Articles | Volume 8, issue 1
https://doi.org/10.5194/wes-8-1-2023
© Author(s) 2023. 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-8-1-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Jan 2023
Research article |
| 02 Jan 2023
| 02 Jan 2023
Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic
Stephanie Redfern
CORRESPONDING AUTHOR
National Renewable Energy Laboratory, Golden, Colorado 80401, USA
Mike Optis
Veer Renewables, Inc., Courtenay, BC, Canada
National Renewable Energy Laboratory, Golden, Colorado 80401, USA
Caroline Draxl
National Renewable Energy Laboratory, Golden, Colorado 80401, USA
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Before constructing wind farms we need to know how much energy they will produce. This requires knowledge of long-term wind conditions from either measurements or models. At the US East Coast there are few wind measurements and little experience with offshore wind farms. Therefore, we created a satellite-based high-resolution wind resource map to quantify spatial variations in the wind conditions over potential sites for wind farms and found larger variation than modelling suggested.
Cited articles
Aird, J. A., Barthelmie, R. J., Shepherd, T. J., and Pryor, S. C.:
Occurrence of Low-Level Jets over the Eastern U.S. Coastal Zone at Heights Relevant to Wind Energy, Energies, 15, 445, https://doi.org/10.3390/en15020445, 2022. a
Alvera-Azcárate, A., Barth, A., Rixen, M., and Beckers, J. M.:
Reconstruction of incomplete oceanographic data sets using empirical orthogonal functions: application to the Adriatic Sea surface temperature, Ocean Model., 9, 325–346, https://doi.org/10.1016/j.ocemod.2004.08.001, 2005. a
Banta, R. M., Pichugina, Y. L., Brewer, W. A., James, E. P., Olson, J. B., Benjamin, S. G., Carley, J. R., Bianco, L., Djalalova, I. V., Wilczak, J. M., Hardesty, R. M., Cline, J., and Marquis, M. C.:
Evaluating and Improving NWP Forecast Models for the Future: How the Needs of Offshore Wind Energy Can Point the Way, B. Am. Meteorol. Soc., 99, 1155–1176, https://doi.org/10.1175/BAMS-D-16-0310.1, 2018. a
Bureau of Ocean Energy Management: Outer Continental Shelf Renewable Energy Leases Map Book, https://www.boem.gov/renewable-energy/mapping-and-data/renewable-energy-gis-data
(last access: 19 December 2022), 2018. a
Byun, D., Kim, S., Cheng, F.-Y., Kim, H.-C., and Ngan, F.: Improved Modeling Inputs: Land Use and Sea-Surface Temperature, Final Report, Texas Commission on Environmental Quality, https://www.tceq.texas.gov/airquality/airmod/project/pj_report_met.html
(last access: 19 December 2022), 2007. a
Chen, F., Miao, S., Tewari, M., Bao, J.-W., and Kusaka, H.:
A numerical study of interactions between surface forcing and sea breeze circulations and their effects on stagnation in the greater Houston area, J. Geophys. Res.-Atmos., 116, D12105, https://doi.org/10.1029/2010JD015533, 2011. a, b
Chen, Z., Curchitser, E., Chant, R., and Kang, D.:
Seasonal Variability of the Cold Pool Over the Mid-Atlantic Bight Continental Shelf, J. Geophys. Res.-Oceans, 123, 8203–8226, https://doi.org/10.1029/2018JC014148, 2018. a
Chin, T. M., Vazquez-Cuervo, J., and Armstrong, E. M.:
A multi-scale high-resolution analysis of global sea surface temperature, Remote Sens. Environ., 200, 154–169, https://doi.org/10.1016/j.rse.2017.07.029, 2017. a
Colle, B. A. and Novak, D. R.:
The New York Bight Jet: Climatology and Dynamical Evolution, Mon. Weather Rev., 138, 2385–2404, https://doi.org/10.1175/2009MWR3231.1, 2010. a
Debnath, M., Doubrawa, P., Optis, M., Hawbecker, P., and Bodini, N.:
Extreme wind shear events in US offshore wind energy areas and the role of induced stratification, Wind Energ. Sci., 6, 1043–1059, https://doi.org/10.5194/wes-6-1043-2021, 2021. a, b
Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and Wimmer, W.:
The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system, Remote Sens. Environ., 116, 140–158, https://doi.org/10.1016/j.rse.2010.10.017, 2012. a, b
Dragaud, I. C. D. V., Soares da Silva, M., Assad, L. P. d. F., Cataldi, M., Landau, L., Elias, R. N., and Pimentel, L. C. G.:
The impact of SST on the wind and air temperature simulations: a case study for the coastal region of the Rio de Janeiro state, Meteorol. Atmos. Phys., 131, 1083–1097, https://doi.org/10.1007/s00703-018-0622-5, 2019. a, b
Fiedler, E. K., Mao, C., Good, S. A., Waters, J., and Martin, M. J.:
Improvements to feature resolution in the OSTIA sea surface temperature analysis using the NEMOVAR assimilation scheme, Q. J. Roy. Meteor. Soc., 145, 3609–3625, https://doi.org/10.1002/qj.3644, 2019. a
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., Putman, 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. a
