Rainfall Monitoring Based on Machine Learning by Earth-Space Link in the Ku Band

Abstract

Recently, the oblique earth-space links (OELs) between satellite and earth station have been used for rainfall monitoring as a supplement to existing observation methods. Most recent studies achieved the rainfall measurement by OELs based on the empirical method such as power-law (PL) model. In practice, two crucial issues need to be addressed: 1) identification of rain and no-rain periods; and 2) determination of attenuation baseline. To solve these problems, this article adopts several machine learning algorithms based on the analysis of earth-space link signal characteristics. For the first issue, we choose the support vector machine as a classifier and the adaptive synthetic sampling algorithm is deployed to eliminate the effects caused by the data imbalance. For the second issue, the long short-term neural network is selected for the determination of attenuation baseline since it has a good ability to solve time-series problem. In terms of the rainfall inversion, we establish a new model by combining the back-propagation (BP) network and genetic algorithm (GA). The PL model is also used as a comparison. To validate the proposed method, we set up an earth-space link that receives the signal from AsiaSat5 in 12.32 GHz. The results demonstrate that the two issues are successfully addressed and the inversion of precipitation is also carried out. Compared to disdrometer, the correlation and mean absolute error of GA-BP model are 0.83 and 1.30 mm/h, respectively, indicating a great potential to use densely OELs for global precipitation monitoring.