Articles | Volume 16, issue 4
https://doi.org/10.5194/essd-16-1965-2024
https://doi.org/10.5194/essd-16-1965-2024
Data description paper
 | 
26 Apr 2024
Data description paper |  | 26 Apr 2024

The 2023 National Offshore Wind data set (NOW-23)

Nicola Bodini, Mike Optis, Stephanie Redfern, David Rosencrans, Alex Rybchuk, Julie K. Lundquist, Vincent Pronk, Simon Castagneri, Avi Purkayastha, Caroline Draxl, Raghavendra Krishnamurthy, Ethan Young, Billy Roberts, Evan Rosenlieb, and Walter Musial

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Cited articles

Beiter, P., Musial, W., Duffy, P., Cooperman, A., Shields, M., Heimiller, D., and Optis, M.: The cost of floating offshore wind energy in California between 2019 and 2032, Tech. Rep., National Renewable Energy Lab.(NREL), Golden, CO, United States, https://doi.org/10.2172/1710181, 2020. a
Bodini, N., Optis, M., Rossol, M., Rybchuk, A., Redfern, S., Lundquist, J. K., and Rosencrans, D.: 2023 National Offshore Wind data set (NOW-23), EDI [data set], https://doi.org/10.25984/1821404, 2020. a, b, c
Bodini, N., Hu, W., Optis, M., Cervone, G., and Alessandrini, S.: Assessing boundary condition and parametric uncertainty in numerical-weather-prediction-modeled, long-term offshore wind speed through machine learning and analog ensemble, Wind Energ. Sci., 6, 1363–1377, https://doi.org/10.5194/wes-6-1363-2021, 2021. a
Bodini, N., Rybchuk, A., Optis, M., Musial, W., Lundquist, J. K., Redfern, S., Draxl, C., Krishnamurthy, R., and Gaudet, B.: Update on NREL's 2020 Offshore Wind Resource Assessment for the California Pacific Outer Continental Shelf, Tech. Rep., National Renewable Energy Lab.(NREL), Golden, CO, United States, https://doi.org/10.2172/1899984, 2022. a
Bodini, N., Castagneri, S., and Optis, M.: Long-term uncertainty quantification in WRF-modeled offshore wind resource off the US Atlantic coast, Wind Energ. Sci., 8, 607–620, https://doi.org/10.5194/wes-8-607-2023, 2023. a
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This article presents the 2023 National Offshore Wind data set (NOW-23), an updated resource for offshore wind information in the US. It replaces the Wind Integration National Dataset (WIND) Toolkit, offering improved accuracy through advanced weather prediction models. The data underwent regional tuning and validation and can be accessed at no cost.
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