Articles | Volume 7, issue 5
https://doi.org/10.5194/wes-7-2059-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-7-2059-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Oct 2022
Research article |
| 19 Oct 2022
| 19 Oct 2022
Offshore reanalysis wind speed assessment across the wind turbine rotor layer off the United States Pacific coast
Lindsay M. Sheridan
CORRESPONDING AUTHOR
Pacific Northwest National Laboratory, Richland, WA, USA
Raghu Krishnamurthy
Pacific Northwest National Laboratory, Richland, WA, USA
Gabriel García Medina
Pacific Northwest National Laboratory, Richland, WA, USA
Brian J. Gaudet
Pacific Northwest National Laboratory, Richland, WA, USA
William I. Gustafson Jr.
Pacific Northwest National Laboratory, Richland, WA, USA
Alicia M. Mahon
Pacific Northwest National Laboratory, Richland, WA, USA
William J. Shaw
Pacific Northwest National Laboratory, Richland, WA, USA
Rob K. Newsom
Pacific Northwest National Laboratory, Richland, WA, USA
Mikhail Pekour
Pacific Northwest National Laboratory, Richland, WA, USA
Zhaoqing Yang
Pacific Northwest National Laboratory, Richland, WA, USA
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Turbulence intensity is critical for wind turbine design and operation as it affects wind power generation efficiency. Turbulence measurements in the marine environment are limited. We use a model to derive turbulence intensity and test how sea surface temperature data may impact the simulated turbulence intensity and atmospheric stability. The model slightly underestimates turbulence, and improved sea surface temperature data reduce the bias. Error with unrealistic mesoscale flow is identified.
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This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.
Fan Mei, Mikhail S. Pekour, Darielle Dexheimer, Gijs de Boer, RaeAnn Cook, Jason Tomlinson, Beat Schmid, Lexie A. Goldberger, Rob Newsom, and Jerome D. Fast
Earth Syst. Sci. Data, 14, 3423–3438, https://doi.org/10.5194/essd-14-3423-2022, https://doi.org/10.5194/essd-14-3423-2022, 2022
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This work focuses on an expanding number of data sets observed using ARM TBS (133 flights) and UAS (seven flights) platforms by the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility. These data streams provide new perspectives on spatial variability of atmospheric and surface parameters, helping to address critical science questions in Earth system science research, such as the aerosol–cloud interaction in the boundary layer.
Sagar P. Parajuli, Georgiy L. Stenchikov, Alexander Ukhov, Suleiman Mostamandi, Paul A. Kucera, Duncan Axisa, William I. Gustafson Jr., and Yannian Zhu
Atmos. Chem. Phys., 22, 8659–8682, https://doi.org/10.5194/acp-22-8659-2022, https://doi.org/10.5194/acp-22-8659-2022, 2022
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Rainfall affects the distribution of surface- and groundwater resources, which are constantly declining over the Middle East and North Africa (MENA) due to overexploitation. Here, we explored the effects of dust on rainfall using WRF-Chem model simulations. Although dust is considered a nuisance from an air quality perspective, our results highlight the positive fundamental role of dust particles in modulating rainfall formation and distribution, which has implications for cloud seeding.
Caleb Phillips, Lindsay M. Sheridan, Patrick Conry, Dimitrios K. Fytanidis, Dmitry Duplyakin, Sagi Zisman, Nicolas Duboc, Matt Nelson, Rao Kotamarthi, Rod Linn, Marc Broersma, Timo Spijkerboer, and Heidi Tinnesand
Wind Energ. Sci., 7, 1153–1169, https://doi.org/10.5194/wes-7-1153-2022, https://doi.org/10.5194/wes-7-1153-2022, 2022
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Adoption of distributed wind turbines for energy generation is hindered by challenges associated with siting and accurate estimation of the wind resource. This study evaluates classic and commonly used methods alongside new state-of-the-art models derived from simulations and machine learning approaches using a large dataset from the Netherlands. We find that data-driven methods are most effective at predicting production at real sites and new models reliably outperform classic methods.
