Research on the Method of Rainfall Field Retrieval Based on the Combination of Earth–Space Links and Horizontal Microwave Links

Abstract

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.

Reconstructing and Nowcasting the Rainfall Field by a CML Network

“…However, OK weights are a set of optimal coefficients based on the relationship between distance and semi‐variance (David et al., 1984). In addition, related studies have shown CML products interpolated by OK have excellent results in rainfall field reconstruction (Eshel et al., 2021; Graf et al., 2021; Haese et al., 2017; Zhao et al., 2022). The procedure of OK is as follows: Calculate the distance h ij and the semi‐variance γ ij between measurement points: $$${h}_{ij}=\sqrt{{\left({X}_{i}-{X}_{j}\right)}^{2}+{\left({Y}_{i}-{Y}_{j}\right)}^{2},}$$$ $$${\gamma }_{ij}=\frac{1}{2}{\left({R}_{i}-{R}_{j}\right)}^{2},$$$ where R i is the rainfall intensity of the measurement point ( X i , Y i ) and γ ij is the semi‐variance between the i th and j th measurement points. The spherical model is adopted to fit the relationship between distance and semi‐variance to acquire the expression of the variance function γ . The semi‐variance between each measurement and the interpolated point is calculated according to γ , and the weight coefficient of measurement points can be solved by the weight matrix: …”

“…However, OK weights are a set of optimal coefficients based on the relationship between distance and semi-variance (David et al, 1984). In addition, related studies have shown CML products interpolated by OK have excellent results in rainfall field reconstruction (Eshel et al, 2021;Graf et al, 2021;Haese et al, 2017;Zhao et al, 2022). The procedure of OK is as follows:…”

“…Especially in the case of short CMLs and light rainfall, the compensation of WAA cannot be neglected. After obtaining the rainfall intensity, to reconstruct rainfall fields, spatial interpolation algorithms (Messer et al., 2008), machine learning algorithms (Zhao et al., 2022), and tomographic methods (D’Amico et al., 2016) are adopted. Finally, deep learning approaches are employed to nowcast the rainfall fields (Imhoff et al., 2020; Jacoby et al., 2020).…”

Precipitation Monitoring Using Commercial Microwave Links: Current Status, Challenges and Prospectives

“…Furthermore, with the development of satellite communications, a large number of satellites are expected to be launched in the near future [205]. Therefore, fusing opportunistic rainfall information from ESLs may be a topic for improving and expanding rainfall estimation based on CMLs [187,206,207]. However, most of the existing studies are still limited to examining rainfall measurement techniques based on CMLs and ESLs separately, and works on combining CMLs and ESLs have not yet been emphasized [208].…”