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

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 calibrat...

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Vydáno v:Geoscientific Model Development Ročník 12; číslo 8; s. 3523 - 3539
Hlavní autoři: Wang, Jiali, Wang, Cheng, Rao, Vishwas, Orr, Andrew, Yan, Eugene, Kotamarthi, Rao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Katlenburg-Lindau Copernicus GmbH 13.08.2019
European Geosciences Union
Copernicus Publications
Témata:
Calibration
Case studies
Climate models
Computation
Computer applications
Computer simulation
ENVIRONMENTAL SCIENCES
Feasibility studies
Floods
Generic drugs
High performance computing
Hydrologic cycle
Hydrologic forecasting
Hydrologic models
Hydrologic research
Hydrological cycle
Hydrology
Mathematical models
Meteorological research
Multiprocessing
Numerical models
Numerical weather prediction
Parameter estimation
Parameters
Pests
Physics
Process parameters
Resources
Retirement benefits
Water cycle
Weather
Weather forecasting
Workflow
Workflow software
Workload
United States
United States > US
ISSN:1991-9603, 1991-959X, 1991-962X, 1991-9603, 1991-962X
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Shrnutí: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).
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AC02-06CH11357
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
ISSN:1991-9603
1991-959X
1991-962X
1991-9603
1991-962X
DOI:10.5194/gmd-12-3523-2019