Simulating Impacts of Real-World Wind Farms on Land Surface Temperature Using the WRF Model: Validation with Observations

Geng Xia Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, New York

Search for other papers by Geng Xia in
Current site
Google Scholar
PubMed
Close
,
Matthew C. Cervarich Department of Atmospheric Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois

Search for other papers by Matthew C. Cervarich in
Current site
Google Scholar
PubMed
Close
,
Somnath Baidya Roy Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India

Search for other papers by Somnath Baidya Roy in
Current site
Google Scholar
PubMed
Close
,
Liming Zhou Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, New York

Search for other papers by Liming Zhou in
Current site
Google Scholar
PubMed
Close
,
Justin R. Minder Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, New York

Search for other papers by Justin R. Minder in
Current site
Google Scholar
PubMed
Close
,
Pedro A. Jimenez Research Applications Laboratory, NCAR, Boulder, Colorado

Search for other papers by Pedro A. Jimenez in
Current site
Google Scholar
PubMed
Close
, and
Jeffrey M. Freedman Atmospheric Sciences Research Center, University at Albany, State University of New York, Albany, New York

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

Abstract

This study simulates the impacts of real-world wind farms on land surface temperature (LST) using the Weather Research and Forecasting (WRF) Model driven by realistic initial and boundary conditions. The simulated wind farm impacts are compared with the observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the first Wind Forecast Improvement Project (WFIP) field campaign. Simulations are performed over west-central Texas for the month of July throughout 7 years (2003–04 and 2010–14). Two groups of experiments are conducted: 1) direct validations of the simulated LST changes between the preturbine period (2003–04) and postturbine period (2010–14) validated against the MODIS observations; and 2) a model sensitivity test of LST to the wind turbine parameterization by examining LST differences with and without the wind turbines for the postturbine period. Overall, the WRF Model is moderately successful at reproducing the observed spatiotemporal variations of the background LST but has difficulties in reproducing such variations for the turbine-induced LST change signals at pixel levels. However, the model is still able to reproduce coherent and consistent responses of the observed LST changes at regional scales. The simulated wind farm–induced LST warming signals agree well with the satellite observations in terms of their spatial coupling with the wind farm layout. Moreover, the simulated areal mean warming signal (0.20°–0.26°C) is about a tenth of a degree smaller than that from MODIS (0.33°C). However, these results suggest that the current wind turbine parameterization tends to induce a cooling effect behind the wind farm region at nighttime, which has not been confirmed by previous field campaigns and satellite observations.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/MWR-D-16-0401.s1.

© 2017 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: Geng Xia, gxia@albany.edu

Abstract

This study simulates the impacts of real-world wind farms on land surface temperature (LST) using the Weather Research and Forecasting (WRF) Model driven by realistic initial and boundary conditions. The simulated wind farm impacts are compared with the observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the first Wind Forecast Improvement Project (WFIP) field campaign. Simulations are performed over west-central Texas for the month of July throughout 7 years (2003–04 and 2010–14). Two groups of experiments are conducted: 1) direct validations of the simulated LST changes between the preturbine period (2003–04) and postturbine period (2010–14) validated against the MODIS observations; and 2) a model sensitivity test of LST to the wind turbine parameterization by examining LST differences with and without the wind turbines for the postturbine period. Overall, the WRF Model is moderately successful at reproducing the observed spatiotemporal variations of the background LST but has difficulties in reproducing such variations for the turbine-induced LST change signals at pixel levels. However, the model is still able to reproduce coherent and consistent responses of the observed LST changes at regional scales. The simulated wind farm–induced LST warming signals agree well with the satellite observations in terms of their spatial coupling with the wind farm layout. Moreover, the simulated areal mean warming signal (0.20°–0.26°C) is about a tenth of a degree smaller than that from MODIS (0.33°C). However, these results suggest that the current wind turbine parameterization tends to induce a cooling effect behind the wind farm region at nighttime, which has not been confirmed by previous field campaigns and satellite observations.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/MWR-D-16-0401.s1.

