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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Cited articles

Arnault, J., Wagner, S., Rummler, T., Fersch, B., Bliefernicht, J., Andresen, S., and Kunstmann, H.: Role of runoff–infiltration partitioning and resolved overland flow on land–atmosphere feedbacks: A case study with the WRF-Hydro coupled modeling system for West Africa, J. Hydrometeorol., 17, 1489–1516, 2016. 
Beven, K. and Freer, J.: Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology, J. Hydrol., 249, 11–29, 2001. 
Campos, E. and Wang, J.: Numerical simulation and analysis of the April 2013 Chicago Floods, J. Hydrol., 531, 454–474, 2015. 
Chen, F. and Dudhia, J.: Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system, Part I: Model implementation and sensitivity, Mon. Weather Rev., 129, 569–585, 2001. 
Cuntz, M., Mai, J., Samaniego, L., Clark, M., Wulfmeyer, V., Branch, O., Attinger, S., and Thober, S.: The impact of standard and hard-coded parameters on the hydrologic fluxes in the Noah-MP land surface model, J. Geophys. Res.-Atmos., 121, 10676–10700, https://doi.org/10.1002/2016JD025097, 2016. 
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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.