Articles | Volume 7, issue 2
https://doi.org/10.5194/wes-7-815-2022
© 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-7-815-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Apr 2022
Research article |
| 05 Apr 2022
| 05 Apr 2022
Detecting and characterizing simulated sea breezes over the US northeastern coast with implications for offshore wind energy
National Renewable Energy Laboratory, Golden, CO 80401 USA
Caroline Draxl
National Renewable Energy Laboratory, Golden, CO 80401 USA
Renewable and Sustainable Energy Institute, Boulder, CO 80309, USA
Michael Optis
EDF Renewables, San Diego, CA 92128, USA
Stephanie Redfern
National Renewable Energy Laboratory, Golden, CO 80401 USA
Related authors
Miguel Sanchez-Gomez, Georgios Deskos, Mike Optis, Julie K. Lundquist, Michael Sinner, Geng Xia, and Walter Musial
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-152, https://doi.org/10.5194/wes-2025-152, 2025
Preprint under review for WES
Short summary
Short summary
Mesoscale WRF simulations with the Fitch wind farm parameterization were compared to large-domain LES for three planned offshore wind farms under varied atmospheric conditions. Mesoscale runs captured key wake deficit patterns and stability effects in the wind farm wake evolution, but underestimated power losses from internal wakes, especially in aligned winds or stable conditions. Results highlight mesoscale strengths for large-scale wakes and limits for turbine-level losses.
Geng Xia, Mike Optis, Georgios Deskos, Michael Sinner, Daniel Mulas Hernando, Julie Kay Lundquist, Andrew Kumler, Miguel Sanchez Gomez, Paul Fleming, and Walter Musial
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-154, https://doi.org/10.5194/wes-2025-154, 2025
Preprint under review for WES
Short summary
Short summary
This study examines energy losses from cluster wakes in offshore wind farms along the U.S. East Coast. Simulations based on real lease projects show that large wind speed deficits do not always cause equally large energy losses. The energy loss method revealed wake areas up to 30 % larger than traditional estimates, underscoring the need to consider both wind speed deficit and energy loss in planning offshore wind development.
Stephanie Redfern, Mike Optis, Geng Xia, and Caroline Draxl
Wind Energ. Sci., 8, 1–23, https://doi.org/10.5194/wes-8-1-2023, https://doi.org/10.5194/wes-8-1-2023, 2023
Short summary
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.
Caroline Draxl, Rochelle P. Worsnop, Geng Xia, Yelena Pichugina, Duli Chand, Julie K. Lundquist, Justin Sharp, Garrett Wedam, James M. Wilczak, and Larry K. Berg
Wind Energ. Sci., 6, 45–60, https://doi.org/10.5194/wes-6-45-2021, https://doi.org/10.5194/wes-6-45-2021, 2021
Short summary
Short summary
Mountain waves can create oscillations in low-level wind speeds and subsequently in the power output of wind plants. We document such oscillations by analyzing sodar and lidar observations, nacelle wind speeds, power observations, and Weather Research and Forecasting model simulations. This research describes how mountain waves form in the Columbia River basin and affect wind energy production and their impact on operational forecasting, wind plant layout, and integration of power into the grid.
Miguel Sanchez-Gomez, Georgios Deskos, Mike Optis, Julie K. Lundquist, Michael Sinner, Geng Xia, and Walter Musial
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-152, https://doi.org/10.5194/wes-2025-152, 2025
Preprint under review for WES
Short summary
Short summary
Mesoscale WRF simulations with the Fitch wind farm parameterization were compared to large-domain LES for three planned offshore wind farms under varied atmospheric conditions. Mesoscale runs captured key wake deficit patterns and stability effects in the wind farm wake evolution, but underestimated power losses from internal wakes, especially in aligned winds or stable conditions. Results highlight mesoscale strengths for large-scale wakes and limits for turbine-level losses.
Geng Xia, Mike Optis, Georgios Deskos, Michael Sinner, Daniel Mulas Hernando, Julie Kay Lundquist, Andrew Kumler, Miguel Sanchez Gomez, Paul Fleming, and Walter Musial
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-154, https://doi.org/10.5194/wes-2025-154, 2025
Preprint under review for WES
Short summary
Short summary
This study examines energy losses from cluster wakes in offshore wind farms along the U.S. East Coast. Simulations based on real lease projects show that large wind speed deficits do not always cause equally large energy losses. The energy loss method revealed wake areas up to 30 % larger than traditional estimates, underscoring the need to consider both wind speed deficit and energy loss in planning offshore wind development.
