## 6 Conclusions  
In this paper, we propose a new approach that using an oblique earth-space links (OELs) network achieves the reconstruction and nowcasting of rainfall field. With the widely distributed links, the OELs network has a great potential to become a futuristic rainfall monitoring system. Meanwhile, based on the deep learning, we have preliminarily achieved the nowcasting of rainfall field by using OELs network. To discuss the potential of using the OELs network in the application of rainfall field monitoring and prediction, we have set up an earthspace link in Nanjing, China to receive the 12.32 GHz vertically polarized signal from AsiaSat5. The resulting measurements are compared with the data from a modern laser dischrometer, OTT-Parsivel (OTT), which is co-located with receiving antenna. Then we select an extreme rainfall event in the plum rain season of 2020 to test this link, and a year of observations are also  
Figure 14: (**a**) A rainfall field with high kurtosis and reconstructed results by using (**b**) gaussian, (**c**) spherical and (**d**) stable models. Figure 13: The data of normalized semi-variance measured by OELs network and corresponding variogram models in (**a**) 2016, (**b**) 2017, (**c**) 2018 and (**d**) 2019.  
used to evaluate its performance. Then a monitoring network including 40 receiving antenna and 93 links are deployed in the Jiangning district of Nanjing. Combing it with the data from Climate Prediction Center morphing method (CMORPH), an interpolation method, ordinary kriging (OK), is employed to the reconstruction of rainfall field. To validate its stability and accuracy, we analyze its performance during the plum rain season of 2016, 2017, 2018 and 2019. Based on the machine learning approach, especially deep learning, we design a learning network to achieve the prediction of reginal rainfall over a relatively short period of time. Then 386 rainfall events from 2016 to 2019 are used to train learning network. A general conclusion can be drawn from this paper that the OLEs network is expected to become a new precipitation monitoring system in support of the existing TBRGs and WR observation network. The more specific conclusions are summarized as follows:  
1. For the rainfall inversion by a single OEL, the results have a good agreement with OTT measurements. In terms of the extreme rainfall event from June 13 to 16, 2020, the _RMSE_ is lower than 12 mm/h and CC is higher than 0.68. According to a year of statistical measurements, the inversion results have a reliable performance which associates with higher values of _CC_ (0.86) and \(\rho^{2}\) (0.73). However, the OLE often underestimates the peak rain intensity of heavy- and extreme rainfall because its observation space is different from OTT and the height of the 0 \({}^{\circ}\)C isotherm is not known well. 2. For the reconstruction of rainfall field, inversed results are strongly correlated with the measurements from CMORPH. It can be seen from the performances of OELs network during plum rain season of 2016, 2017, 2018 and 2019 that the increasing (or decreasing) trend of accumulated rain and the position of maximum (or minimum) value are reproduced accurately. In total, the OELs network can give reliable reconstructed rainfall fields with _RMSE_ lower than 3.46 mm/h and _CC_ higher than 0.80. 3. For the nowcasting of rainfall field, the motion of rain cell and peak rain intensity are predicted successfully, which is of great significance for natural disaster alerts. For given two examples, the values of _RMSE_ is lower than 3.5 mm/h and CC is higher than 0.77. It is also noted that the learning network has a poor performance on predictions for light rain events (0-2.5 mm/h) due to the lack of corresponding samples in the training set. In our near future work, an OELs network will be built in Nanjing, China for the application of regional precipitation monitoring.