针对台风强风突变，提出分频建模的预测框架，提升短期预报精度。
Frequency-aware forecasting for short-term typhoon gust prediction
- 分频处理趋势与波动，用小波分解+双分支网络分别建模
- 前6小时预报误差低于欧洲中期天气预报中心（ECMWF-HRES）
- 台风极端风速峰值捕捉更准，适合海上风电与防灾预警
在台风条件下，准确预测阵风仍具挑战性，因极端风速变化具有高度非平稳性和多尺度特征。现有深度学习模型常难以同时捕捉长期趋势与快速局部波动，导致极端事件下性能下降。本文提出WDANet，一种融合平稳小波分解、特征逐元素线性调制（FiLM）策略与双分支编码器-解码器架构的频率感知预测框架，实现对趋势与波动成分的分离建模。以中国西太平洋近海区域为例，开展高分辨率风速阵风预测研究。结果表明，在24小时预报范围内，WDANet在短时预报中表现更优，前6小时内预测精度优于ECMWF-HRES。在极端风事件中，该模型更准确捕捉阵风峰值，达到最低的均方根误差（RMSE）和平均绝对误差（MAE）。这些结果凸显其在近海风电运营、灾害预警与风险防控中的应用潜力。
原文摘要 · Abstract (English)
Accurate gust forecasting under typhoon conditions remains challenging due to the highly non-stationary and multi-scale characteristics of extreme wind fluctuations. Existing deep learning models often struggle to simultaneously capture long-term trends and rapid local variations, resulting in degraded performance during extreme events. We propose WDANet, a frequency-aware forecasting framework that integrates stationary wavelet decomposition, a Feature-wise Linear Modulation (FiLM) strategy, and a dual-branch encoder-decoder architecture, enabling separate modeling of trend and fluctuation components. Taking the offshore regions of the Western Pacific in China as an example, we conduct fine-grid wind gust prediction research. The results demonstrate that WDANet shows advantages for short lead times under the experimental setting across a 24-h forecasting horizon and achieves higher prediction accuracy than ECMWF-HRES within the first 6 h. During extreme wind events, WDANet more accurately captures gust peaks and attains the best RMSE and MAE performance. These results highlight its potential for offshore wind power operation, disaster warning, and risk mitigation.
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