Update English model card

README.md

@@ -1,20 +1,18 @@
 ---
-frameworks: PyTorch
+license: apache-2.0
 language:
 - en
-license: apache-2.0
 tags:
 - OneScience
 - Earth Science
 - Weather Downscaling
 - Diffusion Model
-- ERA5
-- CorrDiff
-- arxiv:2309.15214
-tasks: []
+frameworks: PyTorch
 datasets:
-- OneScience-Group/ERA5
+- ERA5
+- CWA-WRF
 ---
+
 <p align="center">
   <strong>
     <span style="font-size: 30px;">CorrDiff</span>
@@ -23,50 +21,48 @@ datasets:
 
 # Model Introduction
 
-CorrDiff (Residual Corrective Diffusion Model) is a two-stage generative model for kilometer-scale regional weather downscaling. It downscales approximately 25 km ERA5 fields to approximately 2 km regional weather fields over Taiwan and synthesizes radar reflectivity that is not present in the input.
-
-Paper: Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling
+CorrDiff is a two-stage generative model for kilometer-scale atmospheric downscaling. It first predicts a high-resolution mean with conditional regression and then uses a residual diffusion model to generate local stochastic details and ensemble forecasts.
 
+Paper: Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling  
 https://arxiv.org/abs/2309.15214
 
 # Model Description
 
-CorrDiff was proposed by NVIDIA and its collaborators and was trained with coarse-resolution ERA5 reanalysis and high-resolution WRF regional model data from Taiwan's Central Weather Administration.
-The model is suitable for converting coarse-resolution global weather fields into high-resolution regional weather fields and producing probabilistic weather predictions.
+CorrDiff was proposed by an NVIDIA research team. The model is trained using ERA5 reanalysis data and high-resolution regional CWA-WRF simulation data, and performs weather-field downscaling through regression and residual diffusion. It is suitable for high-resolution weather-field generation, kilometer-scale downscaling, and ensemble uncertainty analysis.
 
 # Use Cases
 
 | Scenario | Description |
 | :---: | :--- |
-| Regional weather downscaling training | Train CorrDiff with time-aligned coarse-resolution ERA5 inputs and high-resolution CWA-WRF targets. |
-| Local quick validation | Use synthetic paired data to check data loading, two-stage training, ensemble inference, and result visualization. |
-| Hugging Face / OneCode execution | Download the standalone model package, install dependencies, and run the scripts directly. |
-| Multi-GPU training | Launch multi-process training with `torchrun` after adapting the real-data pipeline for distributed training. |
+| Weather downscaling | Downscale `36x36` coarse-resolution condition fields to `448x448`. |
+| Ensemble forecasting | Generate multiple possible high-resolution results through diffusion sampling. |
+| Radar reflectivity generation | Generate high-resolution maximum radar reflectivity that is absent from the ERA5 condition fields, supporting analysis of fine-scale precipitation-system structures. |
+| Extreme-weather risk analysis | Use ensemble members and spatial uncertainty to characterize the possible evolution of local heavy precipitation and other high-impact weather. |
+| Local engineering validation | Use a small amount of synthetic data to check training, inference, and evaluation workflows. |
+| Multi-GPU training | Launch distributed training with `torchrun`. |
 
 # Usage Guide
 
-## 1. OneCode Usage
+## 1.OneCode
 
 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**
-
-- A GPU or DCU is recommended.
-- CPU can be used for import and small-scale connectivity verification; full training and inference will be slow.
-- 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
+## 2.Download and Installation
 
 ```bash
 hf download OneScience-Group/CorrDiff --local-dir ./CorrDiff
 cd CorrDiff
 ```
 
-### Install the Runtime Environment
+### Environment Dependencies
+
+**Hardware Requirements**
+
+- A GPU or DCU is recommended.
+- CPU can be used for connectivity validation with a small configuration; full training and inference are slower.
+- DCU users must install DTK in advance. DTK 25.04.2 or above, or the OneScience-recommended version matching the current cluster, is recommended.
 
 **DCU Environment**
 
@@ -90,55 +86,45 @@ pip install onescience[earth-gpu] -i http://mirrors.onescience.ai:3141/pypi/simp
 
 ### Training Data Introduction
 
-This repository uses synthetic paired data by default to validate training, ensemble inference, and evaluation:
+By default, two synthetic samples following the same protocol are used to validate the engineering workflow. The data are saved as `data/corrdiff.npz`, and the synthetic data do not represent real weather distributions.
 
