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.

This article proposes a new rainfall-type classification method using the differential attenuation rates (DARs) calculated from microwave links with multiple frequencies and dual polarization. Several machine-learning algorithms were employed for classification [decision tree (DT), probabilistic neural network (PNN), Gaussian discriminant analysis (GDA), and logistic regression (LR)]. This method was simulated by two or three frequencies (combination of frequencies of 15, 25, 38, 60, and 80 GHz). The results showed that DT, PNN, GDA, and LR achieved maximum accuracies of 88.9%, 84.6%, 66.8%, and 67.6% for the dual-frequency model, respectively, and 89.6%, 85.8%, 67.6%, and 68.7% for the tri-frequency model, respectively, which is obviously better than the classification method based on the sequence of rain rates from the ITU-R model. The model accuracies based on GDA and LR algorithms are obviously lower than those based on DT and PNN due to the defects of the algorithms. In addition, the effects of different noise levels on classification performance are also discussed.

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.

Based on the power-law relation between rain intensity and corresponding specific attenuation, precipitation inversion by oblique earth-space link (OEL) has become a supplement to conventional method. However, the attenuation caused by melting layer has a significant effect on its performance. According to the microphysics, thermodynamics, and scattering properties of melting hydrometeors, a melting layer model is introduced to improve OEL inversion approach. Meanwhile, the observations by a microrain radar (MRR) located at Nanjing Reference Climatological Station (NRCS) are used and plenty of numerical simulations are also performed, which aim to validate the melting layer model. The choice of raindrop size distribution (DSD) determines the simulated values of radar reflectivity factor (RRF) and path-integrated attenuation (PIA). The gamma distribution gives the results of simulated RRF that are consistent with MRR observations. Considering the nonspherical shape of melting hydrometeors, its effects on attenuation calculation are discussed based on T-matrix, which indicates the error due to hydrometeor shape is negligible below Ka-band. The values of PIA derived from our melting layer model show good agreements with MRR observations. In the simulation experiment, we found the average multiplier of melting layer attenuation is a function of rain intensity and frequency. The relation is established by using the nonlinear least-squares method. According to the multiplier, a novel model is proposed to estimate the melting layer attenuation. Compared with other existing attenuation models, it shows a similar performance in different frequency bands. Moreover, we introduce an equivalent multiplier into OEL rainfall inversion model, which demonstrates the obvious enhancement of effectiveness and robustness of improved model in the validation experiment.

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 large-scale monitoring of global rainfall. In this article, 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 to the height of 0 °C-layer from the radiosonde data of ten 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%, 15.17%, 8.93%, and 8.99%, respectively. The average relative errors of rain intensity measured by the ESL in convective rainfall and stratiform rainfall are 16.01% and 26.59%, respectively.