Enhancing weather radar data by removing non-meteorological echoes, using neural networks trained on synthetic weather data

Meteorological weather radars are essential for atmospheric research, weather forecasting and aviation safety, but they often detect non-meteorological echoes from scatterers such as insects, birds, and ground clutter. These non-meteorological echoes can then lead to misinterpretations in quantitative precipitation estimation and hydrometeor classification, which cause difficulties for atmospheric research and weather forecasting. This paper introduces a novel AI-based approach to identify such non-meteorological echoes in polarimetric radar data using a convolutional neural network. More specifically, we utilize a so-called U-net, which relies on large amounts of labeled radar data for training. To address the challenge of acquiring labeled radar data consisting of meteorological and non-meteorological echoes, we generate synthetic training samples by combining preprocessed winter data (meteorological echoes) with cluttered summer data (non-meteorological echoes) provided by Deutscher Wetterdienst (DWD). After training on synthetic data, evaluation of the U-net approach on operationally measured radar data shows that it outperforms the state-of-the-art DWD classification algorithm overall. This is particularly evident in the preservation of precipitation signals at the boundaries of larger weather events.