© 2017 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: Geng Xia, gxia@albany.edu
Save
  • Abkar, M., and F. Porté-Agel, 2015: Influence of atmospheric stability on wind-turbine wakes: A large-eddy simulation study. Phys. Fluids, 27, 035104, https://doi.org/10.1063/1.4913695.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Armstrong, A., S. Waldron, J. Whitaker, and N. J. Ostle, 2014: Wind farm and solar park effects on plant–soil carbon cycling: Uncertain impacts of changes in ground-level microclimate. Global Change Biol., 20, 16991706, https://doi.org/10.1111/gcb.12437.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • AWEA, 2015: U.S. wind industry annual market report. American Wind Energy Association, 110 pp., http://www.awea.org/Resources/Content.aspx?ItemNumber=875&navItemNumber=621.

  • Badger, J., H. P. Frank, A. N. Hahmann, and G. Giebel, 2014: Wind-climate estimation based on mesoscale and microscale modeling: Statistical–dynamical downscaling for wind energy applications. J. Appl. Meteor. Climatol., 53, 19011919, https://doi.org/10.1175/JAMC-D-13-0147.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baidya Roy, S., 2011: Simulating impacts of wind farms on local hydrometeorology. J. Wind Eng. Ind. Aerodyn., 99, 491498, https://doi.org/10.1016/j.jweia.2010.12.013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baidya Roy, S., and J. J. Traiteur, 2010: Impacts of wind farms on surface air temperatures. Proc. Natl. Acad. Sci. USA, 107, 17 89917 904, https://doi.org/10.1073/pnas.1000493107.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baidya Roy, S., S. W. Pacala, and R. L. Walko, 2004: Can large wind farms affect local meteorology? J. Geophys. Res., 109, D19101, https://doi.org/10.1029/2004JD004763.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barrie, D. B., and D. B. Kirk-Davidoff, 2010: Weather response to a large wind turbine array. Atmos. Chem. Phys., 10, 769775, https://doi.org/10.5194/acp-10-769-2010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barthelmie, R. J., and L. E. Jensen, 2010: Evaluation of wind farm efficiency and wind turbine wakes at the Nysted offshore wind farm. Wind Energy, 13, 573586, https://doi.org/10.1002/we.408.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cal, R. B., J. Lebrón, L. Castillo, H. S. Kang, and C. Meneveau, 2010: Experimental study of the horizontally averaged flow structure in a model wind-turbine array boundary layer. J. Renewable Sustainable Energy, 2, 013106, https://doi.org/10.1063/1.3289735.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Calaf, M., C. Meneveau, and J. Meyers, 2010: Large eddy simulation study of fully developed wind-turbine array boundary layers. Phys. Fluids, 22, 015110, https://doi.org/10.1063/1.3291077.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Calaf, M., M. B. Parlange, and C. Meneveau, 2011: Large eddy simulation study of scalar transport in fully developed wind-turbine array boundary layers. Phys. Fluids, 23, 126603, https://doi.org/10.1063/1.3663376.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cervarich, M. C., S. Baidya Roy, and L. Zhou, 2013: Spatiotemporal structure of wind farm-atmospheric boundary layer interactions. Energy Procedia, 40, 530536, https://doi.org/10.1016/j.egypro.2013.08.061.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chang, R., R. Zhu, and P. Guo, 2016: A case study of land-surface-temperature impact from large-scale deployment of wind farms in China from Guazhou. Remote Sens., 8, 790, https://doi.org/10.3390/rs8100790.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569585, https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., C. Liu, J. Dudhia, and M. Chen, 2014: A sensitivity study of high-resolution regional climate simulations to three land surface models over the western United States. J. Geophys. Res. Atmos., 119, 72717291, https://doi.org/10.1002/2014JD021827.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Churchfield, M. J., S. Lee, J. Michalakes, and P. J. Moriarty, 2012: A numerical study of the effects of atmospheric and wake turbulence on wind turbine dynamics. J. Turbul., 13, N14, https://doi.org/10.1080/14685248.2012.668191.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Conil, S., and A. Hall, 2006: Local regimes of atmospheric variability: A case study of southern California. J. Climate, 19, 43084325, https://doi.org/10.1175/JCLI3837.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Creech, A., W. G. Früh, and A. E. Maguire, 2015: Simulations of an offshore wind farm using large-eddy simulation and a torque-controlled actuator disc model. Surv. Geophys., 36, 427481, https://doi.org/10.1007/s10712-015-9313-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 30773107, https://doi.org/10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fitch, A. C., 2015: Climate impacts of large-scale wind farms as parameterized in a global climate model. J. Climate, 28, 61606180, https://doi.org/10.1175/JCLI-D-14-00245.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fitch, A. C., J. Olson, J. Lundquist, J. Dudhia, A. Gupta, J. Michalakes, and I. Barstad, 2012: Local and mesoscale impacts of wind farms as parameterized in a mesoscale NWP model. Mon. Wea. Rev., 140, 30173038, https://doi.org/10.1175/MWR-D-11-00352.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fitch, A. C., J. K. Lundquist, and J. B. Olson, 2013a: Mesoscale influences of wind farms throughout a diurnal cycle. Mon. Wea. Rev., 141, 21732198, https://doi.org/10.1175/MWR-D-12-00185.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fitch, A. C., J. B. Olson, and J. K. Lundquist, 2013b: Parameterization of wind farms in climate models. J. Climate, 26, 64396458, https://doi.org/10.1175/JCLI-D-12-00376.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Freedman, J. M., and Coauthors, 2014: The Wind Forecast Improvement Project (WFIP): A public/private partnership for improving short term wind energy forecasts and quantifying the benefits of utility operations—The southern study area. AWS Truepower Tech. Rep. DOE-AWST-04420, U.S. DOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Hydropower Technology Program (EE-2B), 107 pp., https://doi.org/10.2172/1129905.

