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Simulating impacts of real-world wind farms on land surface temperature using the WRF model: physical mechanisms

  • Geng XiaEmail author
  • Liming Zhou
  • Justin R. Minder
  • Robert G. Fovell
  • Pedro A. Jimenez
Article

Abstract

A recent study shows that the current wind turbine parameterization in the weather research and forecasting (WRF) model can generally reproduce the satellite observed nighttime warming signal over wind farm (WF) regions over west central Texas, but also tends to produce a cooling effect in the WF downwind regions. The present study conducts a series of WRF simulations to further this research by addressing two fundamental questions: (i) what is the 3-D structure of simulated near-surface temperatures within and around WFs? (ii) what are the main physical mechanisms responsible for the simulated WF-induced temperature changes? Our results indicate that the WF-induced temperature changes are not only restricted to the surface but also can extend vertically to the hub-height level and horizontally in the downwind direction. The WF-induced change in sensible heat flux is the dominant factor for the simulated temperature changes at the surface, for both the warming signals over the WF region and the cooling signals behind it. Further diagnosis shows that the turbulent component of the wind turbine parameterization is responsible for the surface warming signal by enhancing vertical mixing while the momentum sink component is responsible for the surface cooling signal by enhancing near-surface thermal stratification. By analyzing the energy budget, we find two important physical processes that are critical to explain the simulated WF impacts on temperature: (i) vertical divergence of heat flux as parameterized in the planetary boundary layer scheme and (ii) resolved-scale 3-D temperature advection.

Notes

Acknowledgements

This work was supported by the National Science Foundation (NSF-AGS-1247137) Grant. We also would like to thank two anonymous reviewers for their helpful comments.

Supplementary material

382_2019_4725_MOESM1_ESM.pdf (644 kb)
Supplementary material 1 (PDF 643 KB)

