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Quantitative precipitation nowcasting (QPN) can help to reduce the enormous socioeconomic damage caused by extreme weather. The QPN has been a challenging topic due to rapid atmospheric variability. Recent QPN studies have proposed datadriven models using deep learning (DL) and ground weather radar. Previous studies have primarily focused on developing DL models, but other factors for DL-QPN have not been thoroughly investigated. This study examines four critical factors in the DL-QPN, focusing on their effect on forecasting performance. The four key factors were the prediction design (single, recursive, and multiple predictions), deep learning model (U-Net and convolutional long short-term memory; ConvLSTM), input past sequence length (60 and 120 min), and output future sequence length (60 and 120 min). Twelve schemes were designed to measure the effects of each factor using weather radar data from South Korea. A long-term evaluation for 2018-2020 was conducted from an operational perspective, and a summer heavy rainfall event was analyzed to examine the extreme case. In both cases, U-Net demonstrated the best performance in the critical score index (CSI) with a multiple prediction design. U-Net performed better with shorter input sequences (i.e., 60 min) than with longer input sequences (i.e., 120 min), whereas ConvLSTM did not demonstrate a clear CSI difference over the input sequence length. Future sequence length did not have a significant effect on model performance. All the DL-QPN schemes exhibited underestimation and blurry results as the lead time increased. Through sensitivity analysis, U-Net was highly dependent on the most recent input time (i.e., 0 past minutes), whereas ConvLSTM was more sensitive to various time steps. With an explicit comparison of crucial factors, this study can provide a modeling strategy and contingency plan for future DL-QPN using weather radar data. [ABSTRACT FROM AUTHOR]

Numerical weather prediction models and probabilistic extrapolation methods using radar images have been widely used for precipitation nowcasting. Recently, machine-learning-based precipitation nowcasting models have also been actively developed for relatively short-term precipitation predictions. This study aimed to develop a radar-based precipitation nowcasting model using an advanced machine learning technique, conditional generative adversarial network (cGAN), which shows high performance in image generation tasks. The cGAN-based precipitation nowcasting model, named Rad-cGAN, developed in this study was trained with a radar reflectivity map of the Soyang-gang Dam region in South Korea with a spatial domain of 128 × 128 km, spatial resolution of 1 km, and temporal resolution of 10 min. The model performance was evaluated using previously developed machine-learning-based precipitation nowcasting models, namely convolutional long short-term memory (ConvLSTM) and U-Net, as well as the baseline Eulerian persistence model. We demonstrated that Rad-cGAN outperformed other models not only for the chosen site but also for the entire domain across the Soyang-gang Dam region. Additionally, the proposed model maintained good performance even with lead times up to 80 min based on the critical success index at the intensity threshold of 0.1 mm h-1, while RainNet and ConvLSTM achieved lead times of 70 and 40 min, respectively. We also demonstrated the successful implementation of the transfer learning technique to efficiently train model with the data from other dam regions in South Korea, such as the Andong and Chungju Dam regions. We used pre-trained model, which was completely trained in the Soyang-gang Dam region. This study confirms that Rad-cGAN can be successfully applied to precipitation nowcasting with longer lead times, and using the transfer learning approach it shows good performance in regions other than the originally trained region. [ABSTRACT FROM AUTHOR]