    • Crossref
    • Export Citation
  • Harris, R. A., L. Zhou, and G. Xia, 2014: Satellite observations of wind farm impacts on nocturnal land surface temperature in Iowa. Remote Sens., 6, 12 23412 246, https://doi.org/10.3390/rs61212234.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, S. Y., J. Dudhia, and S. H. Chen, 2004: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Wea. Rev., 132, 103120, https://doi.org/10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Horvath, K., D. Koracin, R. Vellore, J. Jiang, and R. Belu, 2012: Sub-kilometer dynamical downscaling of near-surface winds in complex terrain using WRF and MM5 mesoscale models. J. Geophys. Res., 117, D11111, https://doi.org/10.1029/2012JD017432.

    • Search Google Scholar
    • Export Citation
  • Jiménez, P. A., J. F. González-Rouco, E. García-Bustamante, J. Navarro, J. P. Montávez, J. V. de Arellano, J. Dudhia, and A. Muñoz-Roldan, 2010: Surface wind regionalization over complex terrain: Evaluation and analysis of a high-resolution WRF simulation. J. Appl. Meteor. Climatol., 49, 268287, https://doi.org/10.1175/2009JAMC2175.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiménez, P. A., J. Dudhia, J. F. González-Rouco, J. Navarro, J. P. Montávez, and E. García-Bustamante, 2012: A revised scheme for the WRF surface layer formulation. Mon. Wea. Rev., 140, 898918, https://doi.org/10.1175/MWR-D-11-00056.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiménez, P. A., J. Navarro, A. M. Palomares, and J. Dudhia, 2015: Mesoscale modeling of offshore wind turbine wakes at the wind farm resolving scale: A composite‐based analysis with the Weather Research and Forecasting model over Horns Rev. Wind Energy, 18, 559566, https://doi.org/10.1002/we.1708.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Keith, D. W., J. DeCarolis, D. Denkenberger, D. Lenschow, S. L. Malyshev, S. Pacala, and P. J. Rasch, 2004: The influence of large-scale wind power on global climate. Proc. Natl. Acad. Sci. USA, 101, 16 11516 120, https://doi.org/10.1073/pnas.0406930101.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kirk-Davidoff, D. B., and D. W. Keith, 2008: On the climate impact of surface roughness anomalies. J. Atmos. Sci., 65, 22152234, https://doi.org/10.1175/2007JAS2509.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, C., and Coauthors, 2017: Continental-scale convection-permitting modeling of the current and future climate of North America. Climate Dyn., 49, 7195, https://doi.org/10.1007/s00382-016-3327-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lo, J. C. F., Z.-L. Yang, and R. A. Pielke, 2008: Assessment of three dynamical climate downscaling methods using the Weather Research and Forecasting (WRF) model. J. Geophys. Res., 113, D09112, https://doi.org/10.1029/2007JD009216.

