Enhancing Weather Radar Reflectivity Emulation From Geostationary Satellite Data Using Dynamic Residual Convolutional Network

Abstract

For ground-based weather radar systems, reflectivity is particularly crucial for monitoring severe convective events. However, its limited coverage poses challenges in acquiring reliable radar data, especially for oceanic and mountainous regions. In contrast, geostationary meteorological satellites offer near-global coverage and near-real-time cloud-top observations. This article introduces a novel deep learning-based radar reflectivity emulation method to reconstruct surface radar observations from cloud-top satellite data, termed dynamic residual convolution-based network (DRC-Net), aiming to provide more accurate and reliable reflectivity data in regions lacking radar coverage. It uniquely combines dynamic convolution, which focuses attention on convolutional kernels for dynamically adjusting weights based on input, with residual convolution, effectively enhancing the network’s ability to capture intricate radar echo details. Experimental results demonstrate that DRC-Net outperforms existing methods in various assessment indices, including the probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), and Heidke skill score (HSS). Generalization tests and case studies further illustrate its effectiveness in reconstructing radar reflectivity across various regions, particularly in mountainous and oceanic areas.