| # Earth-2 Checkpoints: FourCastNet | |
| 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. | |
| Release Date: October 25, 2023 | |
| 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. | |
| ### References: | |
| **Papers**: | |
| - [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) | |
| **Code**: | |
| - [PhysicsNeMo](https://github.com/NVIDIA/physicsnemo) | |
| - [Earth2Studio](https://github.com/NVIDIA/earth2studio) | |
| ## Model Architecture: | |
| **Architecture Type:** Neural Operator <br> | |
| **Network Architecture:** Adaptive Fourier Neural Operator <br> | |
| ## Input: | |
| **Input Type:** | |
| - Tensor (26 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 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 | |
| For variable name information, review the Lexicon at [Earth2Studio](https://github.com/NVIDIA/earth2studio). | |
| ## Output: | |
| **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:** | |
| - Output latitude/longitude grid: 0.25 degree 72 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 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/). | |