Evaluation of Coupled Wind–Wave Model Simulations of Offshore Winds in the Mid-Atlantic Bight Using Lidar-Equipped Buoys

Brian J. Gaudet aPacific Northwest National Laboratory, Richland, Washington

Search for other papers by Brian J. Gaudet in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-9955-1501
,
G. García Medina bPacific Northwest National Laboratory, Seattle, Washington

Search for other papers by G. García Medina in
Current site
Google Scholar
PubMed
Close
,
R. Krishnamurthy aPacific Northwest National Laboratory, Richland, Washington

Search for other papers by R. Krishnamurthy in
Current site
Google Scholar
PubMed
Close
,
W. J. Shaw aPacific Northwest National Laboratory, Richland, Washington

Search for other papers by W. J. Shaw in
Current site
Google Scholar
PubMed
Close
,
L. M. Sheridan aPacific Northwest National Laboratory, Richland, Washington

Search for other papers by L. M. Sheridan in
Current site
Google Scholar
PubMed
Close
,
Z. Yang bPacific Northwest National Laboratory, Seattle, Washington
cUniversity of Washington, Seattle, Washington

Search for other papers by Z. Yang in
Current site
Google Scholar
PubMed
Close
,
R. K. Newsom aPacific Northwest National Laboratory, Richland, Washington

Search for other papers by R. K. Newsom in
Current site
Google Scholar
PubMed
Close
, and
M. Pekour aPacific Northwest National Laboratory, Richland, Washington

Search for other papers by M. Pekour in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

From 2014 to 2017, two Department of Energy buoys equipped with Doppler lidar were deployed off the U.S. East Coast to provide long-term measurements of hub-height wind speed in the marine environment. We performed simulations of selected cases from the deployment using a 5-km configuration of the Weather Research and Forecasting (WRF) Model, to see if simulated hub-height speeds could produce closer agreement with the observations than existing reanalysis products. For each case we performed two additional simulations: one in which marine surface roughness height was one-way coupled to forecast wave parameters from a stand-alone WaveWatch III (WW3) simulation, and another in which WRF and WW3 were two-way coupled using the Coupled Ocean–Atmosphere–Wave–Sediment–Transport (COAWST) framework. It was found that all the 5-km WRF simulations improved 90-m wind speed statistics for the tropical cyclone case of 8 May 2015 and the cold frontal case of 25 March 2016, but not the nor’easter of 18 January 2016. The impact of wave coupling on buoy-level (4 m) wind speed was modest and case dependent, but when present, the impact was typically seen at 90 m as well, being as large as 10% in stable conditions. One-way wave coupling consistently reduced wind speeds, improving biases for 25 March 2016 but worsening them for 8 May 2015. Two-way wave coupling mitigated these negative biases, improved wave field representation and statistics, and mostly improved 4-m wind field correlation coefficients, at least at the Virginia buoy, largely due to greater self-consistency between wind and wave fields.

Significance Statement

Using atmospheric models to forecast winds in the environments of offshore wind turbines will be critical in the new energy economy. The models used are imperfect, however, being sometimes too coarse, and may not properly represent the wind field at typical turbine hub heights of 90 m, for which we have limited observations in the marine environment. To help address this gap, two buoys equipped with lidars that measured hub-height winds continuously were deployed off the U.S. East Coast from 2014 to 2017. We used the lidar buoy data to show the benefits of a relatively high-resolution atmospheric model over existing reanalysis products, as well as including both the impacts of waves on winds and vice versa.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Brian J. Gaudet, brian.gaudet@pnnl.gov

Abstract

From 2014 to 2017, two Department of Energy buoys equipped with Doppler lidar were deployed off the U.S. East Coast to provide long-term measurements of hub-height wind speed in the marine environment. We performed simulations of selected cases from the deployment using a 5-km configuration of the Weather Research and Forecasting (WRF) Model, to see if simulated hub-height speeds could produce closer agreement with the observations than existing reanalysis products. For each case we performed two additional simulations: one in which marine surface roughness height was one-way coupled to forecast wave parameters from a stand-alone WaveWatch III (WW3) simulation, and another in which WRF and WW3 were two-way coupled using the Coupled Ocean–Atmosphere–Wave–Sediment–Transport (COAWST) framework. It was found that all the 5-km WRF simulations improved 90-m wind speed statistics for the tropical cyclone case of 8 May 2015 and the cold frontal case of 25 March 2016, but not the nor’easter of 18 January 2016. The impact of wave coupling on buoy-level (4 m) wind speed was modest and case dependent, but when present, the impact was typically seen at 90 m as well, being as large as 10% in stable conditions. One-way wave coupling consistently reduced wind speeds, improving biases for 25 March 2016 but worsening them for 8 May 2015. Two-way wave coupling mitigated these negative biases, improved wave field representation and statistics, and mostly improved 4-m wind field correlation coefficients, at least at the Virginia buoy, largely due to greater self-consistency between wind and wave fields.

