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Sensitivity of Turbine-Height Wind Speeds to Parameters in Planetary Boundary-Layer and Surface-Layer Schemes in the Weather Research and Forecasting Model

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Abstract

We evaluate the sensitivity of simulated turbine-height wind speeds to 26 parameters within the Mellor–Yamada–Nakanishi–Niino (MYNN) planetary boundary-layer scheme and MM5 surface-layer scheme of the Weather Research and Forecasting model over an area of complex terrain. An efficient sampling algorithm and generalized linear model are used to explore the multiple-dimensional parameter space and quantify the parametric sensitivity of simulated turbine-height wind speeds. The results indicate that most of the variability in the ensemble simulations is due to parameters related to the dissipation of turbulent kinetic energy (TKE), Prandtl number, turbulent length scales, surface roughness, and the von Kármán constant. The parameter associated with the TKE dissipation rate is found to be most important, and a larger dissipation rate produces larger hub-height wind speeds. A larger Prandtl number results in smaller nighttime wind speeds. Increasing surface roughness reduces the frequencies of both extremely weak and strong airflows, implying a reduction in the variability of wind speed. All of the above parameters significantly affect the vertical profiles of wind speed and the magnitude of wind shear. The relative contributions of individual parameters are found to be dependent on both the terrain slope and atmospheric stability.

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References

  • Andren A, Moeng CH (1993) Single-point closures in a neutrally stratified boundary-layer. J Atmos Sci 50(20):3366–3379

    Article  Google Scholar 

  • Banta RM, Pichugina YL, Kelley ND, Hardesty RM, Brewer WA (2013) Wind energy meteorology: insight into wind properties in the turbine-rotor layer of the atmosphere from high-resolution Doppler lidar. Bull Am Meteorol Soc 94(6):883–902

    Article  Google Scholar 

  • Berg LK, Pekour M, Nelson D (2012) Description of the Columbia Basin wind energy study (CBWES). Technical Report PNNL-22036, Pacific Northwest National Laboratory, Richland, Washington, 14 pp

  • Berg LK, Zhong SY (2005) Sensitivity of MM5-simulated boundary layer characteristics to turbulence parameterizations. J Appl Meteorol 44(9):1467–1483

    Article  Google Scholar 

  • Boyle JS, Klein SA, Lucas DD, Ma HY, Tannahill J, Xie S (2015) The parametric sensitivity of CAM5’s MJO. J Geophys Res 120(4):1424–1444

    Google Scholar 

  • Businger JA (1988) A note on the Businger-Dyer profiles. Boundary-Layer Meteorol 42(1–2):145–151

    Article  Google Scholar 

  • Caflisch RE (1998) Monte Carlo and quasi-Monte Carlo methods. Acta Numer 7:1–49

    Article  Google Scholar 

  • Carvalho D, Rocha A, Gómez-Gesteira M, Santos C (2012) A sensitivity study of the WRF model in wind simulation for an area of high wind energy. Environ Modell Softw 33:23–34

    Article  Google Scholar 

  • Chen F, Dudhia J (2001) Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon Weather Rev 129(4):569–585

    Article  Google Scholar 

  • Cheng YG, Brutsaert W (2005) Flux-profile relationships for wind speed and temperature in the stable atmospheric boundary layer. Boundary-Layer Meteorol 114(3):519–538

    Article  Google Scholar 

  • Chou M-D, Suarez MJ (1994) An efficient thermal infrared radiation parameterization for use in general circulation models. NASA Technical Report 104606, 85 pp

  • Draxl C, Hahmann AN, Peña A, Giebel G (2014) Evaluating winds and vertical wind shear from Weather Research and Forecasting model forecasts using seven planetary boundary layer schemes. Wind Energy 17(1):39–55

    Article  Google Scholar 

  • Drechsel S, Mayr GJ, Messner JW, Stauffer R (2012) Wind speeds at heights crucial for wind energy: measurements and verification of forecasts. J Appl Meteorol Climatol 51(9):1602–1617

