Advection-Free Convolutional Neural Network for Convective Rainfall Nowcasting

Abstract

Nowcasts (i.e., short-term forecasts from 5 min to 6 h) of heavy rainfall are important for applications such as flash flood predictions. However, current precipitation nowcasting methods based on the extrapolation of radar echoes have a limited ability to predict the growth and decay of rainfall. While deep learning applications have recently shown improvement compared to extrapolation-based methods, they still struggle to correctly nowcast small-scale high-intensity rainfall. To address this issue, we present a novel model called the Lagrangian convolutional neural network (L-CNN) that separates the growth and decay of rainfall from motion using the advection equation. In the model, differences between consecutive rain rate fields in Lagrangian coordinates are fed into a U-Net-based CNN, known as RainNet, that was trained with the root-mean-squared-error loss function. This results in a better representation of rainfall temporal evolution compared to the RainNet and the extrapolation-based LINDA model that were used as reference models. On Finnish weather radar data, the L-CNN underestimates rainfall less than RainNet, demonstrated by greater POD (29% at 30 min at 1 mm·h-1 threshold) and smaller bias (98% at 15 min). The increased ETS values over LINDA for leadtimes under 15 min, with maximum increases of 7% (5 mm·h-1 threshold) and 10% (10 mm·h-1 ), show that the L-CNN represents the growth and decay of heavy rainfall more accurately than LINDA. This implies that nowcasting of heavy rainfall is improved when growth and decay are predicted using a deep learning model.