Advection-Free Convolutional Neural Network for Convective Rainfall Nowcasting

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Abstract
Nowcasts (i.e., short-term forecasts from 5 minutes to 6 hours) 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 convolutional neural network (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, 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.
Funding Information
  • Research Council of Finland (341964)

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