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.

翻译：近年来，数据驱动的天气预报模型取得了显著进展，其确定性模型的表现已超越基于传统物理模型的主流业务预报系统。然而，这些数据驱动模型通常采用均方误差损失函数进行训练，这会通过"双重惩罚"效应导致精细尺度的平滑化。我们对该损失函数提出了一种简单、无参数的改进方案，通过将去相关引起的损失与频谱幅度误差引起的损失分离开来，从而避免了这一问题。使用这一新损失函数对GraphCast模型进行微调后，可获得清晰的确定性天气预报结果，模型的有效分辨率从1,250公里提升至160公里，同时改善了集合离散度，并提升了对热带气旋强度和地表极端风场的预测能力。