Commercial microwave links (CMLs) have proven useful for providing rainfall information close to the ground surface. However, large uncertainties are associated with these retrievals, partly due to challenges in the type of data collection and processing. In particular, the most common case is when only minimum and maximum received signal levels (RSLs) over a given time interval (hereafter 15 min) are stored by mobile network operators. The average attenuation and the corresponding rainfall rate are then calculated based on a weighted average method using the minimum and maximum attenuation. In this study, an alternative to using a constant weighted average method is explored, based on a machine learning model trained to produce actual attenuation from minimum/maximum values. A rainfall retrieval deep learning model was designed based on a long short-term memory (LSTM) model architecture and trained with disdrometer data in a form that is comparable to the data provided by mobile network operators. A first evaluation used only disdrometer data to mimic both attenuation from a CML and corresponding rainfall rates. For the test data set, the relative bias was reduced from 5.99% to 2.84% and the coefficient of determination (R2) increased from 0.86 to 0.97. The second evaluation used this disdrometer-trained LSTM to retrieve rainfall rates from an actual CML located nearby the disdrometer. A significant improvement in the overall rainfall estimation compared to existing microwave link attenuation models was observed. The relative bias reduced from 7.39% to −1.14% and the R2 improved from 0.71 to 0.82.

中文翻译：

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深度学习用于改进对商业微波链路降雨量反演的预测

商业微波链路 (CML) 已被证明可用于提供接近地表的降雨信息。然而，这些检索存在很大的不确定性，部分原因是数据收集和处理类型的挑战。特别是，最常见的情况是移动网络运营商仅存储给定时间间隔（以下称为 15 分钟）内的最小和最大接收信号电平 (RSL)。然后使用最小和最大衰减基于加权平均方法计算平均衰减和相应的降雨率。在这项研究中，基于经过训练以从最小值/最大值产生实际衰减的机器学习模型，探索了使用恒定加权平均方法的替代方法。基于长短期记忆 (LSTM) 模型架构设计降雨量检索深度学习模型，并使用与移动网络运营商提供的数据相当的形式的 disdrometer 数据进行训练。第一次评估仅使用偏差计数据来模拟来自 CML 的衰减和相应的降雨率。对于测试数据集，相对偏差从 5.99% 降低到 2.84%，决定系数 (R2) 从 0.86 增加到 0.97。第二个评估使用这个经过 disdrometer 训练的 LSTM 从位于 disdrometer 附近的实际 CML 中检索降雨率。观察到与现有微波链路衰减模型相比，总体降雨量估计有显着改进。相对偏差从 7.39% 降低到 -1.14%，R2 从 0.71 提高到 0.82。