Lindsay M. Sheridan, Jiali Wang, Caroline Draxl, Nicola Bodini, Caleb Phillips, Dmitry Duplyakin, Heidi Tinnesand, Raj K. Rai, Julia E. Flaherty, Larry K. Berg, Chunyong Jung, Ethan Young, and Rao Kotamarthi
Wind Energ. Sci., 10, 1551–1574, https://doi.org/10.5194/wes-10-1551-2025, https://doi.org/10.5194/wes-10-1551-2025, 2025
Short summary
Short summary
Three recent wind resource datasets are assessed for their skills in representing annual average wind speeds and seasonal, diurnal, and interannual trends in the wind resource in coastal locations to support customers interested in small and midsize wind energy.
Kyle Peco, Jiali Wang, Chunyong Jung, Gökhan Sever, Lindsay Sheridan, Jeremy Feinstein, Rao Kotamarthi, Caroline Draxl, Ethan Young, Avi Purkayastha, and Andrew Kumler
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-13, https://doi.org/10.5194/wes-2025-13, 2025
Preprint under review for WES
Short summary
Short summary
This study presents a new wind dataset, generated by a climate model, that can help facilitate efforts in wind energy. By providing data across much of North America, this dataset can offer insights into the wind patterns in more understudied regions. By validating the dataset against actual wind observations, we have demonstrated that this dataset is able to accurately capture the wind patterns of different geographic areas, which can help identify locations for wind energy farms.
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
Earth Syst. Sci. Data, 16, 1965–2006, https://doi.org/10.5194/essd-16-1965-2024, https://doi.org/10.5194/essd-16-1965-2024, 2024
Short summary
Short summary
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.
Sue Ellen Haupt, Branko Kosović, Larry K. Berg, Colleen M. Kaul, Matthew Churchfield, Jeffrey Mirocha, Dries Allaerts, Thomas Brummet, Shannon Davis, Amy DeCastro, Susan Dettling, Caroline Draxl, David John Gagne, Patrick Hawbecker, Pankaj Jha, Timothy Juliano, William Lassman, Eliot Quon, Raj K. Rai, Michael Robinson, William Shaw, and Regis Thedin
Wind Energ. Sci., 8, 1251–1275, https://doi.org/10.5194/wes-8-1251-2023, https://doi.org/10.5194/wes-8-1251-2023, 2023
Short summary
Short summary
The Mesoscale to Microscale Coupling team, part of the U.S. Department of Energy Atmosphere to Electrons (A2e) initiative, has studied various important challenges related to coupling mesoscale models to microscale models. Lessons learned and discerned best practices are described in the context of the cases studied for the purpose of enabling further deployment of wind energy. It also points to code, assessment tools, and data for testing the methods.
Stephanie Redfern, Mike Optis, Geng Xia, and Caroline Draxl
Wind Energ. Sci., 8, 1–23, https://doi.org/10.5194/wes-8-1-2023, https://doi.org/10.5194/wes-8-1-2023, 2023
Short summary
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.
William J. Shaw, Larry K. Berg, Mithu Debnath, Georgios Deskos, Caroline Draxl, Virendra P. Ghate, Charlotte B. Hasager, Rao Kotamarthi, Jeffrey D. Mirocha, Paytsar Muradyan, William J. Pringle, David D. Turner, and James M. Wilczak
Wind Energ. Sci., 7, 2307–2334, https://doi.org/10.5194/wes-7-2307-2022, https://doi.org/10.5194/wes-7-2307-2022, 2022
Short summary
Short summary
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.
Vincent Pronk, Nicola Bodini, Mike Optis, Julie K. Lundquist, Patrick Moriarty, Caroline Draxl, Avi Purkayastha, and Ethan Young
Wind Energ. Sci., 7, 487–504, https://doi.org/10.5194/wes-7-487-2022, https://doi.org/10.5194/wes-7-487-2022, 2022
Short summary
Short summary
In this paper, we have assessed to which extent mesoscale numerical weather prediction models are more accurate than state-of-the-art reanalysis products in characterizing the wind resource at heights of interest for wind energy. The conclusions of our work will be of primary importance to the wind industry for recommending the best data sources for wind resource modeling.
