Articles | Volume 25, issue 1
Hydrol. Earth Syst. Sci., 25, 359–374, 2021
https://doi.org/10.5194/hess-25-359-2021
Hydrol. Earth Syst. Sci., 25, 359–374, 2021
https://doi.org/10.5194/hess-25-359-2021

Research article 21 Jan 2021

Research article | 21 Jan 2021

A two-stage blending approach for merging multiple satellite precipitation estimates and rain gauge observations: an experiment in the northeastern Tibetan Plateau

Yingzhao Ma et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (22 Apr 2020) by Fuqiang Tian
AR by Yingzhao Ma on behalf of the Authors (21 May 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (02 Jul 2020) by Fuqiang Tian
RR by Anonymous Referee #3 (09 Jul 2020)
RR by Anonymous Referee #1 (29 Aug 2020)
ED: Publish subject to revisions (further review by editor and referees) (30 Aug 2020) by Fuqiang Tian
AR by Yingzhao Ma on behalf of the Authors (04 Nov 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (09 Nov 2020) by Fuqiang Tian
RR by Anonymous Referee #3 (24 Nov 2020)
ED: Publish subject to minor revisions (review by editor) (02 Dec 2020) by Fuqiang Tian
AR by Yingzhao Ma on behalf of the Authors (03 Dec 2020)  Author's response    Manuscript
ED: Publish as is (11 Dec 2020) by Fuqiang Tian
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Short summary
A two-stage blending approach is proposed for the data fusion of multiple satellite precipitation estimates (SPEs), which firstly reduces the systematic errors of original SPEs based on a Bayesian correction model and then merges the bias-corrected SPEs with a Bayesian weighting model. The model is evaluated in the warm season of 2010–2014 in the northeastern Tibetan Plateau. Results show that the blended SPE is greatly improved compared with the original SPEs, even in heavy rainfall events.