mlsanityData to 7 Oct 2026

PCT-CycleGAN: Paired Complementary Temporal Cycle-Consistent Adversarial Networks for Radar-Based Precipitation Nowcasting

arXiv 2211.15046 ↗First posted 28 Nov 2022cs.LG cs.AI cs.CVcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 24 of its 65 references that are in the map. First posted on arXiv 28 Nov 2022.

Builds on

24 of 65 references.

References · 24

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12 more
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Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 2.

4 papers.

  1. 2023Foundation Models for Weather and Climate Data Understanding: A Comprehensive Surveycited by 32
  2. 2025Deep Learning and Foundation Models for Weather Prediction: A Surveycited by 15
  3. 2024Deep learning for precipitation nowcasting: A survey from the perspective of time series forecastingcited by 14
  4. 2025Advanced Torrential Loss Function for Precipitation Forecastingcited by 0