A Deep Learning Model for Precipitation Nowcasting Using Multiple Optical Flow Algorithms

Abstract The optical flow technique has advantages in motion tracking and has long been employed in precipitation nowcasting to track the motion of precipitation fields using ground radar datasets. However, the performance and forecast timescale of models based on optical flow are limited. Here, we present the results of the application of the deep learning method to optical flow estimation to extend its forecast timescale and enhance the performance of nowcasting. It is shown that deep learning model can better capture both multi-spatial and multi-temporal motions of precipitation events compared with traditional optical flow estimation methods. The model comprises two components: (1) a regression process based on multiple optical flow algorithms, which more accurately captures multi-spatial features compared with a single optical flow algorithm, and (2) a U-Net-based network that trains multi-temporal features of precipitation movement. We evaluated the model performance with cases of precipitation in South Korea. In particular, the regression process minimizes errors by combining multiple optical flow algorithms with a gradient descent method and outperforms other models using only a single optical flow algorithm up to a 3-h lead time. Additionally, the U-Net plays a crucial role in capturing nonlinear motion that cannot be captured by a simple advection model through traditional optical flow estimation. Consequently, we suggest that the proposed optical flow estimation method with deep learning could play a significant role in improving the performance of current operational nowcasting models, which are based on traditional optical flow methods.