Vol.65, No.1, 2020, pp.459-479, doi:10.32604/cmc.2020.010627
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: hjg@hunnu.edu.cn.
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
QPE, Z-R relationship, spatiotemporal network algorithm, radar echo.
Cite This Article
Wang, S., Yu, X., Liu, L., Huang, J., Wong, T. H. et al. (2020). An Approach for Radar Quantitative Precipitation Estimation Based on Spatiotemporal Network. CMC-Computers, Materials & Continua, 65(1), 459–479.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.