Lindsay M. Sheridan, Caleb Phillips, Alice C. Orrell, Larry K. Berg, Heidi Tinnesand, Raj K. Rai, Sagi Zisman, Dmitry Duplyakin, and Julia E. Flaherty
Wind Energ. Sci., 7, 659–676, https://doi.org/10.5194/wes-7-659-2022, https://doi.org/10.5194/wes-7-659-2022, 2022
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The small wind community relies on simplified wind models and energy production simulation tools to obtain energy generation expectations. We gathered actual wind speed and turbine production data across the US to test the accuracy of models and tools for small wind turbines. This study provides small wind installers and owners with the error metrics and sources of error associated with using models and tools to make performance estimates, empowering them to adjust expectations accordingly.
Fan Mei, Steven Spielman, Susanne Hering, Jian Wang, Mikhail S. Pekour, Gregory Lewis, Beat Schmid, Jason Tomlinson, and Maynard Havlicek
Atmos. Meas. Tech., 14, 7329–7340, https://doi.org/10.5194/amt-14-7329-2021, https://doi.org/10.5194/amt-14-7329-2021, 2021
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This study focuses on understanding a versatile water-based condensation particle counter (vWCPC 3789) performance under various ambient pressure conditions (500–1000 hPa). A vWCPC has the advantage of avoiding health and safety concerns. However, its performance characterization under low pressure is rare but crucial for ensuring successful airborne deployment. This paper provides advanced knowledge of operating a vWCPC 3789 to capture the spatial variations of atmospheric aerosols.
Raghavendra Krishnamurthy, Rob K. Newsom, Larry K. Berg, Heng Xiao, Po-Lun Ma, and David D. Turner
Atmos. Meas. Tech., 14, 4403–4424, https://doi.org/10.5194/amt-14-4403-2021, https://doi.org/10.5194/amt-14-4403-2021, 2021
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Planetary boundary layer (PBL) height is a critical parameter in atmospheric models. Continuous PBL height measurements from remote sensing measurements are important to understand various boundary layer mechanisms, especially during daytime and evening transition periods. Due to several limitations in existing methodologies to detect PBL height from a Doppler lidar, in this study, a machine learning (ML) approach is tested. The ML model is observed to improve the accuracy by over 50 %.
Maria A. Zawadowicz, Kaitlyn Suski, Jiumeng Liu, Mikhail Pekour, Jerome Fast, Fan Mei, Arthur J. Sedlacek, Stephen Springston, Yang Wang, Rahul A. Zaveri, Robert Wood, Jian Wang, and John E. Shilling
Atmos. Chem. Phys., 21, 7983–8002, https://doi.org/10.5194/acp-21-7983-2021, https://doi.org/10.5194/acp-21-7983-2021, 2021
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This paper describes the results of a recent field campaign in the eastern North Atlantic, where two mass spectrometers were deployed aboard a research aircraft to measure the chemistry of aerosols and trace gases. Very clean conditions were found, dominated by local sulfate-rich acidic aerosol and very aged organics. Evidence of
long-range transport of aerosols from the continents was also identified.
Daniel Vassallo, Raghavendra Krishnamurthy, and Harindra J. S. Fernando
Wind Energ. Sci., 6, 295–309, https://doi.org/10.5194/wes-6-295-2021, https://doi.org/10.5194/wes-6-295-2021, 2021
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Machine learning is quickly becoming a commonly used technique for wind speed and power forecasting and is especially useful when combined with other forecasting techniques. This study utilizes a popular machine learning algorithm, random forest, in an attempt to predict the forecasting error of a statistical forecasting model. Various atmospheric characteristics are used as random forest inputs in an effort to discern the most useful atmospheric information for this purpose.
Cited articles
Ao, C. O., Waliser, D. E., Chan, S. K., Li, J-L., Tian, B., Xie, F., and Mannucci, A. J.: Planetary boundary layer heights from GPS radio occultation refractivity and humidity profiles, J. Geophys. Res.-Atmos., 117, D16117, https://doi.org/10.1029/2012JD017598, 2012.
Beljaars, A. C. M. and Holtslag, A. A. M.:
Flux parameterization over land surfaces for atmospheric models, J. Appl. Meteorol. Clim., 30, 327–341, https://doi.org/10.1175/1520-0450(1991)030%3C0327:FPOLSF%3E2.0.CO;2, 1991.
