用分位数回归提升降水预报精度，更好预测强降雨。
Beyond MSE: Improving Precipitation Nowcasting with Multi-Quantile Regression
- 将模型训练改为多分位数回归，替代传统均方误差损失。
- 测试集均方误差降低8.6%，且能生成高分位数预报结果。
- 无需新架构或采样，适合需要风险预警的气象应用。
深度学习降水短时预报模型通常使用点对点损失函数（如均方误差或平均绝对误差），导致预报结果过于平滑，难以准确反映强降雨。本研究探讨将现有确定性预报架构的训练方式改为基础多分位数回归，以提升性能。以SmaAt-UNet为核心模型，在荷兰雷达降水预报任务上对比了均方误差（MSE）、平均绝对误差（MAE）和多分位数皮纳布尔损失（pinball-loss）的训练效果。结果表明，采用多分位数训练的模型在中心确定性预报上表现更优，测试集均方误差较MSE训练模型降低8.6%，同时还能生成可用于重雨风险预警的上分位数输出。这说明分位数回归是一种无需更换架构或生成采样的简单有效替代方案。代码与训练配置已公开于GitHub。
原文摘要 · Abstract (English)
Deep-learning precipitation nowcasting models are often optimized using pointwise losses such as mean squared error or mean absolute error, which can lead to overly smooth forecasts and poor representation of heavy rainfall. This study investigates whether the predictive performance of an established deterministic nowcasting architecture can be improved by reformulating training as a multi-quantile regression problem. Using SmaAt-UNet as a core model, we compare MSE, MAE, and multi-quantile pinball-loss training on radar precipitation nowcasting over the Netherlands. The results show that multi-quantile training improves the central deterministic forecast, decreasing test-set MSE by 8.6\% compared to a model trained using MSE, while also producing upper-quantile outputs that are useful for risk-sensitive prediction of heavy precipitation. These findings suggest that quantile regression provides a simple alternative to standard pointwise losses without requiring a new architecture or generative sampling procedure. The implementation of our models and training setup is available on \href{https://github.com/gijsvn/Multi-Quantile-Precipitation-Nowcasting}{GitHub}.
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