Alayna Farrell, Jennifer King, Caroline Draxl, Rafael Mudafort, Nicholas Hamilton, Christopher J. Bay, Paul Fleming, and Eric Simley
Wind Energ. Sci., 6, 737–758, https://doi.org/10.5194/wes-6-737-2021, https://doi.org/10.5194/wes-6-737-2021, 2021
Short summary
Short summary
Most current wind turbine wake models struggle to accurately simulate spatially variant wind conditions at a low computational cost. In this paper, we present an adaptation of NREL's FLOw Redirection and Induction in Steady State (FLORIS) wake model, which calculates wake losses in a heterogeneous flow field using local weather measurement inputs. Two validation studies are presented where the adapted model consistently outperforms previous versions of FLORIS that simulated uniform flow only.
Caroline Draxl, Rochelle P. Worsnop, Geng Xia, Yelena Pichugina, Duli Chand, Julie K. Lundquist, Justin Sharp, Garrett Wedam, James M. Wilczak, and Larry K. Berg
Wind Energ. Sci., 6, 45–60, https://doi.org/10.5194/wes-6-45-2021, https://doi.org/10.5194/wes-6-45-2021, 2021
Short summary
Short summary
Mountain waves can create oscillations in low-level wind speeds and subsequently in the power output of wind plants. We document such oscillations by analyzing sodar and lidar observations, nacelle wind speeds, power observations, and Weather Research and Forecasting model simulations. This research describes how mountain waves form in the Columbia River basin and affect wind energy production and their impact on operational forecasting, wind plant layout, and integration of power into the grid.
Tobias Ahsbahs, Galen Maclaurin, Caroline Draxl, Christopher R. Jackson, Frank Monaldo, and Merete Badger
Wind Energ. Sci., 5, 1191–1210, https://doi.org/10.5194/wes-5-1191-2020, https://doi.org/10.5194/wes-5-1191-2020, 2020
Short summary
Short summary
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
Abbs, D. J. and Physick, W. L.: Sea-breeze observations and modelling: a review, Aust. Meteorol. Mag., 41, 7–19, 1992.
Adams, E.: Four ways to win the sea breeze game, Sailing World, March, 44–49, 1997.
Archer, C. L., Colle, B. A., Monache, L. D., Dvorak, M. J., Lundquist, J., and Bailey, B. H.: Meteorology for coastal/offshore wind energy in the United States: recommendations and research needs for the next 10 years, B. Am. Meteorol. Soc., 95, 515–519, https://doi.org/10.1175/BAMS-D-13-00108.1, 2014.
Arritt, R. W.: Effects of the large-scale flow on characteristic features of the sea breeze, J. Appl. Meteorol., 32, 116–125, https://doi.org/10.1175/1520-0450(1993)032<0116:EOTLSF>2.0.CO;2, 1993.
Azorin-Molina, C., Chen, D., Tijm, S., and Baldi, M.: A multi-year study of sea breezes in a Mediterranean coastal site: Alicante (Spain), Int. J. Climatol., 31, 468–486, https://doi.org/10.1002/joc.2064, 2011.
Bianco, L., Tomassetti, B., Coppola, E., Fracassi, A., Verdecchia, M., and Visconti, G.: Thermally driven circulation in a region of complex topography: comparison of wind-profiling radar measurements and MM5 numerical predictions, Ann. Geophys., 24, 1537–1549, https://doi.org/10.5194/angeo-24-1537-2006, 2006.
Biggs, W. G. and Graves, M. E.: A lake breeze index, J. Appl. Meteorol., 1, 474–480, https://doi.org/10.1175/1520-0450(1962)001<0474:ALBI>2.0.CO;2, 1962.
Boyle, G.: Renewable energy, Oxford University Press, Oxford, UK, 2004.
Chen, F. and Dudhia, J.: Coupling an advanced land-surface/hydrology model with the Penn State/NCAR MM5 modeling system, Part I: Model description and implementation, Mon. Weather Rev., 129, 569–585, https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2, 2001.
Costoya, X., deCastro, M., Carvalho, D., and Gómez-Gesteira, M.: On the suitability of offshore wind energy resource in the United States of America for the 21st century, Appl. Energ., 262, 114537, https://doi.org/10.1016/j.apenergy.2020.114537, 2020.
Crosman, E. T. and Horel, J. D.: Sea and lake breezes: A review of numerical studies, Bound.-Lay. Meteorol., 137, 1–29, https://doi.org/10.1007/s10546-010-9517-9, 2010.
Damato, F., Planchon, O., and Dubreuil, V.: A remote-sensing study of the inland penetration of sea-breeze fronts from the English Channel, Weather, 58, 219–226, https://doi.org/10.1256/wea.50.02, 2006.
