Enhanced AI-Based Growth and Decay Rate Estimation for Radar Precipitation Nowcasting in Korea

  The increasing frequency of localized convective precipitation events demands higher precision in short-range forecasting to support early warning and public safety. The Korea Meteorological Administration (KMA) utilizes the MOtion vector estimation and extrapolation of radar echo for Integrated Operational Nowcasting (MOTION) system as its primary tool. While MOTION provides a stable operational baseline through kinematic extrapolation, its performance is fundamentally limited by a linear formulation that cannot fully represent the rapid, nonlinear intensity changes characteristic of convective systems.
  This study enhances the MOTION framework—which conventionally integrates multi-resolution motion vectors, variational optimization, and Semi-Lagrangian extrapolation—by replacing numerical Growth and Decay Rate (GDR) estimation with a data-driven AI approach. We rigorously compared three architectures: Multi-Layer Perceptron (MLP), Deep Generative Model of Rain (DGMR), and RainNet. Among these candidates, the RainNet model consistently achieved the highest accuracy across all validation metrics. To ensure computational efficiency, SHapley Additive exPlanations (SHAP) analysis was applied to evaluate feature importance, revealing that the GDR from the previous time step was the dominant input variable. This result enabled a significant reduction in computational load while maintaining high forecast performance.
  The proposed framework was validated over consecutive convective seasons. Quantitative results from June to September 2024 showed an 11% reduction in Mean Absolute Percentage Error (MAPE) compared to the original numerical method for lead times up to 120 minutes. Subsequent updates in 2025, featuring higher-resolution data and model optimization, led to an additional 10% improvement in MAPE over the initial AI version. These cumulative improvements demonstrate the effectiveness of the AI-integrated approach. Following a successful trial, the enhanced system has been formally integrated into the KMA’s operational environment, providing a more reliable foundation for high-resolution precipitation nowcasting.

This research was supported by the "Development of radar-based technology for nowcasting information computation (KMA2026-00221)" of "Development of severe weather response technology based on National Radar Convergence" project funded by the Weather Radar Center, Korea Meteorological Administration.