    • Search Google Scholar
    • Export Citation
  • Lu, H., and F. Porté-Agel, 2011: Large-eddy simulation of a very large wind farm in a stable atmospheric boundary layer. Phys. Fluids, 23, 065101, https://doi.org/10.1063/1.3589857.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mellor, G. L., and T. Yamada, 1982: Development of a turbulence closure model for geophysical fluid problems. Rev. Geophys., 20, 851875, https://doi.org/10.1029/RG020i004p00851.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mirocha, J. D., B. Kosovic, M. L. Aitken, and J. K. Lundquist, 2014: Implementation of a generalized actuator disk wind turbine model into the weather research and forecasting model for large-eddy simulation applications. J. Renewable Sustainable Energy, 6, 013104, https://doi.org/10.1063/1.4861061.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102, 16 66316 682, https://doi.org/10.1029/97JD00237.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nakanishi, M., and H. Niino, 2009: Development of an improved turbulence closure model for the atmospheric boundary layer. J. Meteor. Soc. Japan, 87, 895912, https://doi.org/10.2151/jmsj.87.895.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Niu, G. Y., and Coauthors, 2011: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local‐scale measurements. J. Geophys. Res., 116, D12109, https://doi.org/10.1029/2010JD015139.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pan, Z., E. Takle, W. Gutowski, and R. Turner, 1999: Long simulation of regional climate as a sequence of short segments. Mon. Wea. Rev., 127, 308321, https://doi.org/10.1175/1520-0493(1999)127<0308:LSORCA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peña, A., and O. Rathmann, 2014: Atmospheric stability‐dependent infinite wind‐farm models and the wake‐decay coefficient. Wind Energy, 17, 12691285, https://doi.org/10.1002/we.1632.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Porté-Agel, F., Y. T. Wu, H. Lu, and R. J. Conzemius, 2011: Large-eddy simulation of atmospheric boundary layer flow through wind turbines and wind farms. J. Wind Eng. Ind. Aerodyn., 99, 154168, https://doi.org/10.1016/j.jweia.2011.01.011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Qian, J. H., A. Seth, and S. Zebiak, 2003: Reinitialized versus continuous simulations for regional climate downscaling. Mon. Wea. Rev., 131, 28572874, https://doi.org/10.1175/1520-0493(2003)131<2857:RVCSFR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rajewski, D. A., and Coauthors, 2013: Crop Wind Energy Experiment (CWEX): Observations of surface-layer, boundary layer, and mesoscale interactions with a wind farm. Bull. Amer. Meteor. Soc., 94, 655672, https://doi.org/10.1175/BAMS-D-11-00240.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rajewski, D. A., E. S. Takle, J. K. Lundquist, J. H. Prueger, R. L. Pfeiffer, J. L. Hatfield, K. Spoth, and R. K. Doorenbos, 2014: Changes in fluxes of heat, H2O, and CO2 caused by a large wind farm. Agric. For. Meteor., 194, 175187, https://doi.org/10.1016/j.agrformet.2014.03.023.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rajewski, D. A., E. S. Takle, J. H. Prueger, and R. K. Doorenbos, 2016: Toward understanding the physical link between turbines and microclimate impacts from in situ measurements in a large wind farm. J. Geophys. Res. Atmos., 121, 13 39213 414, https://doi.org/10.1002/2016JD025297.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rife, D. L., C. A. Davis, Y. Liu, and T. T. Warner, 2004: Predictability of low-level winds by mesoscale meteorological models. Mon. Wea. Rev., 132, 25532569, https://doi.org/10.1175/MWR2801.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Santos-Alamillos, F. J., D. Pozo-Vázquez, J. A. Ruiz-Arias, V. Lara-Fanego, and J. Tovar-Pescador, 2013: Analysis of WRF Model wind estimate sensitivity to physics parameterization choice and terrain representation in Andalusia (southern Spain). J. Appl. Meteor. Climatol., 52, 15921609, https://doi.org/10.1175/JAMC-D-12-0204.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schmitz, S., 2012: XTURB-PSU: A wind turbine design and analysis tool, version 1.1. Dept. of Aerospace Engineering, The Pennsylvania State University, accessed 10 February 2017, http://www.aero.psu.edu/Faculty_Staff/schmitz/XTurb/XTurb.html.

