改进损失函数，让数据驱动天气预报更清晰、更精准。
Fixing the Double Penalty in Data-Driven Weather Forecasting Through a Modified Spherical Harmonic Loss Function
- 用新损失函数分离相位与振幅误差，避免细节模糊
- 模型有效分辨率从1250公里提升至160公里
- 适合需要高精度气象预测的研究与业务场景
数据驱动的天气预报模型虽已超越传统物理模型，但普遍采用均方误差损失函数，导致细粒度结构被平滑，产生“双重惩罚”效应。本文提出一种无需参数的损失函数改进方法，通过分离相位失配与谱振幅误差的贡献，有效缓解该问题。在GraphCast模型上微调后，预测结果显著更锐利，有效分辨率由1250公里提升至160公里，同时改善了集合预报的散布性，并提升了对热带气旋强度和地表风速极端值的预测能力。
原文摘要 · Abstract (English)
Recent advancements in data-driven weather forecasting models have delivered deterministic models that outperform the leading operational forecast systems based on traditional, physics-based models. However, these data-driven models are typically trained with a mean squared error loss function, which causes smoothing of fine scales through a "double penalty" effect. We develop a simple, parameter-free modification to this loss function that avoids this problem by separating the loss attributable to decorrelation from the loss attributable to spectral amplitude errors. Fine-tuning the GraphCast model with this new loss function results in sharp deterministic weather forecasts, an increase of the model's effective resolution from 1,250km to 160km, improvements to ensemble spread, and improvements to predictions of tropical cyclone strength and surface wind extremes.
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