A data-driven precipitation nowcasting framework using advanced deep learning model for video prediction and real-time learning approaches

Quantitative precipitation nowcasting (QPN) is crucial for forecasting precipitation within the next several hours (generally up to 6) to prevent substantial socioeconomic damage. In general, ground radar data has been widely employed in QPN due to its high spatial-temporal resolution and more precise precipitation estimation than satellite. With the remarkable success of deep learning (DL), recent QPN studies have actively adopted DL using radar data. Although these studies yielded high skill scores in forecasting precipitation areas with a weak intensity (about 1 mm/h), they failed to effectively simulate the horizontal movement of precipitation areas and showed poor ability in forecasting precipitation with stronger intensities. In addition, despite the fact that the skill score is highly dependent on the characteristics of each precipitation event, there was a lack of evaluation over various precipitation cases. From the motivation that there can be room for improving QPN using the advanced DL model in video prediction, this study suggests the QPN model based on simple yet better video prediction (SimVP), which is a state-of-the-art DL model. We trained the SimVP model using radar data in South Korea from June to September (JJAS) for the period of 2019-2022, which includes the summer and early fall. In terms of the critical score index (CSI) with a lead time of 120 minutes (0.46, 0.23, and 0.09 for 1, 5, and 10 mm/h thresholds, respectively), the proposed model showed significant improvement over the existing DL models based on an evaluation from JJAS 2022. Considering different precipitation conditions, three case studies were conducted for heavy rainfall, typhoons, and fast-moving narrow convection events. The suggested model showed comparable or the highest CSI in 120 min with a 1 mm/h threshold in all cases, demonstrating robust performance (0.49, 0.69, and 0.29 for heavy rainfall, typhoon, and narrow convection, respectively). Qualitative evaluation of the proposed model also showed better results in terms of horizontal displacement movement and less underestimation than the other models. In addition, we further explored the possibility of real-time learning (RTL) with newly added radar data. By repeatedly optimizing DL model for currently facing precipitation events, RTL contributed to deep learning models predicting results more similar to actual radar patterns. It is expected that the proposed SimVP and RTL would serve as a new baseline for DL-based QPN due to their ease of implementation and enhanced performance.