Evaluation of a high-resolution regional climate simulation for surface and hub-height wind climatology over North America

Peco, Kyle; Wang, Jiali; Jung, Chunyong; Sever, Gökhan; Sheridan, Lindsay; Feinstein, Jeremy; Kotamarthi, Rao; Draxl, Caroline; Young, Ethan; Purkayastha, Avi; Kumler, Andrew

doi:10.5194/wes-11-13-2026

Title: Evaluation of a high-resolution regional climate simulation for surface and hub-height wind climatology over North America

Journal Article · Wed Jan 07 00:00:00 UTC 2026 · Wind Energy Science (Online)

DOI: https://doi.org/10.5194/wes-11-13-2026 · OSTI ID:3017178

^[1]; Wang, Jiali ^[1];

^[1]; Sever, Gökhan ^[1];

^[2]; Feinstein, Jeremy ^[1];

^[1];

^[3];

^[4]; Purkayastha, Avi ^[4]; Kumler, Andrew ^[4]

Argonne National Laboratory (ANL), Argonne, IL (United States)
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
National Renewable Energy Laboratory (NREL), Golden, CO (United States); Electric Power Research Inst. (EPRI), Palo Alto, CA (United States)
National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Assessing the availability of key wind resources requires augmenting observations to support the implementation of wind energy infrastructure. However, observations are limited, necessitating the development of high-resolution, long-term gridded datasets. This study presents a robust, dynamically downscaled climatological dataset, offering 20 years of hourly wind data at a 4 km spatial resolution across North America, and evaluates its performance against observations, including meteorological towers and automated surface-observing system (ASOS) stations, as well as coarse-resolution reanalysis data (the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5)). Results demonstrate that the downscaled high-resolution wind data outperform ERA5 in regions of complex terrain and coastal areas, with improved overlap coefficients for wind data distributions and reduced root mean square errors (RMSEs) for hub-height and near-surface diurnal wind patterns. The downscaled simulation also captures the synoptic drivers of seasonal wind direction patterns reasonably well, indicated by high wind rose similarity indices. This study also provides an analysis of interannual variability, utilizing the dataset's full 20-year period, and model uncertainty, generated by varying model initial conditions and physics parameterizations across 1-year ensemble members, which are key considerations for wind resource assessment in wind farm development.

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Research Organization:: National Laboratory of the Rockies (NLR), Golden, CO (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office

Grant/Contract Number:: AC02-06CH11357; AC36-08GO28308

OSTI ID:: 3017178

Report Number(s):: NLR/JA--5000-99150

Journal Information:: Wind Energy Science (Online), Journal Name: Wind Energy Science (Online) Journal Issue: 1 Vol. 11; ISSN 2366-7451

Publisher:: Copernicus PublicationsCopyright Statement

Country of Publication:: United States

Language:: English

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