AI 中文总结
该研究利用时空神经网络，通过敏感性实验探究输入数据对冰雹临近预报模型技巧的影响，发现增加训练数据年份可提升预报技巧，数据增强效果与数据集规模相关，为深度学习临近预报发展提供了数据层面的指导。
AI 中文摘要
冰雹会造成巨大的经济损失并对公共安全构成威胁，因此可靠的临近预报对于及时发布预警至关重要。深度学习方法已成为传统方法的有力替代方案，但输入数据选择如何影响性能尚未得到深入探究。我们研究了在不改变深度学习冰雹临近预报模型架构的情况下，如何提升其预报技巧。敏感性实验评估了训练数据量、随机数据增强（镜像和旋转）以及输入时间步长数量的影响。增加训练数据的年份数可大幅提升预报技巧，在较长预报提前期内最多可提升25分钟。数据增强对于较大数据集可提升性能，但有趣的是会降低较小数据集的性能。输入时间步长的敏感性弱于训练年份的敏感性。这些发现表明，即使在固定架构下，数据选择和预处理的改进也能带来显著提升，为未来深度学习临近预报的发展提供了指导。
英文摘要
Hail can cause large financial losses and poses risks to public safety, making reliable nowcasts essential for timely warnings. Deep-learning approaches have emerged as a strong alternative to conventional methods, but how input data choices affect performance has not been deeply explored. We investigate how the skill of a deep-learning hail nowcasting model can be improved without changing the model architecture. Sensitivity experiments assess the impact of training-data volume, random data augmentation (mirroring and rotation) and the number of input timesteps. Increasing the years of training data substantially improves forecast skill, by up to 25 minutes at later lead times. Augmentation improved performance for larger datasets but interestingly degraded performance for smaller ones. Sensitivity to input timesteps was weaker than sensitivity to training years. These findings show that improvements in data selection and preprocessing can yield substantial gains even with a fixed architecture, offering guidance for future deep-learning nowcasting development.
CommentsUnder review for publication in Geophysical Research Letters
