The precipitation nowcasting methods have been elaborated over the centuries because rain has a crucial impact on human life. Not only quantitative precipitation forecast (QPF) models and convolutional long short-term memory (ConvLSTM), but also various sophisticated methods such as the latest MetNet-2 are emerging. In this paper, we propose a paired complementary temporal cycle-consistent adversarial networks (PCT-CycleGAN) for radar-based precipitation nowcasting, inspired by cycle-consistent adversarial networks (CycleGAN), which shows strong performance in image-to-image translation. PCT-CycleGAN generates temporal causality using two generator networks with forward and backward temporal dynamics in paired complementary cycles. Each generator network learns a huge number of one-to-one mappings about time-dependent radar-based precipitation data to approximate a mapping function representing the temporal dynamics in each direction. To create robust temporal causality between paired complementary cycles, novel connection loss is proposed. And torrential loss to cover exceptional heavy rain events is also proposed. The generator network learning forward temporal dynamics in PCT-CycleGAN generates radar-based precipitation data 10 minutes from the current time. Also, it provides a reliable prediction of up to 2 hours with iterative forecasting. The superiority of PCT-CycleGAN is demonstrated through qualitative and quantitative comparisons with several previous methods.

翻译：降水临近预报方法在几个世纪以来不断被完善，因为降雨对人类生活具有关键影响。不仅定量降水预报（QPF）模型和卷积长短期记忆网络（ConvLSTM），还有最新的MetNet-2等各种复杂方法不断涌现。本文受循环一致性对抗网络（CycleGAN）在图像到图像转换中强大性能的启发，提出了一种用于雷达降水临近预报的配对互补时间循环一致性对抗网络（PCT-CycleGAN）。PCT-CycleGAN通过两个生成器网络在配对互补循环中实现前向和后向时间动态，从而生成时间因果关系。每个生成器网络学习大量关于时间依赖的雷达降水数据的一对一映射，以逼近表示每个方向时间动态的映射函数。为建立配对互补循环之间的稳健时间因果关系，本文提出了新型连接损失。此外，还提出了覆盖异常强降雨事件的暴雨损失。PCT-CycleGAN中学习前向时间动态的生成器网络可生成当前时间后10分钟的雷达降水数据，并通过迭代预测提供长达2小时的可靠预报。通过与多种先前方法的定性和定量比较，验证了PCT-CycleGAN的优越性。