Assessment of surface wind datasets for estimating offshore wind energy along the Central California Coast

Yi-Hui Wang, Ryan K. Walter, Crow White, Hayley Farr and Benjamin I. Ruttenberg

Renewable Energy, 2019, vol. 133, issue C, 343-353

Abstract: In the United States, Central California has gained significant interest in offshore wind energy due to its strong winds and proximity to existing grid connections. This study provides a comprehensive evaluation of near-surface wind datasets in this region, including satellite-based observations (QuikSCAT, ASCAT, and CCMP V2.0), reanalysis (NARR and MERRA), and regional atmospheric models (WRF and WIND Toolkit). This work highlights spatiotemporal variations in the performance of the respective datasets in relation to in-situ buoy measurements using error metrics over both seasonal and diurnal time scales. The two scatterometers (QuikSCAT and ASCAT) showed the best overall performance, albeit with significantly less spatial and temporal resolution relative to other datasets. These datasets only slightly outperformed the next best dataset (WIND Toolkit), which has significantly greater temporal and spatial resolution as well as estimates of winds aloft. Considering tradeoffs between spatiotemporal resolution of the underlying datasets, error metrics relative to in-situ measurements, and the availability of data aloft, the WIND Toolkit appears to be the best dataset for this region. The framework and tradeoff analysis this research developed and demonstrated to assess offshore wind datasets can be applied in other regions where offshore wind energy is being considered.

Keywords: Offshore wind energy; Scatterometers; Reanalyses; Regional atmospheric models; Surface winds; Tradeoff analysis (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:133:y:2019:i:c:p:343-353

DOI: 10.1016/j.renene.2018.10.008

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