标题： 回声顶高度是否有助于深度学习的短时预报？ 显示英文标题

标题： Do Echo Top Heights Improve Deep Learning Nowcasts?

摘要： 短时降水预报——利用最近的雷达观测进行短期降雨预测——对交通、农业和灾害缓解等对天气敏感的行业至关重要。 尽管最近的深度学习模型在提高预报技能方面显示出潜力，但大多数方法仅依赖于二维雷达反射率场，忽略了完整三维雷达体积中可用的有价值垂直信息。 在本研究中，我们探讨了回波顶高（ETH），一种表示给定阈值以上雷达反射率最大高度的二维投影，作为深度学习基预报的辅助输入变量。 我们研究了ETH与雷达反射率之间的关系，确认其对预测降雨强度的相关性。 我们实现了一个单次通过的三维U-Net，将雷达反射率和ETH作为独立的输入通道进行处理。 虽然我们的模型能够利用ETH在低降雨率阈值下提高技能，但在较高强度下的结果不一致，且包含ETH的模型系统性地低估了降水强度。 三个案例研究用于说明ETH在某些情况下如何有所帮助，但也可能使模型混淆并增加误差方差。 尽管如此，这项研究为批判性评估其他变量对预报性能的潜在贡献奠定了基础。 显示英文摘要

摘要： Precipitation nowcasting -- the short-term prediction of rainfall using recent radar observations -- is critical for weather-sensitive sectors such as transportation, agriculture, and disaster mitigation. While recent deep learning models have shown promise in improving nowcasting skill, most approaches rely solely on 2D radar reflectivity fields, discarding valuable vertical information available in the full 3D radar volume. In this work, we explore the use of Echo Top Height (ETH), a 2D projection indicating the maximum altitude of radar reflectivity above a given threshold, as an auxiliary input variable for deep learning-based nowcasting. We examine the relationship between ETH and radar reflectivity, confirming its relevance for predicting rainfall intensity. We implement a single-pass 3D U-Net that processes both the radar reflectivity and ETH as separate input channels. While our models are able to leverage ETH to improve skill at low rain-rate thresholds, results are inconsistent at higher intensities and the models with ETH systematically underestimate precipitation intensity. Three case studies are used to illustrate how ETH can help in some cases, but also confuse the models and increase the error variance. Nonetheless, the study serves as a foundation for critically assessing the potential contribution of additional variables to nowcasting performance.