## 1 Introduction
In addition to GEO satellites, there are nearly two thousand low earth orbit (LEO) and medium earth orbit (MEO) satellites operating in space. In the near future, more MEO/LEO satellites will be launched to build satellite constellations such as SpaceX and OneWeb which aim to provide global Internet services [29; 30]. In order to make the service commercially available, the use-cost must be at a lower price. Compared with GEO satellites, the earth-space links of MEO/LEO satellites scan the rainfall field in a vertical direction. Based on this characteristic, some researchers have proposed to detect the precipitation in different heights by using MEO/LEO OELs [31; 32]. Moreover, the preliminary experiment results from [PERSON]'s work demonstrated that the LEO OELs have a great potential for 3-D precipitation reconstruction based on tomography techniques [6]. In practice, there are so many factors needing to be considered, such as the variation of hydrometeor phase which leads to different relations between hydrometeor and signal attenuation [33]. Therefore, this article mainly focuses on the 2-D near surface rainfall field reconstruction by GEO OLEs network, and some experiments are carried out to indicate its feasibility and reliability. So far, there have been few researches on rainfall field reconstruction by the OELs network [34; 35; 36]. In 2015, [PERSON] et al. built four Ku band OELs in the Vivarais region in the south of France and reconstructed the rainfall field based on a four-dimensional variational data assimilation (4 DVAR) method [35]. Due to the limitation of algorithm, their model had a poor performance on the rainfall events with strong advection and is restricted to short events that are less than 45 min in length. In 2020, a network consisting of 37 OELs has been deployed in the City of Genoa (Italy) by [PERSON] et al. [36]. The measurements of main rainfall events between January 2017 and December 2018 were compared with TBRGs and WR. Their results showed 10-min rain intensity is consistent with WR and peak rain intensity as well as total accumulated rain which has a good agreement with TBRGs. Furthermore, the nowcasting for instantaneous heavy rainfall has been a hot topic in meteorological fields because it is significantly important for relevant department to take timely actions for sudden natural disasters alerts such as flash floods and mudslides. To the best of our knowledge, no one else has done this work by using the OELs network. Inspired by the work of [PERSON] who achieved the extrapolation of radar echo based on the machine learning [37], we design a deep learning network to discuss the feasibility of rainfall nowcasting by using the OELs network. The aim of our work is to carry out a preliminary attempt to achieve reasonable rainfall prediction over a relatively short period of time based on OELs network measurements:  
In this paper, our contributions mainly include:  
1. Large spatial region. The reconstruction of rainfall field with \(1\times 1\) km\({}^{2}\) resolution is performed in the Jiangning district of Nanjing, China, whose area is approximately 1225 km\({}^{2}\). The experimental results show the great potential of the OELs network in large scale rainfall monitoring. 2. Lots of data validations. Except for the validation of rain intensity inversion by a single link, the performance of the OELs network for rainfall field reconstruction is validated by using the satellite data during the plum rain season from 2016 to 2019. 3. Accurate rainfall field prediction. The designed deep learning network is used to achieve rainfall field nowcasting based on the observations of the OELs network. In validation experiment, the sequential change of rainfall field is predicted successfully. The paper is organized as follows. The field measurement of rainfall from OEL is presented in Section 2. The reconstruction of rainfall field and corresponding simulation experiment are given in Section 3. A deep learning network is designed to achieve the nowcasting of the rainfall field in Section 4.