February 15, 2024
Journal Article

Identifying meteorological drivers for errors in modeled winds along the Northern California Coast

Abstract

An accurate wind resource dataset is required for assessing the energy yield of floating offshore wind farms that are expected along the California outer continental shelf (OCS). The National Renewable Energy Laboratory (NREL) has developed and disseminated an updated wind resource dataset for the OCS, referred to as the CA20 dataset. As compared to buoy lidar measurements that have become available recently, the CA20 dataset showed significant positive biases (> 2 m s-1) for hub-height wind speeds along northern California wind energy lease regions. To investigate the meteorological drivers for the model errors, we first consider two 1-year simulations run with two different planetary boundary layer (PBL) parameterizations: the Mellor-Yamada-Nakanishi-Niino (MYNN) PBL scheme (chosen configuration in the CA20 dataset) and the Yonsei University (YSU) PBL scheme (which significantly reduces the bias in modeled winds). By comparing the 1-year simulations to the concurrent lidar buoy observations, we find that errors are larger with the MYNN PBL scheme in warm seasons. We then dive deeper into the analysis by running simulations for short-term (3-days) case studies to evaluate the sensitivity of initial/boundary condition forcings on model results in Northern California. By analyzing the short-term simulations, we find that during synoptic scale northerly flows driven by the North Pacific High and inland thermal low, a coastal warm bias in MYNN simulation is mainly responsible for the modeled bias by altering the boundary layer thermodynamics. The results of our analysis will help guide the creation of an updated version of the CA20 dataset.

Published: February 15, 2024

Citation

Liu Y., B.J. Gaudet, R. Krishnamurthy, S. Tai, L.K. Berg, N. Bodini, and A. Rybchuk, et al. 2024. Identifying meteorological drivers for errors in modeled winds along the Northern California Coast. Monthly Weather Review 152, no. 2:455–469. PNNL-SA-181692. doi:10.1175/MWR-D-23-0030.1

Research topics