Init commit

README.md

@@ -0,0 +1,169 @@
+# 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.
+
+### Reference(s)
+
+**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/).

fcn.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a5e1ec27a8a6ec046ecccb7497ccdbc97045c83fe41f1f62aebf6069f2ff2956
+size 279597868