    Article  Google Scholar 

  • Dyer AJ (1967) The turbulent transport of heat and water vapour in an unstable atmosphere. Q J R Meteorol Soc 93(398):501–508

    Article  Google Scholar 

  • Dyer AJ (1974) A review of flux-profile relationships. Boundary-Layer Meteorol 7(3):363–372

    Article  Google Scholar 

  • Fairall CW, Bradley EF, Rogers DP, Edson JB, Young GS (1996) Bulk parameterization of air-sea fluxes for Tropical Ocean-Global Atmosphere Coupled-Ocean Atmosphere Response Experiment. J Geophys Res 101(C2):3747

    Article  Google Scholar 

  • García-Díez M, Fernández J, Fita L, Yagüe C (2013) Seasonal dependence of WRF model biases and sensitivity to PBL schemes over Europe. Q J R Meteorol Soc 139(671):501–514

    Article  Google Scholar 

  • Gibson MM, Launder BE (1978) Ground effects on pressure fluctuations in the atmospheric boundary-layer. J Fluid Mech 86(Jun):491–511

    Article  Google Scholar 

  • Grachev AA, Andreas EL, Fairall CW, Guest PS, Persson POG (2007) SHEBA flux-profile relationships in the stable atmospheric boundary layer. Boundary-Layer Meteorol 124(3):315–333

    Article  Google Scholar 

  • Grachev AA, Fairall CW, Bradley EF (2000) Convective profile constants revisited. Boundary-Layer Meteorol 94(3):495–515

    Article  Google Scholar 

  • Grell GA, Dudhia J, Stauffer DR (1994) A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5), NCAR Technical Note NCAR/TN-398+STR, 117 pp

  • Guo Z, Wang MH, Qian Y, Larson VE, Ghan S, Ovchinnikov M, Bogenschutz PA, Zhao C, Lin G, Zhou TJ (2014) A sensitivity analysis of cloud properties to CLUBB parameters in the single-column Community Atmosphere Model (SCAM5). J Adv Model Earth Syst 6(3):829–858

    Article  Google Scholar 

  • Högström U (1988) Non-dimensional wind and temperature profiles in the atmospheric surface-layer: a re-evaluation. Boundary-Layer Meteorol 42(1–2):55–78

    Article  Google Scholar 

  • Hong S-Y (2010) A new stable boundary-layer mixing scheme and its impact on the simulated East Asian summer monsoon. Q J R Meteorol Soc 136(651):1481–1496

    Article  Google Scholar 

  • Hong SY, Noh Y, Dudhia J (2006) A new vertical diffusion package with an explicit treatment of entrainment processes. Mon Weather Rev 134(9):2318–2341

    Article  Google Scholar 

  • Hou ZS, Huang MY, Leung LR, Lin G, Ricciuto DM (2012) Sensitivity of surface flux simulations to hydrologic parameters based on an uncertainty quantification framework applied to the Community Land Model. J Geophys Res 117:D15108

    Article  Google Scholar 

  • Hu X-M, Klein PM, Xue M (2013) Evaluation of the updated YSU planetary boundary layer scheme within WRF for wind resource and air quality assessments. J Geophys Res 118(18):10,490–10,505

    Google Scholar 

  • Hu X-M, Nielsen-Gammon JW, Zhang F (2010) Evaluation of three planetary boundary layer schemes in the WRF model. J Appl Meteorol Climatol 49(9):1831–1844

    Article  Google Scholar 

  • International Energy Agency (2008) World energy outlook: 2008. OECD/IEA

  • Izumi Y (1971) Kansas 1968 field program data report. Environmental Research Paper, No. 379, Air Force Cambridge Research Laboratories, Bedford, Massachusetts, 86 pp

  • Jiménez PA, Dudhia J, González-Rouco JF, Navarro J, Montávez JP, García-Bustamante E (2012) A revised scheme for the WRF surface layer formulation. Mon Weather Rev 140(3):898–918

    Article  Google Scholar 

  • Kim JW, Mahrt L (1992) Simple formulation of turbulent mixing in the stable free atmosphere and nocturnal boundary-layer. Tellus A 44A(5):381–394

