Improve model card metadata and pipeline tag

Hi! This PR improves the model card for **PW-FouCast** by:
- Adding the `time-series-forecasting` pipeline tag to the YAML metadata.
- Moving the `arxiv` ID from the YAML section to the Markdown section as requested by Hugging Face documentation standards.
- Maintaining the existing code usage and model overview while ensuring formatting consistency.

README.md

@@ -1,25 +1,26 @@
 ---
 license: apache-2.0
-arxiv: 2603.21768
+pipeline_tag: time-series-forecasting
 tags:
-- Pretrained Weights
+- weather
+- precipitation-nowcasting
+- climate
 ---
+
 # PW-FouCast: Pangu-Weather-guided Fourier-domain foreCast
 
-[//]: # (Add badges here if desired, e.g., for License or Paper)
 [![Paper](https://img.shields.io/badge/arXiv-2603.21768-B31B1B.svg)](https://arxiv.org/abs/2603.21768)
 [![GitHub](https://img.shields.io/badge/GitHub-Repository-181717?logo=github)](https://github.com/Onemissed/PW-FouCast)
 [![Conference](https://img.shields.io/badge/IJCNN-2026-blue.svg)](https://attend.ieee.org/wcci-2026/)
 
-This is the official Hugging Face repository for **PW-FouCast**, a novel frequency-domain fusion framework designed to extend precipitation nowcasting horizons by integrating weather foundation model priors with radar observations.
+This is the official Hugging Face repository for **PW-FouCast**, a novel frequency-domain fusion framework designed to extend precipitation nowcasting horizons by integrating weather foundation model priors with radar observations. 
 
+The model was introduced in the paper [Extending Precipitation Nowcasting Horizons via Spectral Fusion of Radar Observations and Foundation Model Priors](https://huggingface.co/papers/2603.21768).
 
 ## 🌟 Model Overview
 
 **PW-FouCast** addresses the challenge of representational heterogeneities between high-resolution radar imagery and large-scale meteorological data. By leveraging Pangu-Weather forecasts as spectral priors within a Fourier-based backbone, the model effectively bridges the gap between atmospheric dynamics and local convective patterns.
 
-
-
 ### Key Features
 
 * **Pangu-Weather-guided Frequency Modulation (PFM):** Aligning spectral magnitudes and phases with physical meteorological priors to ensure physically consistent forecasts.
@@ -38,7 +39,7 @@ from safetensors.torch import load_model
 from huggingface_hub import hf_hub_download
 
 MODEL_REGISTRY = {
-    'pw_foucast': PW_FouCast,
+    'pw_foucast': PWFouCast,
 }
 ModelClass = MODEL_REGISTRY.get(args.model.lower())
 
@@ -52,5 +53,17 @@ load_model(model, weights_path)
 # Eval
 model.eval()
 ……
+```
 
+## ✍️ Citation
+
+If you find this work or code useful for your research, please consider citing:
+
+```bibtex
+@article{qin2026extending,
+  title={Extending Precipitation Nowcasting Horizons via Spectral Fusion of Radar Observations and Foundation Model Priors},
+  author={Yuze Qin, Qingyong Li, Zhiqing Guo, Wen Wang, Yan Liu, Yangli-ao Geng},
+  journal={arXiv preprint arXiv:2603.21768},
+  year={2026}
+}
 ```