PW-FouCast: Pangu-Weather-guided Fourier-domain foreCast

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:

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()
……