Benjamin, S. G., Weygandt, S. S., Brown, J. M., Hu, M., Alexander, C. R., Smirnova, T. G., Olson, J. B., James, E. P., Dowell, D. C., Grell, G. A., Lin, H., Peckham, S. E., Smith, T. L., Moninger, W. R., Kenyon, J. S., and Manikin, G. S.: A North American hourly assimilation and model forecast cycle: The Rapid Refresh, Mon. Weather Rev., 144, 1669–1694, https://doi.org/10.1175/MWR-D-15-0242.1, 2016.
Benjamin, S. G., James, E. P., Brown, J. M., Szoke, E. J., Kenyon, J. S., and Ahmadov, R.:
Diagnostic fields developed for hourly updated NOAA weather models, NOAA Technical Memorandum OAR GSL-66, Earth Systems Research Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO, 58 pp. https://doi.org/10.25923/98fy-xx71, 2020.
Bond, N. A., Mass, C. F., and Overland, J. E.:
Coastally trapped wind reversals along the United States west coast during the warm season. Part I: Climatology and temporal evolution, Mon. Weather Rev., 124, 3, 430, 1996.
Businger, J. A., Wyngaard, J. C., Izumi, Y., and Bradley, E. F.:
Flux profile relationships in the atmospheric surface layer, J. Atmos. Sci., 28, 181–189, https://doi.org/10.1175/1520-0469(1971)028%3C0181:FPRITA%3E2.0.CO;2, 1971.
Copernicus: Climate Data Store, https://cds.climate.copernicus.eu/, last access: 3 January 2022.
de Assis Tavares, L. F., Shadham, M., de Freitas Assad, L. P., and Estefen, S. F.:
Influence of the WRF Model and Atmospheric Reanalysis on the Offshore Wind Resource Potential and Cost Estimation: A Case Study for Rio De Janeiro State, SSRN, 3895673, https://doi.org/10.2139/ssrn.3895673, 2021.
Donelan, M. A., Haus, B. K., Reul, N., Plant, W. J., Stiassnie, M., Graber, H. C., Brown, O. B., and Saltzman, E. S.: On the limiting aerodynamic roughness of the ocean in very strong winds, Geophys. Res. Lett., 13, L18306, https://doi.org/10.1029/2004GL019460, 2004.
Dong, C., Huang, G., and Cheng, G.:
Offshore wind can power Canada, Energy, 236, 121422, https://doi.org/10.1016/j.energy.2021.121422, 2021.
Dorman, C. E.:
Evidence of Kelvin Waves in California's Marine Layer and Related Eddy Generation, Mon. Weather Rev., 113, 827–839, https://doi.org/10.1175/1520-0493(1985)113%3C0827:EOKWIC%3E2.0.CO;2, 1985.
Dorman, C. E.:
Possible role of gravity currents in northern California's coastal summer wind reversals, J. Geophys. Res.-Oceans, 92, 1497–1506, https://doi.org/10.1029/JC092iC02p01497, 1987.
Dorman, C. E. and Winant, C. D.: Buoy observations of the atmosphere along the west coast of the United States, 1981–1990, J. Geophys. Res.-Oceans, 100, 16029–16044, https://doi.org/10.1029/95JC00964, 1995.
Dorman, C. E., Rogers, D. P., Nuss, W., and Thompson, W. T.:
Adjustment of the Summer Marine Boundary Layer around Point Sur, California, Mon. Weather Rev., 127, 2143–2159, https://doi.org/10.1175/1520-0493(1999)127%3C2143:AOTSMB%3E2.0.CO;2, 1999.
Dyer, A. J.:
A review of flux-profile relationships, Bound.-Lay. Meteorol., 7, 363–372, 1974.
Edson, J. B., Jampana, V., Weller, R. A., Bigorre, S. P., Plueddemann, A. J., Fairall, C. W., Miller, S. D., Mahrt, L., Vickers, D., and Hersbach, H.:
On the exchange of momentum over the open ocean, J. Phys. Oceanogr., 43, 1589–1610, https://doi.org/10.1175/JPO-D-12-0173.1, 2013.
