Radar Reflectivity

Radar reflectivity, a measure of the intensity of radio waves reflected by atmospheric targets, is crucial for weather forecasting and various applications like autonomous driving. Current research focuses on improving reflectivity estimation and interpretation using deep learning techniques, particularly convolutional neural networks (CNNs), transformers, and recurrent neural networks (RNNs) like LSTMs, often combined with classical signal processing methods. These models are applied to enhance radar image resolution, improve quantitative precipitation estimation (QPE), and derive crucial meteorological parameters like maximum vertical velocity from radar data, leading to more accurate weather predictions and safer autonomous systems.