High spatiotemporal resolution rainfall is needed in predicting flash floods, local climate impact studies and agriculture management. Rainfall estimation techniques like satellites and the commercial microwave links (MWL) rainfall estimation have independently made significant advancements in high spatiotemporal resolution rainfall estimation. However, their combination for rainfall estimation has received little attention, while it could benefit many applications in ungauged areas. This study investigated the usability of the random forest (RF) algorithm trained with MWL rainfall and Meteosat Second Generation (MSG) based cloud top properties for estimating high spatiotemporal resolution rainfall in the sparsely gauged Kenyan Rift Valley. Our approach retrieved cloud top properties for use as predictor variables from rain areas estimated from the MSG data and estimated path average rainfall intensities from the MWL to serve as the target variable. We trained and validated the RF algorithm using parameters derived through optimal parameter tuning. The RF rainfall intensity estimates were compared with gauge, MWL, Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) and European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Multisensor Precipitation Estimate (MPE) to evaluate its rainfall intensities from point and spatial perspectives. The results can be described as good, considering they were achieved in near real-time, pointing towards a promising rainfall estimation alternative based on the RF algorithm applied to MWL and MSG data. The applicative benefits of this technique could be huge, considering that many ungauged areas have a growing MWL network and MSG and, in the future, Meteosat Third Generation (MTG) coverage.

预测山洪、当地气候影响研究和农业管理需要高时空分辨率降雨。卫星和商业微波链路 (MWL) 降雨估算等降雨估算技术在高时空分辨率降雨估算方面取得了重大进展。然而，它们对降雨量估计的组合很少受到关注，但它可以使许多在未测量区域的应用受益。本研究调查了使用 MWL 降雨和基于 Meteosat 第二代 (MSG) 的云顶属性训练的随机森林 (RF) 算法在估计稀疏测量的肯尼亚裂谷中高时空分辨率降雨的可用性。我们的方法从 MSG 数据估计的雨区中检索云顶属性用作预测变量，并从 MWL 估计路径平均降雨强度作为目标变量。我们使用通过优化参数调整得出的参数训练和验证了 RF 算法。将射频降雨强度估计值与测量仪、MWL、全球降水测量 (GPM) 综合多卫星 GPM (IMERG) 和欧洲气象卫星开发组织 (EUMETSAT) 多传感器降水估计 (MPE) 进行比较，以评估其降雨量从点和空间的角度看强度。考虑到它们是近乎实时的，结果可以说是好的，指向基于应用于 MWL 和 MSG 数据的 RF 算法的有希望的降雨估计替代方案。考虑到许多未测量区域的 MWL 网络和 MSG 以及未来的第三代气象卫星 (MTG) 覆盖范围不断扩大，这种技术的应用优势可能是巨大的。