File size: 5,491 Bytes
5945b75 c67a639 5945b75 d333cfd 5945b75 c67a639 5945b75 c67a639 5945b75 c67a639 5945b75 c67a639 5945b75 c67a639 5945b75 c67a639 5945b75 c67a639 5945b75 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 | # 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/).
|