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.

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.

Lightning detection is of great importance for disaster protection and atmospheric science. For accurate detection of the remote lightning return strokes outside the detection network, the time reversal with auxiliary differential equation finite difference time domain (TR with ADE-FDTD) method that adopts the physical propagation model is proposed to overcome the effect of the long-distance propagation of lightning electromagnetic pulse (LEMP) signals. First, the numerical model of the long-distance propagation of LEMP in the earth-ionosphere waveguide (EIWG) is constructed by using the auxiliary differential equation finite difference time domain (ADE-FDTD). Then, the time reversal signals of LEMP are innovatively put into the ADE-FDTD model for reverse propagation, so as to implement the accurate location of the lightning radiation source by the time reversal (TR) method. The experimental results show that the location error of the local lightning detection network using the TR with ADE-FDTD method is only 0.311 km for a natural lightning return stroke outside its boundary at a distance of 298.852 km from its center, and the location error of a lightning strike to the tall tower outside the local lightning detection network at a distance of 744.942 km away from its center can be improved to 2.649 km, which is significantly better than that of the traditional lightning location methods. The implementation of the method has great potential for extending the location range of a local lightning detection network consisting of a limited number of sensors and improving the location accuracy of the remote lightning.

A single-station lightning location has the obvious advantages of low cost and convenience in lightning monitoring and warning. To address the critical challenge of distance estimation accuracy in this technology, we propose a novel physical time-reversal (PTR) method to utilize the full wave information of both the ground wave and the sky wave in the detected signal. First, we improved the numerical model for accurately calculating the lightning sferics signals in the complex propagation environment of the Earth–ionosphere waveguide using the measured International Reference Ionosphere 2020. Subsequently, the sferics signal with multipath effect is transformed by time reversal and back propagated in the numerical model. Furthermore, a broadening factor reflecting the waveform dispersion in the back propagation is defined as the single-station focusing criterion to determine the optimal lightning propagation distance, considering the multipath effect and the focus of the PTR process. The experimental results demonstrate that the average root mean square error (RMSE) and the mean relative error (MRE) of the PTR method for the lightning distance estimation in the numerical simulation within the range of 100–1200 km are 5.517 km and 1.21%, respectively, and the average RMSE and the MRE for the natural lightning strikes to the Canton Tower from the measured data in the range of 181.643–1152.834 km are 9.251 km and 2.07%, respectively. Moreover, the correlation coefficients of the detection results are all as high as 0.999. These results indicate that the PTR method significantly outperforms the traditional ionospheric reflection method, demonstrating that it is able to perform a more accurate single-station lightning distance estimation by utilizing the compensation mechanism of the multipath effect on the sferics. The implementation of the proposed method has significant application value for improving the accuracy of single-station lightning location.

In this paper, a novel adaptive rain-induced attenuation-rain rate ( A - R ) relationship model is proposed, which is built based on a clustering algorithm to improve the accuracy of rainfall inversion based on commercial microwave link (CML). The performance of this adaptive A - R relationship is systematically evaluated at eight common CML operating frequencies with five years of raindrop size distribution data from a PARSIVEL disdrometer. The results show that the overall single-frequency adaptive A - R relationship has a better performance than the ITU-R and local fitting A - R relationships (lower root mean square error, relative bias (an improvement of around 10% at several of the frequencies), and significant unbiasedness). In addition, the effect of cluster number on the A - R relationship and the performance of dual-frequency or dual-polarization adaptive A - R relationships are also discussed. Finally, the relative errors of these A - R relationships on the inversion of cumulative rainfall are depicted to visualize the performance, which demonstrate that certain single/dual-frequency adaptive A - R models have good performance in most cases.

High-precision rainfall information is of great importance for the improvement of the accuracy of numerical weather prediction and the monitoring of floods and mudslides that affect human life. With the rapid development of satellite constellation networks, there is great potential for reconstructing high-precision rainfall fields in large areas by using widely distributed Earth–space link (ESL) networks. In this paper, we have carried out research on reconstructing high-precision rainfall fields using an ESL network with the compressed sensing (CS) method in the case of a sparse distribution of the ESLs. Firstly, ESL networks with different densities are designed using the K-means clustering algorithm. The real rainfall fields are then reconstructed using the designed ESL networks with CS, and the reconstructed results are compared with that of the inverse distance weighting (IDW) algorithm. The results show that the root mean square error (RMSE) and correlation coefficient (CC) of the reconstructed rainfall fields using the ESL network with CS are lower than 0.15 mm/h and higher than 0.999, respectively, when the density is 0.05 links per square kilometer, indicating that the ESL network with CS is capable of reconstructing the high-precision rainfall fields under sparse sampling. Additionally, the performance of reconstructing the rainfall fields using the ESL networks with CS is superior compared to the reconstructed results of the IDW algorithm.