Abstract
High-precision rainfall field reconstruction and nowcasting play an important role in many aspects of social life. In recent years, the rain-induced signal attenuation of oblique earth-space links (OELs) has been presented to monitor regional rainfall. In this paper, we set up the first OEL in Nanjing, China, for the estimation of rain intensity. A year of observations from this link are also compared with the measurements from laser disdrometer OTT-Parsivel (OTT), between which the correlation is 0.86 and the determination coefficient is 0.73. Then, the simulation experiment is carried out: an OELs network is built, and the Kriging interpolation algorithm is employed to perform rainfall field reconstruction. The rainfall fields of plum rain season from 2016 to 2019 have been reconstructed by this network, which shows a good agreement with satellite remote sensing data. The resulting root-mean-square errors are lower than 3.46 mm/h and spatial correlations are higher than 0.80. Finally, we have achieved the nowcasting of rainfall field based on a machine-learning approach, especially deep learning. It can be seen from experiment results that the motion of rain cell and the position of peak rain intensity are predicted successfully, which is of great significant for taking concerted actions in case of emergency. Our experiment demonstrates that the densely distributed OELs are expected to become a futuristic rainfall monitoring system complementing existing weather radar and rain gauge observation networks.
Highlights
Precipitation is an important chain of the water cycle and its spatiotemporal distribution is closely related to nature disasters such as floods and drought [1]
The results demonstrate that the ordinary kriging (OK) interpolation method used in our work is better at addressing this issue than inverse distance weighting (IDW) which is adopted in other relevant studies
We propose a deeplearning-based method to achieve the high spatiotemporal resolution nowcasting of rainfall field
Summary
Precipitation is an important chain of the water cycle and its spatiotemporal distribution is closely related to nature disasters such as floods and drought [1]. The latest research suggests climate changes have significantly affected the variability and trends of precipitation in China [2]. In 2020, the city of Shanghai suffered its longest plum rain season in the recent two decades. Mishra et al indicated that the probability of compound flooding from extreme precipitation and storm surge will become higher because of climate change [3]. High-resolution and accurate precipitation measurement is crucial to study and monitor this changing risk, which has a great significance on a numerical weather prediction (NWP) model that heavily relies on its initial conditions [4]
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