-```bash
-python scripts/fake_data.py --output data/era5_corrdiff.npz
-```
-
-The synthetic file contains `input` with shape `[N, 12, 36, 36]` and `target` with shape `[N, 4, 448, 448]`. It is intended for pipeline validation only and does not represent real weather predictions.
+The synthetic data retain the input and output specifications of the Taiwan downscaling experiment in the paper: 12-channel `36x36` condition fields and 4-channel `448x448` target fields.
 
-ERA5 inputs can be downloaded from the OneScience community:
+Real data must be preprocessed and converted to the following NPZ training protocol. This protocol is consistent with the model input specification but is not equivalent to the original datasets' download format.
 
-```bash
-hf download --repo-type dataset OneScience-Group/ERA5 --local-dir ./data/era5
+```text
+input: float32 [N,12,36,36]
+target: float32 [N,4,448,448]
 ```
 
-The paired CWA-WRF resource can be downloaded from NVIDIA NGC:
+`fake_data.py` automatically writes the `protocol` and `data_source` protocol metadata. These fields must be retained when using real data.
 
 ```bash
-ngc registry resource download-version "nvidia/modulus/modulus_datasets_cwa:v1"
+python scripts/fake_data.py
 ```
 
-ERA5 alone is insufficient for supervised CorrDiff training. Real data must be time-aligned and preprocessed into the paired `input` and `target` interface expected by the scripts. Verify the data path in `conf/config.yaml` before training.
-
 ### Training
 
-Single GPU:
-
 ```bash
 python scripts/train.py
 ```
 
-Multi-GPU:
+Multi-GPU training can use:
 
 ```bash
-torchrun --nproc_per_node=8 --nnodes=1 --rdzv_id=1000 --rdzv_backend=c10d --max_restarts=0 --master_addr="localhost" --master_port=29500 scripts/train.py
+torchrun --nproc_per_node=8 scripts/train.py
 ```
 
-Training outputs:
+Training sequentially optimizes the conditional-regression and residual-diffusion stages and saves a checkpoint and overall training metrics. The default configuration is intended for quick workflow validation. Formal experiments should use the data scale, model configuration, and training schedule corresponding to the paper.
 
 ```text
-data/checkpoints/regression_model.pth
-data/checkpoints/diffusion_model.pth
-data/checkpoints/training_history.npz
+result/checkpoints/corrdiff.pt
+result/training/metrics.json
 ```
 
-Training first fits the conditional-mean regression model and then freezes it while fitting the residual diffusion model. `training_history.npz` stores the regression and diffusion denoising losses.
-
 ### Training Weights
 
-The training command generates `regression_model.pth` and `diffusion_model.pth` from `data/era5_corrdiff.npz`. Weights produced with synthetic data validate the training and inference pipeline only and do not provide real weather forecasting skill. This repository does not present them as pretrained weights reproducing the paper.
+This repository will provide CorrDiff training weights in the `weight/` folder. The weight files will be uploaded soon and are expected to be available in the near future.
 
 ### Inference
 
@@ -146,29 +132,25 @@ The training command generates `regression_model.pth` and `diffusion_model.pth`
 python scripts/inference.py
 ```
 
-Prediction output:
+Inference loads the training checkpoint, generates high-resolution ensemble forecasts according to the configuration, and saves the results to:
 
 ```text
 result/output/predictions.npz
 ```
 
-The file stores the ensemble predictions, ensemble mean, ensemble standard deviation, input, and target generated by the current trained weights.
-
 ### Evaluation and Visualization
 
 ```bash
 python scripts/result.py
 ```
 
-Evaluation outputs:
+Evaluation reports overall MAE, RMSE, ensemble CRPS, and ensemble spread, and generates an ensemble diagnostic figure. Synthetic-data results are only for validating the engineering workflow and do not represent the full performance reported in the paper.
 
 ```text
-result/output/metrics.json
-result/output/prediction_comparison.png
+result/evaluation/metrics.json
+result/evaluation/ensemble_diagnostics.png
 ```
 
-`metrics.json` stores MAE and CRPS for the four output variables. `prediction_comparison.png` compares the ensemble mean, ensemble standard deviation, and target. Metrics and figures generated from synthetic data are pipeline checks, not paper results.
-
 # Official OneScience Resources
 
 | Platform | OneScience Main Repository | Skills Repository |
@@ -178,4 +160,6 @@ result/output/prediction_comparison.png
 
 # Citation and License
 
-- This repository is a reproduction of the original CorrDiff paper.
+This repository is a reproduction of the original CorrDiff paper.
+
+The use of the code and data in this repository remains subject to the licenses and terms of use of their respective projects.