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Title: 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

Abstract

The Weather Research and Forecasting Hydrological (WRF-Hydro) system is a state-of-the-art numerical model that models the entire hydrological cycle based on physical principles. As with other hydrological models, WRF-Hydro parameterizes many physical processes. Hence, WRF-Hydro needs to be calibrated to optimize its output with respect to observations for the application region. When applied to a relatively large domain, both WRF-Hydro simulations and calibrations require intensive computing resources and are best performed on multimode, multicore high-performance computing (HPC) systems. Typically, each physics-based model requires a calibration process that works specifically with that model and is not transferrable to a different process or model. The parameter estimation tool (PEST) is a flexible and generic calibration tool that can be used in principle to calibrate any of these models. In its existing configuration, however, PEST is not designed to work on the current generation of massively parallel HPC clusters. To address this issue, we ported the parallel PEST to HPCs and adapted it to work with WRF-Hydro. The porting involved writing scripts to modify the workflow for different workload managers and job schedulers, as well as to connect the parallel PEST to WRF-Hydro. To test the operational feasibility and the computational benefits ofmore » this first-of-its-kind HPC-enabled parallel PEST, we developed a case study using a flood in the midwestern United States in 2013. Results on a problem involving the calibration of 22 parameters show that on the same computing resources used for parallel WRF-Hydro, the HPC-enabled parallel PEST can speed up the calibration process by a factor of up to 15 compared with commonly used PEST in sequential mode. The speedup factor is expected to be greater with a larger calibration problem (e.g., more parameters to be calibrated or a larger size of study area).« less

Authors:
 [1]; ORCiD logo [1];  [1];  [1];  [1]; ORCiD logo [1]
  1. Argonne National Lab. (ANL), Lemont, IL (United States)
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1557999
Grant/Contract Number:  
AC02-06CH11357
Resource Type:
Accepted Manuscript
Journal Name:
Geoscientific Model Development (Online)
Additional Journal Information:
Journal Name: Geoscientific Model Development (Online); Journal Volume: 12; Journal Issue: 8; Journal ID: ISSN 1991-9603
Publisher:
European Geosciences Union
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Wang, Jiali, Wang, Cheng, Rao, Vishwas, Orr, Andrew, Yan, Eugene, and Kotamarthi, Rao. 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. United States: N. p., 2019. Web. doi:10.5194/gmd-12-3523-2019.
Wang, Jiali, Wang, Cheng, Rao, Vishwas, Orr, Andrew, Yan, Eugene, & Kotamarthi, Rao. 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. United States. doi:10.5194/gmd-12-3523-2019.
Wang, Jiali, Wang, Cheng, Rao, Vishwas, Orr, Andrew, Yan, Eugene, and Kotamarthi, Rao. Tue . "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". United States. doi:10.5194/gmd-12-3523-2019. https://www.osti.gov/servlets/purl/1557999.
@article{osti_1557999,
title = {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},
author = {Wang, Jiali and Wang, Cheng and Rao, Vishwas and Orr, Andrew and Yan, Eugene and Kotamarthi, Rao},
abstractNote = {The Weather Research and Forecasting Hydrological (WRF-Hydro) system is a state-of-the-art numerical model that models the entire hydrological cycle based on physical principles. As with other hydrological models, WRF-Hydro parameterizes many physical processes. Hence, WRF-Hydro needs to be calibrated to optimize its output with respect to observations for the application region. When applied to a relatively large domain, both WRF-Hydro simulations and calibrations require intensive computing resources and are best performed on multimode, multicore high-performance computing (HPC) systems. Typically, each physics-based model requires a calibration process that works specifically with that model and is not transferrable to a different process or model. The parameter estimation tool (PEST) is a flexible and generic calibration tool that can be used in principle to calibrate any of these models. In its existing configuration, however, PEST is not designed to work on the current generation of massively parallel HPC clusters. To address this issue, we ported the parallel PEST to HPCs and adapted it to work with WRF-Hydro. The porting involved writing scripts to modify the workflow for different workload managers and job schedulers, as well as to connect the parallel PEST to WRF-Hydro. To test the operational feasibility and the computational benefits of this first-of-its-kind HPC-enabled parallel PEST, we developed a case study using a flood in the midwestern United States in 2013. Results on a problem involving the calibration of 22 parameters show that on the same computing resources used for parallel WRF-Hydro, the HPC-enabled parallel PEST can speed up the calibration process by a factor of up to 15 compared with commonly used PEST in sequential mode. The speedup factor is expected to be greater with a larger calibration problem (e.g., more parameters to be calibrated or a larger size of study area).},
doi = {10.5194/gmd-12-3523-2019},
journal = {Geoscientific Model Development (Online)},
number = 8,
volume = 12,
place = {United States},
year = {2019},
month = {8}
}

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