Merge branch 'main' of https://huggingface.co/Onemiss/PW-FouCast

README.md

@@ -2,5 +2,55 @@
 license: apache-2.0
 arxiv: 2603.21768
 tags:
-- pretrain
+- Pretrained Weights
 ---
+# 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.
+
+
+## 🌟 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.
+* **Frequency Memory (FM):** A learned repository of ground-truth spectral patterns that dynamically corrects phase discrepancies and preserves complex temporal evolutions (e.g., expansion/contraction).
+* **Inverted Frequency Attention (IFA):** A residual-reinjection mechanism designed to recover high-frequency details typically lost during spectral filtering, maintaining sharp structural fidelity in long-term predictions.
+* **Extended Horizon:** Demonstrates superior performance on **SEVIR** and **MeteoNet** benchmarks, significantly mitigating performance decay in long-lead nowcasting.
+
+## 🚀 How to Use
+
+You can load the model weights for inference or fine-tuning as follows:
+
+```python
+import torch
+from pw_foucast import PWFouCast
+from safetensors.torch import load_model
+from huggingface_hub import hf_hub_download
+
+MODEL_REGISTRY = {
+    'pw_foucast': PW_FouCast,
+}
+ModelClass = MODEL_REGISTRY.get(args.model.lower())
+
+model = ModelClass(**model_kwargs).to(args.device)
+model = torch.nn.DataParallel(model)
+
+# Load the model from Hugging Face
+weights_path = hf_hub_download(repo_id=f"Onemiss/PW-FouCast", filename=f"{args.model}/{args.dataset}/model.safetensors")
+load_model(model, weights_path)
+
+# Eval
+model.eval()
+……
+
+```