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.
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.
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