DeepwaterWind: Block island wind farm, http://dwwind.com/project/block-island-wind-farm (last access: 13 July 2021), 2016.
DOE: Offshore Wind Workshop Addresses Industry Challenges, https://www.energy.gov/eere/wind/articles/offshore-wind-workshop-addresses-industry-challenges (last access: 29 July 2021), 2019.
Draxl, C., Clifton, A., Hodge, B., and McCaa, J.: The Wind Integration National Dataset (WIND) toolkit, Appl. Energ., 151, 355–366, https://doi.org/10.1016/j.apenergy.2015.03.121, 2015.
Esteban, M. D., Diez, J. J., López, J. S., and Negro, V.: Why offshore wind energy?, Renew. Energ., 36, 444–450, https://doi.org/10.1016/j.renene.2010.07.009, 2011.
Finkele, K., Hacker, J. M., Kraus, H., and Byronscott, R. A. D.: A complete sea-breeze circulation cell-derived from aircraft observations, Bound.-Lay. Meteorol., 73, 299–317, https://doi.org/10.1007/BF00711261, 1995.
Golding, B., Clark, P., and May, B.: The Boscastle flood: Meteorological analysis of the conditions leading to flooding on 16 August 2004, Weather, 60, 230–235, https://doi.org/10.1002/joc.3370150706, 2005.
Gustavsson, T., Lindqvist, S., Borne, K., and Bogren, J.: A study of sea and land breezes in an archipelago on the west coast of Sweden, Int. J. Climatol., 15, 785–800, https://doi.org/10.1002/joc.3370150706, 1995.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz Sabater, J., Nicolas, J., Peubey, C., Schepers, D., Simmons, A.,
Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G.,
Bidlot, J., Bonavita, M., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, 2020.
Hu, X. M. and Xue, M.: Influence of synoptic sea-breeze fronts on the urban heat island intensity in dallas – fort worth Tex, Mon. Weather Rev., 144, 1487–1507, https://doi.org/10.1175/MWR-D-15-0201.1, 2016.
Hunt, J. C. R., Orr, A., Rottman, J. W., and Capon, R.: Coriolis effects in mesoscale flows with sharp changes in surface conditions, Q. J. Roy. Meteor. Soc., 130, 2703–2731, https://doi.org/10.1256/qj.04.14, 2004.
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., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008.
Kain, J. S.: The Kain–Fritsch convective parameterization: An update, J. Appl. Meteorol., 43, 170–181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2, 2004.
Kain, J. S. and Fritsch, J. M.: A one-dimensional entraining/ detraining plume model and its application in convective parameterization, J. Atmos. Sci., 47, 2784–2802, https://doi.org/10.1175/1520-0469(1990)047<2784:AODEPM>2.0.CO;2, 1990.
Laird, N. F., Kristovich, D. A. R., Liang, X-Z, Arritt, R. W., and Labas, K.: Lake Michigan lake breezes: climatology, local forcing, and synoptic environment, J. Appl. Meteorol., 40, 409–424, https://doi.org/10.1175/1520-0450(2001)040<0409:LMLBCL>2.0.CO;2, 2001.
Lombardo, K., Sinsky, E., Jia, Y., Whitney, M. M., and Edson, J: Sensitivity of simulated sea breezes to initial conditions in complex coastal regions, Mon. Weather Rev., 30, 1299–1320, https://doi.org/10.1175/MWR-D-15-0306.1, 2016.
Lyons, W. A.: The climatology and prediction of the Chicago lake breeze, J. Appl. Meteorol., 11, 1259–1270, https://doi.org/10.1175/1520-0450(1972)011<1259:TCAPOT>2.0.CO;2, 1972.
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.
Moore, G. W. K. and Renfrew, I. A.: Tip jets and barrier winds: A QuikSCAT climatology of high wind speed events around Greenland, J. Climate, 18, 3713–3725, https://doi.org/10.1175/JCLI3455.1, 2005.
Musial, W., Heimiller, D., Beiter, P., Scott, G., and Draxl, C.: offshore wind energy resource assessment for the United States, Tech. Rep., NREL/TP-5000-66599; September 2016, https://doi.org/10.2172/1324533, 2016.
Nakanishi, M. and Niino, H.: An improved Mellor–Yamada level 3 model: its numerical stability and application to a regional prediction of advecting fog, Bound.-Lay. Meteorol., 119, 397–407, https://doi.org/10.1007/s10546-005-9030-8, 2006.