Significance Statement

Using atmospheric models to forecast winds in the environments of offshore wind turbines will be critical in the new energy economy. The models used are imperfect, however, being sometimes too coarse, and may not properly represent the wind field at typical turbine hub heights of 90 m, for which we have limited observations in the marine environment. To help address this gap, two buoys equipped with lidars that measured hub-height winds continuously were deployed off the U.S. East Coast from 2014 to 2017. We used the lidar buoy data to show the benefits of a relatively high-resolution atmospheric model over existing reanalysis products, as well as including both the impacts of waves on winds and vice versa.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Brian J. Gaudet, brian.gaudet@pnnl.gov
Save
  • Alves, J. M. R., A. Peliz, R. M. A. Caldeira, and P. M. A. Miranda, 2018: Atmosphere-ocean feedbacks in a coastal upwelling system. Ocean Modell., 123, 5565, https://doi.org/10.1016/j.ocemod.2018.01.004.

    • Search Google Scholar
    • Export Citation
  • Archer, C. L., H. P. Simão, W. Kempton, W. B. Powell, and M. J. Dvorak, 2017: The challenge of integrating offshore wind power in the U.S. electric grid. Part I: Wind forecast error. Renew. Energy, 103, 346360, https://doi.org/10.1016/j.renene.2016.11.047.

    • Search Google Scholar
    • Export Citation
  • Ardhuin, F., and Coauthors, 2010: Semiempirical dissipation source functions for ocean waves. Part I: Definition, calibration, and validation. J. Phys. Oceanogr., 40, 19171941, https://doi.org/10.1175/2010JPO4324.1.

    • Search Google Scholar
    • Export Citation
  • Ardhuin, F., S. T. Gille, D. Menemenlis, C. B. Rocha, N. Rascle, B. Chapron, J. Gula, and J. Molemaker, 2017: Small-scale open ocean currents have large effects on wind wave heights. J. Geophys. Res. Oceans, 122, 45004517, https://doi.org/10.1002/2016JC012413.

    • Search Google Scholar
    • Export Citation
  • Badger, M., A. Peña, A. N. Hahmann, A. A. Mouche, and C. B. Hasager, 2016: Extrapolating satellite winds to turbine operating heights. J. Appl. Meteor. Climatol., 55, 975991, https://doi.org/10.1175/JAMC-D-15-0197.1.

    • Search Google Scholar
    • Export Citation
  • Battjes, J. A., and J. P. F. M. Janssen, 1978: Energy loss and set-up due to breaking of random waves. 16th Int. Conf. on Coastal Engineering, Hamburg, Germany, ASCE, 56987.

    • Search Google Scholar
    • Export Citation
  • Beljaars, A. C. M., and A. A. M. Holtslag, 1991: Flux parameterization over land surfaces for atmospheric models. J. Appl. Meteor., 30, 327341, https://doi.org/10.1175/1520-0450(1991)030<0327:FPOLSF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Benjamin, S. G., and Coauthors, 2016: A North American hourly assimilation and model forecast cycle: The Rapid Refresh. Mon. Wea. Rev., 144, 16691694, https://doi.org/10.1175/MWR-D-15-0242.1.

    • Search Google Scholar
    • Export Citation
  • Businger, J. A., J. C. Wyngaard, Y. Izumi, and E. F. Bradley, 1971: Flux–profile relationships in the atmospheric surface layer. J. Atmos. Sci., 28, 181189, https://doi.org/10.1175/1520-0469(1971)028<0181:FPRITA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Carniel, S., A. Benetazzo, D. Bonaldo, F. M. Falcieri, M. M. Miglietta, A. Ricchi, and M. Sclavo, 2016: Scratching beneath the surface while coupling atmosphere, ocean and waves: Analysis of a dense water formation event. Ocean Modell., 101, 101112, https://doi.org/10.1016/j.ocemod.2016.03.007.