Fairall, C. W., Bradley, E. F., Rogers, D. P., Edson, J. B., and Young, G. S.: Bulk parameterization of air–sea fluxes for tropical ocean–global atmosphere coupled-ocean atmosphere response experiment, J. Geophys. Res.-Oceans, 101, 3747–3764, https://doi.org/10.1029/95JC03205, 1996.
Fernandes, I. G., Pimenta, F. M., Saavedra, O. R., and Silva, A. R.:
Offshore Validation of ERA5 Reanalysis with Hub Height Wind Observations of Brazil, 2021 IEEE PES Innovative Smart Grid Technologies Conference – Latin America (ISGT Latin America), 15–17 September 2021, Virtual, 1–5, https://doi.org/10.1109/ISGTLatinAmerica52371.2021.9542993, 2021.
Gaudet, B. J., García Medina, G., Krishnamurthy, R., Shaw, W. J., Sheridan, L. M., Yang, Z., Newsom, R. K., and Pekour, M.:
Evaluation of coupled wind / wave model simulations of offshore winds in the Mid-Atlantic Bight using lidar-equipped buoys, Mon. Weather Rev., 150, 1377–1395, https://doi.org/10.1175/MWR-D-21-0166.1, 2022.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putnam, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.:
The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Gorton, A. M. and Shaw, W. J.:
Advancing Offshore Wind Resource Characterization Using Buoy-Based Observations, Mar. Technol. Soc. J., 54, 37–43, https://doi.org/10.4031/MTSJ.54.6.5, 2020.
Grachev, A. A. and Fairall, C. W.:
Dependence of the Monin-Obukhov Stability Parameter on the Bulk Richardson Number over the Ocean, J. Appl. Meteorol. Clim., 36, 406–414, https://doi.org/10.1175/1520-0450(1997)036%3C0406:DOTMOS%3E2.0.CO;2, 1997.
Guzman-Morales, J., Gershunov, A., Theiss, J., Li, H., and Cayan, D.:
Santa Ana Winds of Southern California: Their climatology, extremes, and behavior spanning six and a half decades, Geophys. Res. Lett., 43, 2827–2834, https://doi.org/10.1002/2016GL067887, 2016.
Haack, T. and Burk, S. D.: Summertime Marine Refractivity Conditions along Coastal California, J. Appl. Meteorol. Clim., 40, 673–687, https://doi.org/10.1175/1520-0450(2001)040%3C0673:SMRCAC%3E2.0.CO;2, 2001.
Haus, B. K., Ortiz-Suslow, D. G., Doyle, J. D., Flagg, D. D., Graber, H. C., MacMahan, J., Shen, L., Wang, Q., Williams, N. J., and Yardim, C.:
CLASI: Coordinating innovative observations and modeling to improve coastal environmental prediction systems, B. Am. Meteorol. Soc., https://doi.org/10.1175/BAMS-D-20-0304.1, in press, 2022.
Hayes, L., Stocks, M., and Blakers, A.:
Accurate long-term power generation model for offshore wind farms in Europe using ERA5 reanalsysis, Energy, 229, 120603, https://doi.org/10.1016/j.energy.2021.120603, 2021.
Helfand, H. M. and Schubert, S. D.:
Climatology of the simulated great plains low-level jet and its contribution to the continental moisture budget of the United States, J. Climate, 8, 784–806, https://doi.org/10.1175/1520- 0442(1995)008<0784:COTSGP>2.0.CO;2, 1995.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.:
The ERA5 Global Reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Jani, H. K., Nagababu, G., Patel, R. P., and Kachhwaha, S. S.: Evaluation of meteorological and reanalysis wind data for the offshore wind resource assessment, 12th International Conference on Thermal Engineering: Theory and Applications, 23–26 February 2019, Gandhinagar, India, https://journals.library.ryerson.ca/index.php/ictea/article/download/1125/1268 (last access: 17 October 2022), 2019.
Juliano, T. W., Parish, T. R., Rahn, D. A., and Leon, D. C.:
An Atmospheric Hydraulic Jump in the Santa Barbara Channel, J. Appl. Meteorol. Clim., 56, 2981–2998, https://doi.org/10.1175/JAMC-D-16-0396.1, 2017.
Koračin, D., Dorman, C. E., Lewis, J. M., Hudson, J. G., Wilcox, E. M., and Torregosa, A.:
Marine fog: A review, Atmos. Res., 143, 142–175, https://doi.org/10.1016/j.atmosres.2013.12.012, 2014.