Nakanishi, M. and Niino, H.: Development of an improved turbulence closure model for the atmospheric boundary layer, J. Meteorol. Soc. Jpn., 87, 895–912, https://doi.org/10.2151/jmsj.87.895, 2009.
Optis, M., Rybchuk, A., Bodini, N., Rossol, M., and Musial, W.: Offshore Wind Resource Assessment for the California Pacific Outer Continental Shelf, Golden, CO, National Renewable Energy Laboratory, NREL/TP-5000-77642, https://www.nrel.gov/docs/fy21osti/77642.pdf (last access: 12 July 2021), 2020.
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.
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.
Prtenjak, M. T. and Grisogono, B.: Sea/land breeze climatological characteristics along the northern Croatian Adriatic coast, Theor. Appl. Climatol., 91, 1–15, https://doi.org/10.1007/s00704-006-0286-9, 2007.
Redfern, S., Optis, M., Xia, G., and Draxl, C.: Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2021-150, in review, 2021.
Ryznar, E. and Touma, J. S.: Characteristics of true lake breezes along the eastern shore of Lake Michigan, Atmos. Environ., 15, 1201–1205, https://doi.org/10.1016/0004-6981(81)90311-5, 1981.
Schwartz, M., Heimiller, D., Haymes, S., and Musial, W.: Assessment of offshore wind energy resource for the United States, Tech. Rep., NREL/TP-500-45889, https://doi.org/10.2172/983415, 2010.
Simpson, J. E.: Sea Breeze and Local Winds, Cambridge University Press, Cambridge, UK, 1994.
Skamarock, W. C. and Klemp, J. B.: A time-split non-hydrostatic atmospheric model for weather research and forecasting applications, J. Comput. Phys., 227, 3465–3485, https://doi.org/10.1016/j.jcp.2007.01.037, 2008.
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 07 IEEE Aberdeen, conference proceedings Marine challenges: coastline to deep sea, Aberdeen, Scotland, https://doi.org/10.1109/OCEANSE.2007.4302251, 2007.
Steele, C. J., Dorling, S. R., von Glasow, R., and Bacon, J.: Idealized WRF model sensitivity simulations of sea breeze types and their effects on offshore windfields, Atmos. Chem. Phys., 13, 443–461, https://doi.org/10.5194/acp-13-443-2013, 2013.
Steele, C. J., Dorling, S. R., von Glasowa, R., and Bacon, J.: Modelling sea-breeze climatologies and interactions on coasts in the southern North Sea: Implications for offshore wind energy, Q. J. Roy. Meteor. Soc., 141, 1821–1835, https://doi.org/10.1002/qj.2484, 2015.
Tao, W. K., Simpson, J., Baker, D., Braun, S., Chou, M. D., Ferrier, B., Johnson, D., Khain, A., Lang, S., Lynn, B., Shie, C. L., Starr, D., Sui, C. H., Wang, Y., and Wetzel, P.: Microphysics, radiation and surface processes in the Goddard Cumulus Ensemble (GCE) model, Meteorol. Atmos. Phys., 82, 97–137, 2003.
van Hoof, J.: Unlocking Europe's offshore wind potential, Tech. Rep., PricewaterhouseCoopers B. V., https://www.pwc.nl/nl/assets/documents/pwc-unlocking-europes-offshore-wind-potential.pdf
(last access: 12 July 2021), 2017.
Yerramilli, A., Srinivas, C. V., Dasari, H. P., Tuluri, F., White, L. D., Baham, J. M., Young, J. H., Hughes, R., Patrick, C., Hardy, M. G., and Swanier, S. J.: Simulation of atmospheric dispersion of elevated releases from point sources in Mississippi Gulf coast with different meteorological data, Int. J. Environ. Res. Pub. He., 6, 1055–1074, https://doi.org/10.3390/ijerph6031055, 2009.
Short summary
In this study, we propose a new method to detect sea breeze events from the Weather Research and Forecasting simulation. Our results suggest that the method can identify the three different types of sea breezes in the model simulation. In addition, the coastal impact, seasonal distribution and offshore wind potential associated with each type of sea breeze differ significantly, highlighting the importance of identifying the correct type of sea breeze in numerical weather/wind energy forecasting.
In this study, we propose a new method to detect sea breeze events from the Weather Research and...
Altmetrics
Final-revised paper
Preprint