| --- |
| datasets: |
| - OneScience/ERA5 |
| frameworks: |
| - PyTorch |
| language: |
| - en |
| - zh |
| license: apache-2.0 |
| tags: |
| - OneScience |
| - Earth Science |
| - Weather Forecast |
| - Regional Forecast |
| - Diffusion Model |
| - ERA5 |
| - HRRR |
| tasks: [] |
| --- |
| |
| <p align="center"> |
| <strong> |
| <span style="font-size: 30px;">StormCast</span> |
| </strong> |
| </p> |
| |
| # Model Introduction |
|
|
| StormCast is a generative regional weather forecasting model proposed by NVIDIA, targeting high-resolution nowcasting of mesoscale convective weather. |
|
|
| Paper: StormCast: A Machine Learning Method for Meso-β-Scale Convection-resolving Weather Forecasting |
|
|
| https://arxiv.org/abs/2408.10958 |
|
|
| # Model Description |
|
|
| StormCast constrains the evolution of regional states with large-scale weather backgrounds, and uses a generative diffusion approach to supplement the fine-scale structures that deterministic forecasts struggle to represent. |
|
|
| # Use Cases |
|
|
| | Scenario | Description | |
| | :---: | :--- | |
| | Two-Stage Weather Forecast Training | Train a deterministic regression model and a conditional residual diffusion model in sequence. | |
| | Local Quick Validation | Use synthetic data to verify data loading, model training, inference, and inference result visualization. | |
| | ModelScope / OneCode Execution | Download as a standalone model package, install dependencies, and run scripts directly. | |
| | Multi-GPU Training | Launch multi-process training via `torchrun`. | |
|
|
| # Usage Guide |
|
|
| ## 1. OneCode Usage |
|
|
| Experience intelligent one-click AI4S programming through the OneCode online environment: |
|
|
| [Click to Experience Intelligent One-Click AI4S Programming](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home) |
|
|
| ## 2. Manual Installation and Usage |
|
|
| **Hardware Requirements** |
|
|
| - Training and inference require a GPU or DCU recognized by PyTorch; CPU can be used to generate synthetic data and verify configuration, but cannot run the current training and inference scripts. |
| - Multi-GPU training uses the NCCL backend. Please make sure the device driver, communication libraries, and PyTorch version are compatible. |
| - DCU users must install DTK in advance. DTK 25.04.2 or above, or the OneScience recommended version matching your cluster, is recommended. |
|
|
| ### Download the Model Package |
|
|
| ```bash |
| hf download OneScience-Group/StormCast --local-dir ./StormCast |
| cd StormCast |
| ``` |
|
|
| ### Install the Runtime Environment |
|
|
| **DCU Environment** |
|
|
| ```bash |
| # Please activate DTK and CONDA first |
| conda create -n onescience311 python=3.11 -y |
| conda activate onescience311 |
| # uv installation is supported |
| pip install onescience[earth-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai |
| ``` |
|
|
| **GPU Environment** |
|
|
| ```bash |
| # Please activate CONDA first |
| conda create -n onescience311 python=3.11 -y libstdcxx-ng=12 libgcc-ng=12 gcc_linux-64=12 gxx_linux-64=12 |
| conda activate onescience311 |
| # uv installation is supported |
| pip install onescience[earth-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai |
| ``` |
|
|
| ### Training Data Introduction |
|
|
| The OneScience community provides ERA5 data for training (due to file size limits, the current repository contains a slice of the full dataset). Users can download it with the command below and confirm that the data path in `conf/config.yaml` is set correctly: |
|
|
| ```bash |
| hf download --repo-type dataset OneScience-Group/ERA5 --local-dir ./data |
| ``` |
|
|
| ### Generate Synthetic Data for Pipeline Validation |
|
|
| ```bash |
| python scripts/fake_data.py |
| ``` |
|
|
| Synthetic data is only used to verify the data protocol and program flow; it does not represent the model's scientific forecasting capability. |
|
|
| ### Training |
|
|
| Single GPU: |
|
|
| ```bash |
| # Train the deterministic regression model; weights are saved to data/checkpoint/regression/model_bak.pt by default |
| python scripts/train.py --stage regression |
| # Train the residual diffusion model; weights are saved to data/checkpoint/diffusion/model_bak.pt by default |
| python scripts/train.py --stage diffusion |
| ``` |
|
|
| ### Multi-GPU |
|
|
| ```bash |
| # Train the deterministic regression model |
| torchrun --nproc_per_node=2 scripts/train.py --stage regression |
| # Train the residual diffusion model |
| torchrun --nproc_per_node=2 scripts/train.py --stage diffusion |
| ``` |
|
|
| ### Training Weights |
|
|
| This repository provides weights trained on ERA5 reanalysis data in the `weights/` folder. The weight files will be uploaded soon and are expected to be available in the near future. |
|
|
| ### Inference |
|
|
| Run autoregressive prediction with the default configuration and the two sets of weights saved during training: |
|
|
| ```bash |
| python scripts/inference.py |
| ``` |
|
|
| ### Evaluation and Visualization |
|
|
| ```bash |
| python scripts/result.py |
| ``` |
|
|
| Plots are saved to `outputs/inference/plots/` by default. |
|
|
| # OneScience Official Information |
|
|
| | Platform | OneScience Main Repository | Skills Repository | |
| | --- | --- | --- | |
| | Gitee | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills | |
| | GitHub | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills | |
|
|
| # Citation & License |
|
|
| - This repository is a reproduction of the original StormCast paper. |
|
|
