fix README

README.md

@@ -1,3 +1,56 @@
 ---
 license: mit
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+
+# Global Data-driven High-resolution Weather Model
+
+This is a global data-driven high-resolution weather model implemented and open sourced by [High-Flyer AI](https://www.high-flyer.cn/). It is the first AI weather model, which can compare with the ECMWF Integrated Forecasting System (IFS).
+
+Typhoon track comparison:
+
+![](./img/wind_small.gif)
+
+Water vapour comparison:
+
+![](./img/precipitation_small.gif)
+
+## Requirements
+
+- [hfai](https://doc.hfai.high-flyer.cn/index.html)
+- torch >=1.8
+
+
+## Training
+The raw data is from the public dataset, [ERA5](https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5) , which is integrated into the dataset warehouse, `hfai.datasets`.
+
+submit these tasks to Yinghuo HPC:
+
+1. pretrain `backbone.pt`
+
+   ```shell
+    hfai python train/pretrain.py -- -n 8 -p 30
+   ```
+
+2. finetune `backbone.pt`
+
+   ```shell
+    hfai python train/fine_tune.py -- -n 8 -p 30
+   ```
+
+3. train `precipitation.pt`
+
+   ```shell
+    hfai python train/precipitation.py -- -n 8 -p 30
+   ```
+
+
+## Citation
+
+```bibtex
+@article{pathak2022fourcastnet,
+  title={Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators},
+  author={Pathak, Jaideep and Subramanian, Shashank and Harrington, Peter and Raja, Sanjeev and Chattopadhyay, Ashesh and Mardani, Morteza and Kurth, Thorsten and Hall, David and Li, Zongyi and Azizzadenesheli, Kamyar and others},
+  journal={arXiv preprint arXiv:2202.11214},
+  year={2022}
+}
+```