In this paper, we present two micro rain radar-based approaches to discriminate between stratiform and convective precipitation. One is based on probability density functions (PDFs) in combination with a confidence function, and the other one is an artificial neural network (ANN) classification. Both methods use the maximum radar reflectivity per profile, the maximum of the observed mean Doppler velocity per profile and the maximum of the temporal standard deviation (±15 min) of the observed mean Doppler velocity per profile from a micro rain radar (MRR). Training and testing of the algorithms were performed using a 2-year data set from the Jülich Observatory for Cloud Evolution (JOYCE). Both methods agree well, giving similar results. However, the results of the ANN are more decisive since it is also able to distinguish an inconclusive class, in turn making the stratiform and convective classes more reliable.

中文翻译：

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基于微雨雷达的降水分类算法评估层状降水和对流降水

在本文中，我们提出了两种基于微雨雷达的方法来区分层状降水和对流降水。一种是基于概率密度函数 (PDF) 结合置信函数，另一种是人工神经网络 (ANN) 分类。两种方法都使用每个剖面的最大雷达反射率、每个剖面观测到的平均多普勒速度的最大值和时间标准偏差的最大值 ( ±15 分钟）从微雨雷达 (MRR) 观察到的每个剖面的平均多普勒速度。算法的训练和测试是使用 Jülich Observatory for Cloud Evolution (JOYCE) 的 2 年数据集进行的。两种方法都很好，给出了相似的结果。然而，ANN 的结果更具决定性，因为它也能够区分不确定的类别，从而使层状和对流类别更可靠。