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. The Weather Research and Forecasting Hydrological(WRF-Hydro) system is a state-of-the-art numerical model that models theentire hydrological cycle based on physical principles. As with otherhydrological models, WRF-Hydro parameterizes many physical processes. Hence,WRF-Hydro needs to be calibrated to optimize its output with respect toobservations for the application region. When applied to a relatively largedomain, both WRF-Hydro simulations and calibrations require intensivecomputing resources and are best performed on multimode, multicorehigh-performance computing (HPC) systems. Typically, each physics-basedmodel requires a calibration process that works specifically with that modeland is not transferrable to a different process or model. The parameterestimation tool (PEST) is a flexible and generic calibration tool that canbe used in principle to calibrate any of these models. In its existingconfiguration, however, PEST is not designed to work on the currentgeneration of massively parallel HPC clusters. To address this issue, weported the parallel PEST to HPCs and adapted it to work with WRF-Hydro. Theporting involved writing scripts to modify the workflow for differentworkload managers and job schedulers, as well as to connectthe parallel PEST to WRF-Hydro. To test the operational feasibility and thecomputational benefits of this first-of-its-kind HPC-enabled parallel PEST,we developed a case study using a flood in the midwestern United States in2013. Results on a problem involving the calibration of 22 parameters show thaton the same computing resources used for parallel WRF-Hydro, the HPC-enabledparallel PEST can speed up the calibration process by a factor of up to 15compared with commonly used PEST in sequential mode. The speedup factor isexpected to be greater with a larger calibration problem (e.g., moreparameters to be calibrated or a larger size of study area).

geoscientific model development

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