References

  1. Adams AS, Keith DW (2013) Are global wind power resources estimates overstated? Environ Res Lett.  https://doi.org/10.1088/1748-9326/8/1/015021 Google Scholar
  2. Armstrong A, Waldron S, Whitaker J, Ostle NJ (2014) Wind farm and solar park effects on plant–soil carbon cycling: uncertain impacts of changes in ground-level microclimate. Glob Change Biol.  https://doi.org/10.1111/gcb.12437 Google Scholar
  3. Armstrong A, Burton RR, Lee SE, Mobbs S, Ostle N, Smith V, Whitaker J (2016) Ground-level climate at a peatland wind farm in Scotland is affected by wind turbine operation. Environ Res Lett 11:044024CrossRefGoogle Scholar
  4. Baidya RS, Traiteur JJ (2010) Impacts of wind farms on surface air temperatures. Proc Nat Acad Sci.  https://doi.org/10.1073/pnas.1000493107 Google Scholar
  5. Cervarich M, Baidya RS, Zhou L (2013) Spatiotemporal structure of wind farm-atmospheric boundary layer interactions. Energy Procedia 40:530–536CrossRefGoogle Scholar
  6. Chang R, Zhu R, Guo P (2016) A case study of land-surface-temperature impact from large-scale deployment of wind farms in China from Guazhou. Remote Sens.  https://doi.org/10.3390/rs8100790 Google Scholar
  7. Fitch AC, Olson J, Lundquist J, Dudhia J, Gupta A, Michalakes J, Barstad I (2012) Local and mesoscale impacts of wind farms as parameterized in a mesoscale NWP model. Mon Weather Rev 204:3017–3038CrossRefGoogle Scholar
  8. Fitch AC, Lundquist JK, Olson JB (2013) Mesoscale influences of wind farms throughout a diurnal cycle. Mon Weather Rev 141(7):2173–2198CrossRefGoogle Scholar
  9. Harris RA, Zhou L, Xia G (2014) Satellite observations of wind farm impacts on nocturnal land surface temperature in Iowa. Remote Sens 6(12):12234–12246CrossRefGoogle Scholar
  10. Jacobson MZ, Archer CL (2012) Saturation wind power potential and its implications for wind energy. Proc Nat Acad Sci 109(39):15679–15684CrossRefGoogle Scholar
  11. Jimenez PA, Navarro J, Palomares AM, Dudhia J (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(3):559–566CrossRefGoogle Scholar
  12. Lee JCY, Lundquist JK (2017) Observing and simulating wind-turbine wakes during the evening transition. Bound Layer Meteorol 163(3):449–474CrossRefGoogle Scholar
  13. Nakanishi M, Niino H (2009) Development of an improved turbulence closure model for the atmospheric boundary layer. J Meteorol Soc Jpn 87:895–912CrossRefGoogle Scholar
  14. Rajewski DA, Tackle ES, Lundquist JK, Oncley S, Prueger JH, Horst T, Rhodes M, Pfeiffer R, Hatfield JL, Spoth K, Doorenbos R (2013) Crop wind energy experiment (CWEX): observations of surface-layer, boundary layer, and mesoscale interactions with a wind farm. Bull Am Meteorol Soc 94:655–672CrossRefGoogle Scholar
  15. Rajewski DA, Takle ES, Lundquist JK, Prueger JH, Pfeiffer RL, Hatfield JL, Doorenbos RK (2014) Changes in fluxes of heat, H2O, and CO2 caused by a large wind farm. Agric For Meteorol 194:175–187CrossRefGoogle Scholar
  16. Rajewski DA, Takle ES, Prueger JH, Doorenbos RK (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(22):13392–13414CrossRefGoogle Scholar
  17. Skamarock WC, Klemp JB (2008) A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. J Comput Phys 227:3465–3485CrossRefGoogle Scholar
  18. Skamarock WC et al (2008) A description of the advanced research WRF version 3. Tech. Rep. NCAR/TN-475 + STRGoogle Scholar
  19. Slawsky LM, Zhou L, Baidya SR, Xia G, Vuille M, Harris RA (2015) Observed thermal impacts of wind farms over northern illinois. Remote Sens 15(7):14981–15005Google Scholar
  20. Smith CR, Barthelmie RJ, Pryor SC (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 8:034006CrossRefGoogle Scholar
  21. Sun H, Luo Y, Zhao Z, Chang R (2018) The impacts of Chinese wind farms on climate. J Geophys Res Atmos 123:5177–5187.  https://doi.org/10.1029/2017JD028028 CrossRefGoogle Scholar
  22. Tang B, Wu D, Zhao X, Zhou T, Zhao W, Wei H (2017) The observed impacts of wind farms on local vegetation growth in Northern China. Remote Sens 9(4):332CrossRefGoogle Scholar
  23. Vanderwende B, Lundquist JK, Rhodes ME, Takle GS, Purdy SI (2015) Observing and simulating the summertime low-level jet in central Iowa. Mon Weather Rev 143:2319–2336CrossRefGoogle Scholar
  24. Volker PJH, Badger J, Hahmann AN, Ott S (2015) The explicit wake parameterization v1.0: a wind farm parameterization in the mesoscale model WRF. Geosci Model Dev 8(11):3715–3731CrossRefGoogle Scholar
  25. Wilczak J, Finley C, Freedman J, Cline J, Bianco L, Olson J, Djalalova I, Sheridan L, Ahlstrom M, Manobianco J, Zack J, Carley J, Benjamin S, Marquis M (2014) The wind forecast improvement project (WFIP): a public-private partnership addressing wind energy forecast needs. Bull Am Meteorol Soc.  https://doi.org/10.1175/BAMS-D-14-00107.1 Google Scholar
  26. Xia G, Zhou L (2017a) Detecting wind farm impacts on local vegetation growth in Texas and Illinois using MODIS vegetation greenness measurements. Remote Sens 9:698CrossRefGoogle Scholar
  27. Xia G, Zhou L, Freedman JM, Roy SB, Harris RA, Cervarich MC (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. Clim Dyn 46:1–18CrossRefGoogle Scholar
  28. Xia G, Cervarich M, Baidya SB, Zhou L, Minder J, Freedam JM, Jiménez PA (2017b) Simulating impacts of real-world wind farms on land surface temperature using WRF model: validation with MODIS observations. Mon Weather Rev 145:4813–4836CrossRefGoogle Scholar
  29. Zhou L, Dickinson RE, Ogawa K, Tian Y, Jin M, Schmugge T, Tsvetsinskaya E (2003a) Relations between albedos and emissivities from MODIS and ASTER data over North African desert. Geophys Res Lett 30(20):2026CrossRefGoogle Scholar
  30. Zhou L, Dickinson RE, Tian Y, Jin M, Ogawa K, Yu H, Schmugge T (2003b) A sensitivity study of climate and energy balance simulations with use of satellite derived emissivity data over the northern Africa and the Arabian peninsula. J Geophys Res 108(D24):4795.  https://doi.org/10.1029/2003JD004083 CrossRefGoogle Scholar
  31. Zhou L, Tian Y, Baidya RS, Thorncroft C, Bosart LF, Hu Y (2012) Impacts of wind farms on land surface temperature. Nat Clim Change 2(7):539–543CrossRefGoogle Scholar
  32. Zhou L, Tian Y, Baidya RS, Dai Y, Chen H (2013a) Diurnal and seasonal variations of wind farm impacts on land surface temperature over western Texas. Clim Dyn 41:307–326CrossRefGoogle Scholar
  33. Zhou L, Tian Y, Chen H, Dai Y, Harris RA (2013b) Effects of topography on assessing wind farm impacts using MODIS data. Earth Interact 17(13):1–18CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Atmospheric and Environmental SciencesUniversity at Albany, State University of New YorkAlbanyUSA
  2. 2.Research Applications LaboratoryNCARBoulderUSA

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