Mesinger, F., DiMego, G., Kalnay, E., Mitchell, K., Shafran, P. C., Ebisuzaki, W., Jović, D., Woollen, J., Rogers, E., Berbery, E. H., Ek, M. B., Fan, Y., Grumbine, R., Higgins, W., Li, H., Lin, Y., Manikin, G., Parrish, D., and Shi, W.: North American Regional Reanalysis, B. Am. Meteorol. Soc., 87, 343–360, https://doi.org/10.1175/BAMS-87-3-343, 2006.
Musial, W., Beiter, P., Nunemaker, J., Heimiller, D., Ahmann, J., and Busch, J.:
Oregon Offshore Wind Site Feasibility and Cost Study, NREL/TP-5000-74597, National Renewable Energy Laboratory (NREL), Golden, CO (United States), https://doi.org/10.2172/1570430, 2019.
Musial, W., Spitsen, P., Beiter, P., Duffy, P., Marquis, M., Cooperman, A., Hammond, R., and Shields, M.:
Offshore Wind Market Report: 2021 Edition, DOE/GO-102021-5614, National Renewable Energy Laboratory (NREL), Golden, CO (United States), https://doi.org/10.2172/1818842, 2021.
NASA: Modern-Era Retrospective analysis for Research and Applications, Version 2, NASA [data set], https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/data_access/, last access: 3 January 2022.
National Weather Service: Damaging Wind Event, https://www.weather.gov/mtr/Wind_1_17-19_2021, last access: 3 January 2022.
NCAR: Research Data Archive, https://rda.ucar.edu/, last access: 3 January 2022.
NCEI: National Centers for Environmental Information, https://www.ncei.noaa.gov/, last access: 3 January 2022.
NDBC: National Oceanic and Atmospheric Administration's National Data Buoy Center, NDBC [data set], https://www.ndbc.noaa.gov/, last access: 23 November 2021.
Nehzad, M., Neshat, M., Groppi, D., Marzialetti, P., Heydari, A., Sylaios, G., and Astiaso Garcia, D.: A primary offshore wind farm site assessment using reanalysis data: a case study for Samothraki island, Renew. Energ., 172, 667–679, https://doi.org/10.1016/j.renene.2021.03.045, 2021.
Optis, M., Monahan, A., and Bosveld, F. C.:
Limitations and breakdown of Monin-Obukhov similarity theory for wind profile extrapolation under stable stratification, Wind Energy, 19, 1053–1072, https://doi.org/10.1002/we.1883, 2015.
Parish, T. R.:
Forcing of the Summertime Low-Level Jet along the California Coast, J. Appl. Meteorol. Clim., 39, 2421–2433, https://doi.org/10.1175/1520-0450(2000)039%3C2421:FOTSLL%3E2.0.CO;2, 2000.
Parish, T. R., Rahn, D. A., and Leon, D. C.: Aircraft measurements and numerical simulations of an expansion fan off the California coast, J. Appl. Meteorol. Clim., 55, 2053–2062, https://doi.org/10.1175/JAMC-D-16-0101.1, 2016.
Pronk, V., Bodini, N., Optis, M., Lundquist, J. K., Moriarty, P., Draxl, C., Purkayastha, A., and Young, E.: Can reanalysis products outperform mesoscale numerical weather prediction models in modeling the wind resource in simple terrain?, Wind Energ. Sci., 7, 487–504, https://doi.org/10.5194/wes-7-487-2022, 2022.
Ramon, J., Lledó, L., Torralba, V., Soret, A., and Doblas-Reyes, F. J.:
Which global reanalysis best represents near-surface winds?, Q. J. Roy. Meteor. Soc., 145, 3236–3251, https://doi.org/10.1002/qj.3616, 2019.
Raphael, M. N.:
The Santa Ana Winds of California, Earth Interact., 7, 1–13, https://doi.org/10.1175/1087-3562(2003)007%3C0001:TSAWOC%3E2.0.CO;2, 2003.
Ribal, A. and Young, I. R.:
33 years of globally calibrated wave height and wind speed data based on altimeter observations, Sci. Data-UK, 6, 77, https://doi.org/10.1038/s41597-019-0083-9, 2019.
