Comparison of Wind Farm Parameterization Performance in two Mesoscale Models: Preparation for Surrogate Wind Farm Model Incorporation into Global Exascale Models

High-fidelity codes for floating offshore wind energy are needed for mesoscale and global earth system modeling in high-performance computing (HPC). These codes must incorporate both floating turbines and wind farms and regional weather dynamics at multiple scales. As part of the FLoating Offshore Wind Modeling And Simulation (FLOWMAS) project, we are using results from modeling and measurements to create data-driven surrogate models that are computationally efficient and able to explore parameter spaces and time horizons not accessible to current modeling platforms. The models under development will leverage exascale computing power to create a new understanding of the floating offshore wind energy system, including impacts of climate change on offshore wind resources, the physics of floating wind farms and turbine wake dynamics, and the loads and dynamics of floating wind turbines in operational and nonoperational extreme events. Here we will present our preliminary results on the integration of current state-of-the-science wind farm modeling into the graphics processing unit (GPU) enabled Energy Resource and Forecasting Model (ERF) and the validation of these results by comparing them to those obtained using the Weather Research and Forecasting (WRF) model. Two simulations are performed with each mesoscale model: one including the Fitch parameterization and one including the Explicit Wake Parametrization (EWP). Results from these simulations are compared for both output accuracy and computational performance. These simulations are run in preparation for validation of a new surrogate model under development for integration into the global Energy Exascale Earth System Model (E3SM). The new surrogate model will communicate changes in momentum sinks, turbulent kinetic energy (TKE), wake transport, and broader, long-term weather effects to the global model.