| --- |
| license: apache-2.0 |
| pipeline_tag: time-series-forecasting |
| tags: |
| - weather |
| - precipitation-nowcasting |
| - climate |
| --- |
| |
| # PW-FouCast: Pangu-Weather-guided Fourier-domain foreCast |
|
|
| [](https://arxiv.org/abs/2603.21768) |
| [](https://github.com/Onemissed/PW-FouCast) |
| [](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. |
|
|
| 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. |
| * **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 PW_FouCast |
| 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() |
| …… |
| ``` |
|
|
| ## ✍️ 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} |
| } |
| ``` |