Ribal, A. and Young, I. R.: Wind-Wave-Altimetry-DM00, Thredds [data set], http://thredds.aodn.org.au/thredds/catalog/IMOS/SRS/Surface-Waves/Wave-Wind-Altimetry-DM00/catalog.html, last access: 12 October 2021.
Rolinski, T., Capps, S. B., and Zhuang, W.:
Santa Ana Winds: A Descriptive Climatology, Weather Forecast., 34, 257–275, https://doi.org/10.1175/WAF-D-18-0160.1, 2019.
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y. T., Chuang, H. Y., Iredell, M., and Ek, M.: NCEP climate forecast system version 2 (CFSv2) 6-hourly products. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, 10, Dp. D61C1TXF, 2011.
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y-T., Chuang, H-Y., Iredell, M., Ek, M., Meng, J., Yang, R., Peña Mendez, M., van den Dool, H., Zhang, Q., Wang, W., Chen, M., and Becker, E.:
The NCEP Climate Forecast System Version 2, J. Climate, 27, 2185–2208, https://doi.org/10.1175/JCLI-D-12-00823.1, 2014.
Severy, M. A. Gorton, A. M., Krishnamurthy, R., and Levin, M. S.: Lidar Buoy Data Dictionary For the 2020–2021 California Deployments, PNNL-30947, Pacific Northwest National Laboratory (PNNL), Richland, WA, USA, https://a2e.energy.gov/data/buoy/buoy.z05.00/attach/pnnl-30937-datadictionary.pdf, last access: 29 November 2021.
Sheridan, L. M., Krishnamurthy, R., Gorton, A. M., Shaw, W. J., and Newsom, R. K.:
Validation of Reanalysis-Based Offshore Wind Resource Characterization Using Lidar Buoy Observation, Mar. Technol. Soc. J., 54, 44–61, https://doi.org/10.4031/MTSJ.54.6.13, 2020.
Ström, L. and Tjernström, M.:
Variability in the summertime coastal marine atmospheric boundary-layer off California, USA, Q. J. Roy. Meteor. Soc., 130, 423–448, https://doi.org/10.1256/qj.03.12, 2004.
U.S. Department of Energy: Buoy – California – Processed Data, U.S. Department of Energy [data set], https://doi.org/10.21947/1783807, 2022a.
U.S. Department of Energy: Lidar – California – Processed Data, U.S. Department of Energy [data set], https://doi.org/10.21947/1783809, 2022b.
U.S. Department of Energy: Buoy – California – Quality-Controlled Reanalysis and Observational Data – Derived Data, U.S. Department of Energy [code and data set], https://doi.org/10.21947/1839076, 2022c.
Vickers, D. and Mahrt, L.:
Observations of non-dimensional wind shear in the coastal zone, Q. J. Roy. Meteor. Soc., 125, 2685–2702, https://doi.org/10.1002/qj.49712555917, 1999.
Wang, Y-H., Walter, R. K., White, C., Farr, H., and Ruttenberg, B. I.:
Assessment of surface wind datasets for estimating offshore wind energy along the Central California Coast, Renew. Energ., 133, 343–353, https://doi.org/10.1016/j.renene.2018.10.008, 2019.
Weather Prediction Center: Western U. S. Atmospheric River Event (1/2–1/29), https://www.wpc.ncep.noaa.gov/storm_summaries/event_reviews.php?YYYYMMDD=20210129&product=snow, last access: 3 January 2022.
Wharton, S. and Lundquist, J. K.:
Atmospheric stability affects wind turbine power collection, Environ. Res. Lett., 7, 014005, https://doi.org/10.1088/1748-9326/7/1/014005/meta, 2012.
Short summary
Using observations from lidar buoys, five reanalysis and analysis models that support the wind energy community are validated offshore and at rotor-level heights along the California Pacific coast. The models are found to underestimate the observed wind resource. Occasions of large model error occur in conjunction with stable atmospheric conditions, wind speeds associated with peak turbine power production, and mischaracterization of the diurnal wind speed cycle in summer months.
Using observations from lidar buoys, five reanalysis and analysis models that support the wind...
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