds.climate.copernicus.eu/) <br> |
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| **Data Collection Method by dataset** <br> |
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| - Automatic/Sensors <br> |
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| **Labeling Method by dataset** <br> |
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| - Automatic/Sensors <br> |
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| **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> |
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| ## Evaluation Dataset: |
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| **Link:** [ERA5](https://cds.climate.copernicus.eu/) <br> |
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| **Data Collection Method by dataset** <br> |
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| - Automatic/Sensors <br> |
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| **Labeling Method by dataset** <br> |
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| - Automatic/Sensors <br> |
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| **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> |
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| ## Inference: |
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| **Acceleration Engine:** PhysicsNeMo, PyTorch <br> |
| **Test Hardware:** |
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| - A100 <br> |
| - H100 <br> |
| - L40S <br> |
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| ## Ethical Considerations: |
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| 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. |
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| For more detailed information on ethical considerations for this model, please see the |
| Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. |
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| Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/). |
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