Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning

Abstract. Quantitative precipitation nowcasting (QPN) may assist to mitigate the tremendous socioeconomic harm caused by severe weather. Because of the rapid atmospheric variability, the QPN has been a challenging problem to solve. Recent QPN research has presented data-driven models that make use of deep learning (DL) and ground weather radar. Previous research has mostly concentrated on constructing DL models, while other elements for DL-QPN have received less attention. This research looks at four crucial aspects in the DL-QPN and their impact on predicting performance. The prediction strategy (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 were the four key factors (60 and 120 min). Using weather radar data from South Korea, twelve schemes have been developed to assess the influence of each factor. A long-term operational study for 2018–2020 was conducted, and a summer high rainfall event was examined to investigate the extreme case. In both situations, U-Net outperformed the critical score index (CSI) using a multiple prediction design. While ConvLSTM did not show a definite CSI difference across input sequence length, U-Net performed better with shorter input sequences (i.e., 60 min) than with longer input sequences (i.e., 120 min). The length of future sequences has little influence on model performance. As the lead time increased, all of the DL-QPN schemes showed underestimation and blurry outcomes. U-Net was shown to be significantly reliant on the most recent input time (i.e., 0 previous minutes) in sensitivity analysis, while ConvLSTM was more responsive to multiple time steps. This work may give a modeling technique and contingency plan for future DL-QPN employing weather radar data by explicitly comparing critical elements.