Articles | Volume 14, issue 11
https://doi.org/10.5194/amt-14-7007-2021
https://doi.org/10.5194/amt-14-7007-2021
Research article
 | 
05 Nov 2021
Research article |  | 05 Nov 2021

Leveraging machine learning for quantitative precipitation estimation from Fengyun-4 geostationary observations and ground meteorological measurements

Xinyan Li, Yuanjian Yang, Jiaqin Mi, Xueyan Bi, You Zhao, Zehao Huang, Chao Liu, Lian Zong, and Wanju Li

Viewed

Total article views: 5,016 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
4,169 742 105 5,016 201 61 77
  • HTML: 4,169
  • PDF: 742
  • XML: 105
  • Total: 5,016
  • Supplement: 201
  • BibTeX: 61
  • EndNote: 77
Views and downloads (calculated since 20 Jul 2021)
Cumulative views and downloads (calculated since 20 Jul 2021)

Viewed (geographical distribution)

Total article views: 5,016 (including HTML, PDF, and XML) Thereof 4,907 with geography defined and 109 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
Short summary
A random forest (RF) model framework for Fengyun-4A (FY-4A) daytime and nighttime quantitative precipitation estimation (QPE) is established using FY-4A multi-band spectral information, cloud parameters, high-density precipitation observations and physical quantities from reanalysis data. The RF model of FY-4A QPE has a high accuracy in estimating precipitation at the heavy-rain level or below, which has advantages for quantitative estimation of summer precipitation over East Asia in future.