  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.
