Open Access
ARTICLE
An Approach for Radar Quantitative Precipitation Estimation Based on Spatiotemporal Network
Shengchun Wang1, Xiaozhong Yu1, Lianye Liu2, Jingui Huang1, *, Tsz Ho Wong3, Chengcheng Jiang1
1 School of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China.
2 Hunan Meteorological Observatory, Changsha, 410118, China.
3 Blackmagic Design, Rowville, VIC 3178, Australia.
* Corresponding Author: Jingui Huang. Email: .
Computers, Materials & Continua 2020, 65(1), 459-479. https://doi.org/10.32604/cmc.2020.010627
Received 14 March 2020; Accepted 13 May 2020; Issue published 23 July 2020
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.
Keywords
Cite This Article
S. Wang, X. Yu, L. Liu, J. Huang, T. Ho Wong
et al., "An approach for radar quantitative precipitation estimation based on spatiotemporal network,"
Computers, Materials & Continua, vol. 65, no.1, pp. 459–479, 2020. https://doi.org/10.32604/cmc.2020.010627
Citations