    • Search Google Scholar
    • Export Citation
  • Cavaleri, L., and P. Malanotte-Rizzoli, 1981: Wind wave prediction in shallow water: Theory and applications. J. Geophys. Res., 86, 10 96110 973, https://doi.org/10.1029/JC086iC11p10961.

    • Search Google Scholar
    • Export Citation
  • Charnock, H., 1981: Wind stress on a water surface. Quart. J. Roy. Meteor. Soc., 81, 639640, https://doi.org/10.1002/qj.49708135027.

  • Doubrawa, P., R. J. Barthelmie, S. C. Pryor, C. B. Hasager, M. Badger, and I. Karagali, 2015: Satellite winds as a tool for offshore wind resource assessment: The Great Lakes Wind Atlas. Remote Sens. Environ., 168, 349359, https://doi.org/10.1016/j.rse.2015.07.008.

    • Search Google Scholar
    • Export Citation
  • Drennan, W. M., P. K. Taylor, and M. J. Yelland, 2005: Parameterizing the sea surface roughness. J. Phys. Oceanogr., 35, 835848, https://doi.org/10.1175/JPO2704.1.

    • Search Google Scholar
    • Export Citation
  • Edson, J. B., and C. W. Fairall, 1998: Similarity relationships in the marine atmospheric surface layer for terms in the TKE and scalar variance budgets. J. Atmos. Sci., 55, 23112328, https://doi.org/10.1175/1520-0469(1998)055<2311:SRITMA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Edson, J. B., and Coauthors, 2013: On the exchange of momentum over the open ocean. J. Phys. Oceanogr., 43, 15891610, https://doi.org/10.1175/JPO-D-12-0173.1.

    • Search Google Scholar
    • Export Citation
  • Fairall, C. W., E. F. Bradley, J. E. Hare, A. A. Grachev, and J. B. Edson, 2003: Bulk parameterization of air–sea fluxes: Updates and verification for the COARE algorithm. J. Climate, 16, 571591, https://doi.org/10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • García Medina, G., W. J. Shaw, Z. Yang, and R. K. Newsom, 2020: Mid-Atlantic Bight wave hindcast to support DOE lidar buoy deployments: Model validation. Tech. Rep. PNNL-29814, PNNL, https://doi.org/10.2172/1635751.

    • Search Google Scholar
    • Export Citation
  • Gelaro, R., and Coauthors, 2017: The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Climate, 30, 54195454, https://doi.org/10.1175/JCLI-D-16-0758.1.

    • Search Google Scholar
    • Export Citation
  • Grachev, A. A., C. W. Fairall, and E. F. Bradley, 2000: Convective profile constants revisited. Bound.-Layer Meteor., 94, 495515, https://doi.org/10.1023/A:1002452529672.

    • Search Google Scholar
    • Export Citation
  • Halliwell, G. R., 2004: Evaluation of vertical coordinate and vertical mixing algorithms in the HYbrid-Coordinate Ocean Model (HYCOM). Ocean Modell., 7, 285322, https://doi.org/10.1016/j.ocemod.2003.10.002.

    • Search Google Scholar
    • Export Citation
  • Hasager, C. B., and Coauthors, 2015: Offshore wind climatology based on synergetic use of Envisat ASAR, ASCAT and QuikSCAT. Remote Sens. Environ., 156, 247263, https://doi.org/10.1016/j.rse.2014.09.030.

    • Search Google Scholar
    • Export Citation
  • Hasager, C. B., A. N. Hahmann, T. Ahsbahs, I. Karagali, T. Sile, M. Badger, and J. Mann, 2020: Europe’s offshore winds assessed with synthetic aperture radar, ASCAT and WRF. Wind Energy Sci., 5, 375390, https://doi.org/10.5194/wes-5-375-2020.

    • Search Google Scholar
    • Export Citation
  • Hasselmann, K., and Coauthors, 1973: Measurements of wind-wave growth and swell decay during the Joint North Sea Wave Project (JONSWAP). Ergaenzung. Z. Dtsch. Hydrogr. Z. A, 12, 195.

    • Search Google Scholar
    • Export Citation
  • Hasselmann, S., K. Hasselmann, J. H. Allender, and T. P. Barnett, 1985: Computations and parameterizations of the nonlinear energy transfer in a gravity-wave spectrum. Part II: Parameterizations of the nonlinear energy transfer for application in wave models. J. Phys. Oceanogr., 15, 13781391, https://doi.org/10.1175/1520-0485(1985)015<1378:CAPOTN>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 19992049, https://doi.org/10.1002/qj.3803.