Gerber, H., Chang, S., and Holt, T.:
Evolution of a Marine Boundary-Layer Jet, J. Atmos. Sci., 46, 1312–1326, https://doi.org/10.1175/1520-0469(1989)046<1312:EOAMBL>2.0.CO;2, 1989. a
Gutierrez, W., Araya, G., Kiliyanpilakkil, P., Ruiz-Columbie, A., Tutkun, M., and Castillo, L.:
Structural impact assessment of low level jets over wind turbines, J. Renew Sustain. Ener., 8, 023308, https://doi.org/10.1063/1.4945359, 2016. a
Gutierrez, W., Ruiz-Columbie, A., Tutkun, M., and Castillo, L.:
Impacts of the low-level jet's negative wind shear on the wind turbine, Wind Energ. Sci., 2, 533–545, https://doi.org/10.5194/wes-2-533-2017, 2017. a
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. a, b, c
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models, J. Geophys. Res.-Atmos., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008. a
Källstrand, B.: Low level jets in a marine boundary layer during spring, Contrib. Atmos. Phys., 71, 359–373, 1998. a
Kain, J. S. and Fritsch, J. M.: Convective parameterization for mesoscale models: The Kain–Fritsch scheme, in: The representation of cumulus convection in numerical models, American Meteorological Society, Boston, MA, 165–170, https://doi.org/10.1007/978-1-935704-13-3_16, 1993. a
Kikuchi, Y., Fukushima, M., and Ishihara, T.:
Assessment of a Coastal Offshore Wind Climate by Means of Mesoscale Model Simulations Considering High-Resolution Land Use and Sea Surface Temperature Data Sets, Atmosphere, 11, 379, https://doi.org/10.3390/atmos11040379, 2020. a, b, c
Lantz, E. J., Roberts, J. O., Nunemaker, J., DeMeo, E., Dykes, K. L., and Scott, G. N.: Increasing Wind Turbine Tower Heights: Opportunities and Challenges, United States, OSTI technical report NREL/TP-5000-73629, NREL, https://doi.org/10.2172/1515397, 2019. a, b
Li, H., Claremar, B., Wu, L., Hallgren, C., Körnich, H., Ivanell, S., and Sahlée, E.: A sensitivity study of the WRF model in offshore wind modeling over the Baltic Sea, Geosci. Front., 12, 101229, https://doi.org/10.1016/j.gsf.2021.101229, 2021. a
Lombardo, K., Sinsky, E., Edson, J., Whitney, M. M., and Jia, Y.:
Sensitivity of Offshore Surface Fluxes and Sea Breezes to the Spatial Distribution of Sea-Surface Temperature, Bound.-Lay. Meteorol., 166, 475–502, https://doi.org/10.1007/s10546-017-0313-7, 2018. a
Miller, S. T. K., Keim, B. D., Talbot, R. W., and Mao, H.: Sea breeze: Structure, forecasting, and impacts, Rev. Geophys., 41, 1011, https://doi.org/10.1029/2003RG000124, 2003. a
Murphy, P., Lundquist, J. K., and Fleming, P.:
How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine, Wind Energ. Sci., 5, 1169–1190, https://doi.org/10.5194/wes-5-1169-2020, 2020. a
Murphy, S. C., Nazzaro, L. J., Simkins, J., Oliver, M. J., Kohut, J., Crowley, M., and Miles, T. N.:
Persistent upwelling in the Mid-Atlantic Bight detected using gap-filled, high-resolution satellite SST, Remote Sens. Environ., 262, 112487, https://doi.org/10.1016/j.rse.2021.112487, 2021. a, b, c
Nakanishi, M. and Niino, H.:
An improved Mellor–Yamada level-3 model: Its numerical stability and application to a regional prediction of advection fog, Bound.-Lay. Meteorol., 119, 397–407, 2006. a
Nakanishi, M. and Niino, H.:
Development of an improved turbulence closure model for the atmospheric boundary layer, J. Meteorol. Soc. Jpn. Ser. II, 87, 895–912, 2009. a
National Oceanic and Atmospheric Administration: National Data Buoy Center, https://www.ndbc.noaa.gov (last access: 19 December 2022), 2021. a
Nunalee, C. G. and Basu, S.:
Mesoscale modeling of coastal low-level jets: implications for offshore wind resource estimation, Wind Energy, 17, 1199–1216, https://doi.org/10.1002/we.1628, 2014. a
Optis, M. and Redfern, S.: Mid-Atlantic SST namelist, Zenodo [data set], https://doi.org/10.5281/zenodo.7275214, 2022. a
Optis, M., Bodini, N., Debnath, M., and Doubrawa, P.:
Best Practices for the Validation of US Offshore Wind Resource Models, Tech. rep., Tech. Rep. NREL/TP-5000-78375, NREL – National Renewable Energy Laboratory, https://doi.org/10.2172/1755697, 2020. a
Park, R. S., Cho, Y.-K., Choi, B.-J., and Song, C. H.: Implications of sea surface temperature deviations in the prediction of wind and precipitable water over the Yellow Sea, J. Geophys. Res.-Atmos., 116, D17106, https://doi.org/10.1029/2011JD016191, 2011. a
Pichugina, Y. L., Brewer, W. A., Banta, R. M., Choukulkar, A., Clack, C. T. M., Marquis, M. C., McCarty, B. J., Weickmann, A. M., Sandberg, S. P., Marchbanks, R. D., and Hardesty, R. M.:
Properties of the offshore low level jet and rotor layer wind shear as measured by scanning Doppler Lidar, Wind Energy, 20, 987–1002, https://doi.org/10.1002/we.2075, 2017.
