面向降雨预报后处理的不确定性感知分割
Uncertainty-aware segmentation for rainfall prediction post processing
AI总结：
本文针对数值天气预报降水的偏差与不确定性，提出SDE U-Net分割模型进行后处理，在典型和强降水事件中实现了准确性与可靠性的最佳权衡，优于基线NWP方案。
摘要：
准确的降水预报对于洪水管理、农业规划、水资源分配和天气预警等应用至关重要。尽管数值天气预报（NWP）模型取得了进展，但它们仍表现出显著的偏差和不确定性，尤其是在高空间和时间分辨率下。为了解决这些局限性，我们探索了不确定性感知的深度学习模型，用于对每日累积定量降水预报进行后处理，以获得预报不确定性，从而在准确性与可靠性之间实现更好的权衡。我们的研究比较了不同的最先进模型，并提出了一种著名的SDE-Net的变体，称为SDE U-Net，专门针对像我们这样的分割问题而定制。我们评估了其在典型降水和强降水事件中的性能。我们的结果表明，所有深度学习模型都显著优于平均基线NWP解决方案，其中我们实现的SDE U-Net在准确性与可靠性之间表现出最佳权衡。将这些考虑不确定性的模型集成到业务预报系统中，可以改善与天气相关事件的决策和准备。
英文摘要：
Accurate precipitation forecasts are crucial for applications such as flood management, agricultural planning, water resource allocation, and weather warnings. Despite advances in numerical weather prediction (NWP) models, they still exhibit significant biases and uncertainties, especially at high spatial and temporal resolutions. To address these limitations, we explore uncertainty-aware deep learning models for post-processing daily cumulative quantitative precipitation forecasts to obtain forecast uncertainties that lead to a better trade-off between accuracy and reliability. Our study compares different state-of-the-art models, and we propose a variant of the well-known SDE-Net, called SDE U-Net, tailored to segmentation problems like ours. We evaluate its performance for both typical and intense precipitation events. Our results show that all deep learning models significantly outperform the average baseline NWP solution, with our implementation of the SDE U-Net showing the best trade-off between accuracy and reliability. Integrating these models, which account for uncertainty, into operational forecasting systems can improve decision-making and preparedness for weather-related events.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
