Designing early warning systems for harsh weather and its effects, such as urban flooding or landslides, requires accurate short-term forecasts (nowcasts) of precipitation. Nowcasting is a significant task with several environmental applications, such as agricultural management or increasing flight safety. In this study, we investigate the use of a UNet core-model and its extension for precipitation nowcasting in western Europe for up to 3 hours ahead. In particular, we propose the Weather Fusion UNet (WF-UNet) model, which utilizes the Core 3D-UNet model and integrates precipitation and wind speed variables as input in the learning process and analyze its influences on the precipitation target task. We have collected six years of precipitation and wind radar images from Jan 2016 to Dec 2021 of 14 European countries, with 1-hour temporal resolution and 31 square km spatial resolution based on the ERA5 dataset, provided by Copernicus, the European Union's Earth observation programme. We compare the proposed WF-UNet model to persistence model as well as other UNet based architectures that are trained only using precipitation radar input data. The obtained results show that WF-UNet outperforms the other examined best-performing architectures by 22%, 8% and 6% lower MSE at a horizon of 1, 2 and 3 hours respectively.

翻译：针对城市内涝、滑坡等恶劣天气及其影响的早期预警系统设计，需要准确的短期降水预报（临近预报）。临近预报是农业管理、提升飞行安全等多项环境应用中的重要任务。本研究探讨了基于UNet核心模型及其扩展模型在西欧地区未来3小时降水临近预报中的应用。具体而言，我们提出了气象融合UNet（WF-UNet）模型，该模型采用Core 3D-UNet架构，将降水量和风速变量作为输入融入学习过程，并分析其对降水目标预测任务的影响。我们收集了2016年1月至2021年12月期间14个欧洲国家的六年降水与风场雷达图像数据，基于欧盟哥白尼地球观测计划的ERA5数据集，时间分辨率为1小时，空间分辨率为31平方公里。我们将所提出的WF-UNet模型与持久性模型以及其他仅使用降水雷达输入数据训练的UNet架构模型进行了对比。结果表明，在1小时、2小时和3小时的预报时效下，WF-UNet的均方误差（MSE）分别比其他最优架构模型低22%、8%和6%。