a
Ping, B., Su, F., and Meng, Y.: An Improved DINEOF Algorithm for Filling Missing Values in Spatio-Temporal Sea Surface Temperature Data, PLOS ONE, 11, e0155928, https://doi.org/10.1029/2011JD016191, 2016. a, b
Powers, J. G., Klemp, J. B., Skamarock, W. C., Davis, C. A., Dudhia, J., Gill, D. O., Coen, J. L., Gochis, D. J., Ahmadov, R., Peckham, S. E., Grell, G. A., Michalakes, J., Trahan, S., Benjamin, S. G., Alexander, C. R., Dimego, G. J., Wang, W., Schwartz, C. S., Romine, G. S., Liu, Z., Snyder, C., Chen, F., Barlage, M. J., Yu, W., and Duda, M. G.:
The Weather Research and Forecasting Model: Overview, System Efforts, and Future Directions, B. Am. Meteorol. Soc., 98, 1717–1737, https://doi.org/10.1175/BAMS-D-15-00308.1, 2017. a
Schmit, T. J., Li, J., Li, J., Feltz, W. F., Gurka, J. J., Goldberg, M. D., and Schrab, K. J.:
The GOES-R Advanced Baseline Imager and the Continuation of Current Sounder Products, J. Appl. Meteorol. Clim., 47, 2696–2711, https://doi.org/10.1175/2008JAMC1858.1, 2008. a
Schmit, T. J., Griffith, P., Gunshor, M. M., Daniels, J. M., Goodman, S. J., and Lebair, W. J.:
A closer look at the ABI on the GOES-R series, B. Am. Meteorol. Soc., 98, 681–698, 2017. a
Schoenberg Ferrier, B.:
A double-moment multiple-phase four-class bulk ice scheme. Part I: Description, J. Atmos. Sci., 51, 249–280, 1994. a
Shimada, S., Ohsawa, T., Kogaki, T., Steinfeld, G., and Heinemann, D.:
Effects of sea surface temperature accuracy on offshore wind resource assessment using a mesoscale model, Wind Energy, 18, 1839–1854, https://doi.org/10.1002/we.1796, 2015. a
Stark, J. D., Donlon, C. J., Martin, M. J., and McCulloch, M. E.: OSTIA: An operational, high resolution, real time, global sea surface temperature analysis system, OCEANS 2007 – Europe, 2007, 1–4, https://doi.org/10.1109/OCEANSE.2007.4302251, 2007. a, b
Stull, R. B.: Practical meteorology: an algebra-based survey of atmospheric science, University of British Columbia, 2015. a
Tewari, M., Chen, F., Wang, W., Dudhia, J., LeMone, M., Mitchell, K., Ek, M., Gayno, G., Wegiel, J., and Cuenca, R. H.: Implementation and verification of the unified NOAH land surface model in the WRF model (Formerly Paper Number 17.5), in: Vol. 14, Proceedings of the 20th Conference on Weather Analysis and Forecasting/16th Conference on Numerical Weather Prediction, Seattle, WA, USA, lgebra-based survey of atmospheric science, University of British Columbia, https://ams.confex.com/ams/84Annual/techprogram/paper_69061.htm (last access: 19 December 2022), 2004. a
Xia, G., Draxl, C., Optis, M., and Redfern, S.:
Detecting and characterizing simulated sea breezes over the US northeastern coast with implications for offshore wind energy, Wind Energ. Sci., 7, 815–829, https://doi.org/10.5194/wes-7-815-2022, 2022. a
Short summary
As wind farm developments expand offshore, accurate forecasting of winds above coastal waters is rising in importance. Weather models rely on various inputs to generate their forecasts, one of which is sea surface temperature (SST). In this study, we evaluate how the SST data set used in the Weather Research and Forecasting model may influence wind characterization and find meaningful differences between model output when different SST products are used.
As wind farm developments expand offshore, accurate forecasting of winds above coastal waters is...
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