@Article{cmc.2020.010627,
AUTHOR = {Shengchun Wang, Xiaozhong Yu, Lianye Liu, Jingui Huang, Tsz Ho Wong, Chengcheng Jiang},
TITLE = {An Approach for Radar Quantitative Precipitation Estimation  Based on Spatiotemporal Network},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {1},
PAGES = {459--479},
URL = {http://www.techscience.com/cmc/v65n1/39577},
ISSN = {1546-2226},
ABSTRACT = {Radar quantitative precipitation estimation (QPE) is a key and challenging task 
for many designs and applications with meteorological purposes. Since the Z-R relation 
between radar and rain has a number of parameters on different areas, and the rainfall 
varies with seasons, the traditional methods are incapable of achieving high spatial and 
temporal resolution and thus difficult to obtain a refined rainfall estimation. This paper 
proposes a radar quantitative precipitation estimation algorithm based on the 
spatiotemporal network model (ST-QPE), which designs a convolutional time-series 
network QPE-Net8 and a multi-scale feature fusion time-series network QPE-Net22 to 
address these limitations. We report on our investigation into contrast reversal 
experiments with radar echo and rainfall data collected by the Hunan Meteorological 
Observatory. Experimental results are verified and analyzed by using statistical and 
meteorological methods, and show that the ST-QPE model can inverse the rainfall 
information corresponding to the radar echo at a given moment, which provides practical 
guidance for accurate short-range precipitation nowcasting to prevent and mitigate 
disasters efficiently.},
DOI = {10.32604/cmc.2020.010627}
}