A deep learning approach to estimate the mean Doppler velocity and spectral width in weather radars is presented. It can operate in scenarios with and without the presence of ground clutter. The method uses a deep neural network with two branches, one for velocity and the other for spectral width estimation. Different network architectures are analysed and one is selected based on its validation performance, considering both serial and parallel implementations . Training is performed using synthetic data covering a wide range of possible scenarios. Monte Carlo realisations are used to evaluate the performance of the proposed method for different weather conditions. Results are compared against two standard methods, pulse-pair processing (PPP) for signals without ground clutter and Gaussian model adaptive processing (GMAP) for signals contaminated with ground clutter. Better estimates are obtained when comparing the proposed algorithm against GMAP and comparable results when compared against PPP. The performance is also validated using real weather data from the C-band radar RMA-12 located in San Carlos de Bariloche, Argentina. Once trained, the proposed method requires a moderate computational load and has the advantage of processing all the data at once, making it a good candidate for real-time implementations.

中文翻译：

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使用深度学习的天气雷达中的多普勒处理

提出了一种估计天气雷达中平均多普勒速度和频谱宽度的深度学习方法。它可以在有地面杂波或没有地面杂波的情况下运行。该方法使用具有两个分支的深层神经网络，一个分支用于速度，另一个分支用于谱宽估计。考虑到串行和并行实现，分析了不同的网络体系结构并根据其验证性能选择了一种 。培训是使用综合数据进行的，涵盖了多种可能的情况。蒙特卡洛实现用于评估所提出方法在不同天气条件下的性能。将结果与两种标准方法进行比较：无地杂波信号的脉冲对处理（PPP）和被地杂波污染的信号的高斯模型自适应处理（GMAP）。将建议的算法与GMAP进行比较时，可获得更好的估计；与PPP相比，可获得可比的结果。还使用来自阿根廷圣卡洛斯·德巴里洛切的C波段雷达RMA-12的真实天气数据对性能进行了验证。经过训练后，提出的方法需要适度的计算负荷，并且具有一次处理所有数据的优势，