  • Skamarock, W. C., and J. B. Klemp, 2008: A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. J. Comput. Phys., 227, 34653485, https://doi.org/10.1016/j.jcp.2007.01.037.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Slawsky, L. M., L. Zhou, S. Baidya Roy, G. Xia, M. Vuille, and R. A. Harris, 2015: Observed thermal impacts of wind farms over northern Illinois. Sensors, 15, 14 98115 005, https://doi.org/10.3390/s150714981.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, C. M., R. J. Barthelmie, and S. C. Pryor, 2013: In situ observations of the influence of a large onshore wind farm on near-surface temperature, turbulence intensity and wind speed profiles. Environ. Res. Lett., 8, 034006, https://doi.org/10.1088/1748-9326/8/3/034006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tang, B., D. Wu, X. Zhao, T. Zhou, W. Zhao, and H. Wei, 2017: The observed impacts of wind farms on local vegetation growth in northern China. Remote Sens., 9, 332, https://doi.org/10.3390/rs9040332.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • U.S. DOE, 2015: Wind vision: A new era for wind power in the United States. U. S. Dept. of Energy Rep. DOE/GO-102015-4557, 350 pp., https://energy.gov/sites/prod/files/2015/03/f20/wv_full_report.pdf.

  • Wan, Z., 2002: Estimate of noise and systematic error in early thermal infrared data of the Moderate Resolution Imaging Spectroradiometer (MODIS). Remote Sens. Environ., 80, 4754, https://doi.org/10.1016/S0034-4257(01)00266-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wan, Z., 2008: New refinements and validation of the MODIS Land-Surface Temperature/Emissivity products. Remote Sens. Environ., 112, 5974, https://doi.org/10.1016/j.rse.2006.06.026.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, C., and R. G. Prinn, 2010: Potential climatic impacts and reliability of very large-scale wind farms. Atmos. Chem. Phys., 10, 20532061, https://doi.org/10.5194/acp-10-2053-2010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, C., and R. G. Prinn, 2011: Potential climatic impacts and reliability of large-scale offshore wind farms. Environ. Res. Lett., 6, 025101, https://doi.org/10.1088/1748-9326/6/2/025101.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilczak, J., and Coauthors, 2015: The Wind Forecast Improvement Project (WFIP): A public–private partnership addressing wind energy forecast needs. Bull. Amer. Meteor. Soc., 96, 16991718, https://doi.org/10.1175/BAMS-D-14-00107.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, Y. T., and F. Porté-Agel, 2011: Large-eddy simulation of wind-turbine wakes: Evaluation of turbine parametrisations. Bound.-Layer Meteor., 138, 345366, https://doi.org/10.1007/s10546-010-9569-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, Y. T., and F. Porté-Agel, 2013: Simulation of turbulent flow inside and above wind farms: Model validation and layout effects. Bound.-Layer Meteor., 146, 181205, https://doi.org/10.1007/s10546-012-9757-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xia, G., and L. Zhou, 2017: Detecting wind farm impacts on local vegetation growth in Texas and Illinois using MODIS vegetation greenness measurements. Remote Sens., 9, 698, https://doi.org/10.3390/rs9070698.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xia, G., L. Zhou, J. M. Freedman, S. Baidya Roy, R. A. Harris, and M. C. Cervarich, 2016: A case study of effects of atmospheric boundary layer turbulence, wind speed, and stability on wind farm induced temperature changes using observations from a field campaign. Climate Dyn., 46, 21792196, https://doi.org/10.1007/s00382-015-2696-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, Z. L., and Coauthors, 2011: The community Noah land surface model with multiparameterization options (Noah‐MP): 2. Evaluation over global river basins. J. Geophys. Res., 116, D12110, https://doi.org/10.1029/2010JD015140.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, L., Y. Tian, S. Baidya Roy, C. Thorncroft, L. F. Bosart, and Y. Hu, 2012: Impacts of wind farms on land surface temperature. Nat. Climate Change, 2, 539543, https://doi.org/10.1038/nclimate1505.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, L., Y. Tian, S. Baidya Roy, Y. Dai, and H. Chen, 2013a: Diurnal and seasonal variations of wind farm impacts on land surface temperature over western Texas. Climate Dyn., 41, 307326, https://doi.org/10.1007/s00382-012-1485-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, L., Y. Tian, H. Chen, Y. Dai, and R. A. Harris, 2013b: Effects of topography on assessing wind farm impacts using MODIS data. Earth Interact., 17, https://doi.org/10.1175/2012EI000510.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1766 843 30
PDF Downloads 1067 239 16