    • Search Google Scholar
    • Export Citation
  • Jiménez, P. A., and J. Dudhia, 2018: On the need to modify the sea surface roughness formulation over shallow waters. J. Appl. Meteor. Climatol., 57, 11011110, https://doi.org/10.1175/JAMC-D-17-0137.1.

    • Search Google Scholar
    • Export Citation
  • Jones, P. W., 1998: A user’s guide for SCRIP: A Spherical Coordinate Remapping and Interpolation Package, v1.4. Los Alamos National Laboratory, 27 pp.

    • Search Google Scholar
    • Export Citation
  • Karagali, I., A. N. Hahmann, M. Badger, C. Hasager, and J. Mann, 2018: New European wind atlas offshore. J. Phys.: Conf. Ser., 1037, 052007, https://doi:10.1088/1742-6596/1037/5/052007.

    • Search Google Scholar
    • Export Citation
  • Lundquist, J. K., and Coauthors, 2017: Assessing state-of-the-art capabilities for probing the atmospheric boundary layer: The XPIA field campaign. Bull. Amer. Meteor. Soc., 98, 289314, https://doi.org/10.1175/BAMS-D-15-00151.1.

    • Search Google Scholar
    • Export Citation
  • Nelson, J., and R. He, 2012: Effect of the Gulf Stream on winter extratropical cyclone outbreaks. Atmos. Sci. Lett., 13, 311316, https://doi.org/10.1002/asl.400.

    • Search Google Scholar
    • Export Citation
  • Nelson, J., R. He, J. C. Warner, and J. Bane, 2014: Air-sea interactions during strong winter extratropical storms. Ocean Dyn., 64, 12331246, https://doi.org/10.1007/s10236-014-0745-2.

    • Search Google Scholar
    • Export Citation
  • Newsom, R. K., 2016: Optimizing lidar wind measurements from the DOE WindSentinel Buoys. Pacific Northwest National Laboratory Rep. PNNL-25512, 36 pp., PNNL, https://www.pnnl.gov/main/publications/external/technical_reports/PNNL-25512.pdf.

    • Search Google Scholar
    • Export Citation
  • Newsom, R. K., L. M. Sheridan, B. J. Gaudet, G. G. Medina, Z. Yang, R. Krishnamurthy, and W. J. Shaw, 2020: A study on modeled wind speed errors using the U.S. Department of Energy buoys. Pacific Northwest National Laboratory Rep. PNNL-30117, 62 pp., PNNL, https://doi.org/10.2172/1713065.

    • Search Google Scholar
    • Export Citation
  • Olabarrieta, M., J. C. Warner, and B. Armstrong, 2012: Ocean–atmosphere dynamics during Hurricane Ida and Nor’Ida: An application of the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) modeling system. Ocean Modell., 43–44, 112137, https://doi.org/10.1016/j.ocemod.2011.12.008.

    • Search Google Scholar
    • Export Citation
  • Oost, W., G. J. Komen, C. M. J. Jacobs, and C. Van Oort, 2002: New evidence for a relation between wind stress and wave age from measurements during ASGAMAGE. Bound.-Layer Meteor., 103, 409438, https://doi.org/10.1023/A:1014913624535.

    • Search Google Scholar
    • Export Citation
  • Ren, D., J. Du, F. Hua, Y. Yang, and L. Han, 2016: Analysis of different atmospheric physical parameterizations in COAWST modeling system for the Tropical Storm Nock-ten application. Nat. Hazards, 82, 903920, https://doi.org/10.1007/s11069-016-2225-0.

    • Search Google Scholar
    • Export Citation
  • Renault, L., J. Chiggiato, J. C. Warner, M. Gomez, G. Vizoso, and J. Tintoré, 2012: Coupled atmosphere–ocean–wave simulations of a storm event over the Gulf of Lion and Balearic Sea. J. Geophys. Res., 117, C09019, https://doi.org/10.1029/2012JC007924.

    • Search Google Scholar
    • Export Citation
  • Ricchi, A., M. Miglietta, P. Falco, A. Benetazzo, D. Bonaldo, A. Bergamasco, M. Sclavo, and S. Carniel, 2016: On the use of a coupled ocean–atmosphere–wave model during an extreme cold air outbreak over the Adriatic Sea. Atmos. Res., 172–173, 4865, https://doi.org/10.1016/j.atmosres.2015.12.023.

