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

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Reviewer's comment on amt-2021-175', Anonymous Referee #2, 10 Aug 2021
    • AC1: 'Reply on RC1', Yuanjian Yang, 06 Sep 2021
  • RC2: 'Comment on amt-2021-175', Anonymous Referee #1, 11 Aug 2021
    • AC2: 'Reply on RC2', Yuanjian Yang, 06 Sep 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Yuanjian Yang on behalf of the Authors (06 Sep 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (08 Sep 2021) by Simone Lolli
RR by Anonymous Referee #1 (09 Sep 2021)
ED: Publish as is (01 Oct 2021) by Simone Lolli
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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.