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 present 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 paper 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 (SVM) as a classifier and the adaptive synthetic sampling algorithm (ADSYN) is deployed to eliminate the effects caused by data imbalance. For the second issue, the long short-term (LSTM) 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 12.32GHz. 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. Index Terms-rainfall monitoring, remote sensing, machine learning, earth-space link, Ku-band I. INTRODUCTION 1 CCURATE and real-time rainfall measurement plays an important role in many aspects of human life such as agricultural issues, water resource management and natural disaster warning. Existing rainfall detection method mainly comprises rain gauge, weather radar and weather satellite [1]. Based on the exploitation of existing radio spectrum sources, the opportunistic use of microwave links has become a new approach to detecting precipitation. Messer et al. firstly suggested the application of commercial wireless communication networks (CWCNs) to environmental monitoring [2]. In recent years, the use of horizontal microwave links (HMLs) has been developed rapidly in many fields such as path-average rain intensity inversion [3, 4], radar calibration [5] and regional rainfall monitoring [6].

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High-precision rainfall field reconstruction and nowcasting play an important role in many aspects of social life. In recent years, the rain-induced signal attenuation of oblique earth-space links (OELs) has been presented to monitor regional rainfall. In this paper, we set up the first OEL in Nanjing, China, for the estimation of rain intensity. A year of observations from this link are also compared with the measurements from laser disdrometer OTT-Parsivel (OTT), between which the correlation is 0.86 and the determination coefficient is 0.73. Then, the simulation experiment is carried out: an OELs network is built, and the Kriging interpolation algorithm is employed to perform rainfall field reconstruction. The rainfall fields of plum rain season from 2016 to 2019 have been reconstructed by this network, which shows a good agreement with satellite remote sensing data. The resulting root-mean-square errors are lower than 3.46 mm/h and spatial correlations are higher than 0.80. Finally, we have achieved the nowcasting of rainfall field based on a machine-learning approach, especially deep learning. It can be seen from experiment results that the motion of rain cell and the position of peak rain intensity are predicted successfully, which is of great significant for taking concerted actions in case of emergency. Our experiment demonstrates that the densely distributed OELs are expected to become a futuristic rainfall monitoring system complementing existing weather radar and rain gauge observation networks.

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High-precision retrieval of rainfall over large areas is of great importance for the research of atmospheric detection and the social life. With the rapid development of communication satellite constellations and 5G communication networks, the use of widely distributed networks of earth–space links (ESLs) and horizontal microwave links (HMLs) to retrieve rainfall over large areas has great potential for obtaining high-precision rainfall fields and complementing traditional instruments of rainfall measurement. In this paper, we carry out the research of combining multiple ESLs with HMLs to retrieve rainfall fields. Firstly, a rainfall detection network for retrieving rainfall fields is built based on the atmospheric propagation model of ESL and HML. Then, the ordinary Kriging interpolation (OK) and radial basis function (RBF) neural network are applied to the reconstruction of rainfall fields. Finally, the performance of the joint network of ESLs and HMLs to retrieve rainfall fields in the area is validated. The results show that the joint network of ESLs and HMLs based on OK algorithm and RBF neural network is capable of retrieving the distribution of rain rates in different rain cells with high accuracy, and the root mean square error (RMSE) of retrieving the rain rates of real rainfall fields is lower than 0.56 mm/h, and the correlation coefficient (CC) is higher than 0.996. In addition, the CC for retrieving stratiform rainfall and convective rainfall by the joint network of ESLs and HMLs is higher than 0.949, indicating that the characteristics of the two different types of rainfall events can be accurately monitored.

The large-scale monitoring of rainfall is of great significance in the research of meteorology, hydrology, and atmospheric measurement science. In recent years, with the quick development of communication satellite constellation, the use of Earth-space link (ESL) to measure rainfall in the atmosphere is expected to be a potential approach for the largescale monitoring of global rainfall. In this paper, to verify the long-term performance of rainfall measurement using ESL, the data of an ESL at the Ku band and a Thies Laser Precipitation Monitor (LPM) in Nanjing were collected, the rainfall inversion model using ESL was optimized according the height of 0 ℃layer from to the radiosonde data of 10 years, and the inversion results in the different types of rainfall were discussed. The results show that the rainfall inversed by the optimized ESL model are in good agreement with the rainfall measured by LPM (correlative coefficient is 0.985), the relative errors of rain intensity inversed by ESL in light rain, moderate rain, heavy rain, and extreme rain are 20.00%, 15.17%, 8.93%, and 8.99% respectively. The average relative errors (RE) of rain intensity measured by the ESL in convective rainfall and stratiform rainfall are 16.01% and 26.59% respectively.

The accurate measurement of rain intensity and its distribution in vertical direction can not only help to understand the process of rainfall development, but also play an important role in human life such as agriculture, weather forecasting, water resources management, and natural disaster warning. According to the analysis of the geometric structure of earth-space link and propagation model of electromagnetic wave in atmosphere, in this paper we propose a method to reconstruct two-dimensional(2D) vertical rainfall field by using earth-space links. Firstly, the measured data of micro rain radar (MRR) from Nanjing are used to generate three real vertical rainfall fields which are marked as I, II and III respectively. Secondly, based on the analysis of the earth-space link’s geometry and the effect of signal attenuation from other factors such as scintillation, atmosphere gas and cloud, the vertical rainfall field inversion model is established. According to the power-law relationship between rain intensity and rain attenuation, which is given by International Telecommunication Union (ITU), the simultaneous algebraic reconstruction technique (SART) is used to inverse the vertical rainfall field. Then, one earth station which can receive a 17 GHz signal from satellite is employed to detect the vertical rainfall field. However, the simulation results show that it is difficult for one earth station to achieve the inversion of rainfall field, and that the correlation coefficients between rainfall fields and inversed fields are 0.556, 0.504 and 0.364 respectively. Based on the result, two earth stations are jointly used. In this simulation, the result shows that after 500 iterations the correlation coefficients all increase above 0.98, and the average biases between rainfall field I, II, III and their inversed fields are 0.122, 0.159 and 0.537 mm/h, respectively. Meanwhile, the Euclidean distances decrease to 0.246, 0.235 and 0.812 mm/h, and the relative errors of entropy are both less than 2%. It can be seen from the inversion fields that the vertical distribution of rain rate is close to that of the real field, which suggests that the method proposed in this paper can basically achieve the inversion of vertical rainfall field by using earth-space links. In addition, with the combined detection of three earth stations the accuracy of the inversion results is significantly improved. The correlation coefficients are all close to 1 and the mean deviations are all on the order of 10^–12 mm/h, indicating that the 2D vertical rainfall fields are accurately reconstructed. In the near future, the satellite constellation system will be globally deployed, which can promote the applications of our method in areas, such as plateaus, mountains and islands, where there exist no traditional observation data, serving as a supplement to existing precipitation measurements.