提出新型暴雨预报损失函数，提升干旱期降水预测准确性。
Advanced Torrential Loss Function for Precipitation Forecasting
- 将惩罚项重构成无约束二元优化问题，再松弛为可微分损失函数。
- 在操作模型上验证，显著优于传统指标优化的预报效果。
- 特别适合极端干旱或暴雨场景，对气象预报员有实用价值。
准确的降水预报在气候变化背景下愈发重要。近年来，机器学习方法作为数值天气预报和气候模型的新兴替代方案受到关注。然而，多数方法仍依赖通用损失函数，即使较先进的方法也仅基于临界成功指数（CSI）进行优化。问题在于，当降水持续低于阈值时，CSI在长期干期会失效，难以有效指导优化。为此，本文引入一个简单的惩罚表达式，并将其重新解释为无约束二元优化（QUBO）形式。通过近似过程，该QUBO被松弛为可微分的先进暴雨（AT）损失函数。所提AT损失函数在利普希茨常数、预报性能评估、一致性实验及消融研究中均表现出优越性，尤其在操作模型上验证了其有效性。
原文摘要 · Abstract (English)
Accurate precipitation forecasting is becoming increasingly important in the context of climate change. In response, machine learning-based approaches have recently gained attention as an emerging alternative to traditional methods such as numerical weather prediction and climate models. Nonetheless, many recent approaches still rely on off-the-shelf loss functions, and even the more advanced ones merely involve optimization processes based on the critical success index (CSI). The problem, however, is that CSI may become ineffective during extended dry periods when precipitation remains below the threshold, rendering it less than ideal as a criterion for optimization. To address this limitation, we introduce a simple penalty expression and reinterpret it as a quadratic unconstrained binary optimization (QUBO) formulation. Ultimately, the resulting QUBO formulation is relaxed into a differentiable advanced torrential (AT) loss function through an approximation process. The proposed AT loss demonstrates its superiority through the Lipschitz constant, forecast performance evaluations, consistency experiments, and ablation studies with the operational model.
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