    • Search Google Scholar
    • Export Citation
  • Ricchi, A., and Coauthors, 2017: Sensitivity of a Mediterranean tropical-like cyclone to different model configurations and coupling strategies. Atmosphere, 8, 92, https://doi.org/10.3390/atmos8050092.

    • Search Google Scholar
    • Export Citation
  • Saha, S., and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 10151057, https://doi.org/10.1175/2010BAMS3001.1.

    • Search Google Scholar
    • Export Citation
  • Saha, S., and Coauthors, 2014: The NCEP Climate Forecast System version 2. J. Climate, 27, 21852208, https://doi.org/10.1175/JCLI-D-12-00823.1.

    • Search Google Scholar
    • Export Citation
  • Shaw, W. J., and Coauthors, 2020: General analysis of data collected from DOE lidar buoy deployments off Virginia and New Jersey. Pacific Northwest National Laboratory Rep. PNNL-29823, PNNL, https://doi.org/10.2172/1632348.

    • Search Google Scholar
    • Export Citation
  • Sheridan, L. M., R. Krishnamurthy, A. M. Gorton, W. J. Shaw, and R. K. Newsom, 2020: Validation of reanalysis-based offshore wind resource characterization using lidar buoy observations. Mar. Technol. Soc. J., 54, 4461, https://doi.org/10.4031/MTSJ.54.6.13.

    • Search Google Scholar
    • Export Citation
  • Sheridan, L. M., R. Krishnamurthy, and B. J. Gaudet, 2021: Assessment of model hub height wind speed performance using DOE lidar buoy data. Pacific Northwest National Laboratory PNNL-30840, PNNL, https://www.pnnl.gov/publications/assessment-model-hub-height-wind-speed-performance-using-doe-lidar-buoy-data.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Coauthors, 2019: A description of the Advanced Research WRF Model version 4. NCAR Tech. Note NCAR/TN-556+STR, 145 pp., https://doi.org/10.5065/1dfh-6p97.

    • Search Google Scholar
    • Export Citation
  • Stauffer, D. R., and N. L. Seaman, 1994: Multiscale four-dimensional data assimilation. J. Appl. Meteor., 33, 416434, https://doi.org/10.1175/1520-0450(1994)033<0416:MFDDA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Taylor, P. K., and M. A. Yelland, 2001: The dependence of sea surface roughness on the height and steepness of the waves. J. Phys. Oceanogr., 31, 572590, https://doi.org/10.1175/1520-0485(2001)031<0572:TDOSSR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • The WAVEWATCH III Development Group, 2016: User manual and system documentation of WAVEWATCH III version 5.16. Tech. Note 329, NOAA/NWS/NCEP/MMAB, 361 pp., https://polar.ncep.noaa.gov/waves/wavewatch/manual.v5.16.pdf.

    • Search Google Scholar
    • Export Citation
  • Warner, J. C., B. Armstrong, R. He, and J. B. Zambon, 2010: Development of a Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) modeling system. Ocean Modell., 35, 230244, https://doi.org/10.1016/j.ocemod.2010.07.010.

    • Search Google Scholar
    • Export Citation
  • Zambon, J. B., R. He, and J. C. Warner, 2014a: Tropical to extratropical: Marine environmental changes associated with Superstorm Sandy prior to its landfall. Geophys. Res. Lett., 41, 89358943, https://doi.org/10.1002/2014GL061357.

    • Search Google Scholar
    • Export Citation
  • Zambon, J. B., R. He, and J. C. Warner, 2014b: Investigation of Hurricane Ivan using the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) model. Ocean Dyn., 64, 15351554, https://doi.org/10.1007/s10236-014-0777-7.

    • Search Google Scholar
    • Export Citation
  • Zang, Z., G. Xue, S. Bao, Q. Chen, N. D. Walker, A. S. Haag, Q. Ge, and Z. Yao, 2018: Numerical study of sediment dynamics during Hurricane Gustav. Ocean Modell., 126, 2942, https://doi.org/10.1016/j.ocemod.2018.04.002.

    • Search Google Scholar
    • Export Citation
  • Zhao, X., and J. C. L. Chan, 2017: Effect of the initial vortex size on intensity change in the WRF-ROMS coupled model. J. Geophys. Res. Oceans, 122, 96369648, https://doi.org/10.1002/2017JC013283.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 479 0 0
Full Text Views 765 404 22
PDF Downloads 546 251 11