Articles | Volume 12, issue 8
https://doi.org/10.5194/gmd-12-3523-2019
https://doi.org/10.5194/gmd-12-3523-2019
Development and technical paper
 | 
13 Aug 2019
Development and technical paper |  | 13 Aug 2019

A parallel workflow implementation for PEST version 13.6 in high-performance computing for WRF-Hydro version 5.0: a case study over the midwestern United States

Jiali Wang, Cheng Wang, Vishwas Rao, Andrew Orr, Eugene Yan, and Rao Kotamarthi

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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
AR by Jiali Wang on behalf of the Authors (09 Mar 2019)  Manuscript 
ED: Referee Nomination & Report Request started (24 Mar 2019) by Wolfgang Kurtz
RR by Anonymous Referee #2 (08 Apr 2019)
ED: Reconsider after major revisions (21 Apr 2019) by Wolfgang Kurtz
AR by Jiali Wang on behalf of the Authors (16 Jun 2019)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (19 Jun 2019) by Wolfgang Kurtz
RR by Anonymous Referee #2 (03 Jul 2019)
ED: Publish subject to technical corrections (12 Jul 2019) by Wolfgang Kurtz
AR by Jiali Wang on behalf of the Authors (17 Jul 2019)  Author's response   Manuscript 
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Short summary
WRF-Hydro needs to be calibrated to optimize its output with respect to observations. However, when applied to a relatively large domain, both WRF-Hydro simulations and calibrations require intensive computing resources and are best performed in parallel. This study ported an independent calibration tool (parameter estimation tool – PEST) to high-performance computing clusters and adapted it to work with WRF-Hydro. The results show significant speedup for model calibration.