Shengchun Wang1, Xiaozhong Yu1, Lianye Liu2, Jingui Huang1, *, Tsz Ho Wong3, Chengcheng Jiang1
CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 459-479, 2020, DOI:10.32604/cmc.2020.010627
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… More >
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