    Article  Google Scholar 

  • Lu X, McElroy MB, Kiviluoma J (2009) Global potential for wind-generated electricity. Proc Natl Acad Sci USA 106(27):10933–10938

    Article  Google Scholar 

  • Ma PL, Gattiker JR, Liu XH, Rasch PJ (2013) A novel approach for determining source-receptor relationships in model simulations: a case study of black carbon transport in northern hemisphere winter. Environ Res Lett 8(2):024042

    Article  Google Scholar 

  • Mahoney WP, Parks K, Wiener G, Liu YB, Myers WL, Sun JZ, Delle Monache L, Hopson T, Johnson D, Haupt SE (2012) A wind power forecasting system to optimize grid integration. IEEE Trans Sustain Energy 3(4):670–682

    Article  Google Scholar 

  • Marjanovic N, Wharton S, Chow FK (2014) Investigation of model parameters for high-resolution wind energy forecasting: case studies over simple and complex terrain. J Wind Eng Ind Aerodyn 134:10–24

    Article  Google Scholar 

  • Marquis M, Wilczak J, Ahlstrom M, Sharp J, Stern A, Smith JC, Calvert S (2011) Forecasting the wind to reach significant penetration levels of wind energy. Bull Am Meteorol Soc 92(9):1159–1171

    Article  Google Scholar 

  • Mass C, Ovens D (2010) WRF model physics: progress, problems, and perhaps some solutions. In: The 11th WRF users’ workshop, Boulder, CO, 21–25 June, 2010

  • McCullagh P, Nelder JA (1989) Generalized linear models. Chapman and Hall, London, UK, 532 pp

  • Mellor GL, Yamada T (1974) A hierarchy of turbulence closure models for planetary boundary-layers. J Atmos Sci 31(7):1791–1806

    Article  Google Scholar 

  • Mellor GL, Yamada T (1982) Development of a turbulence closure-model for geophysical fluid problems. Rev Geophys 20(4):851–875

    Article  Google Scholar 

  • Mesinger F, DiMego G, Kalnay E, Mitchell K, Shafran PC, Ebisuzaki W, Jovic D, Woollen J, Rogers E, Berbery EH, Ek MB, Fan Y, Grumbine R, Higgins W, Li H, Lin Y, Manikin G, Parrish D, Shi W (2006) North American regional reanalysis. Bull Am Meteorol Soc 87(3):343–360

    Article  Google Scholar 

  • Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J Geophys Res 102(D14):16663–16682

    Article  Google Scholar 

  • Moeng CH, Wyngaard JC (1986) An analysis of closures for pressure-scalar covariances in the convective boundary-layer. J Atmos Sci 43(21):2499–2513

    Article  Google Scholar 

  • Musgrove P (2010) Wind power. Cambridge Univ Press, Cambridge, UK, 338 pp

  • Nakanishi M (2001) Improvement of the Mellor–Yamada turbulence closure model based on large-eddy simulation data. Boundary-Layer Meteorol 99(3):349–378

    Article  Google Scholar 

  • Nakanishi M, Niino H (2004) An improved Mellor–Yamada level-3 model with condensation physics: its design and verification. Boundary-Layer Meteorol 112(1):1–31

    Article  Google Scholar 

  • Nakanishi M, Niino H (2006) An Improved Mellor–Yamada Level-3 Model: its numerical stability and application to a regional prediction of advection fog. Boundary-Layer Meteorol 119(2):397–407

    Article  Google Scholar 

  • Nakanishi M, Niino H (2009) Development of an improved turbulence closure model for the atmospheric boundary layer. J Meteorol Soc Jpn 87(5):895–912

    Article  Google Scholar 

  • National Renewable Energy Laboratory (2008) 20% Wind Energy by 2030: increasing wind energy’s contribution to U.S. electricity supply. U.S. Department of Energy, Washington, D.C., 228 pp

  • Nielsen-Gammon JW, Hu X-M, Zhang F, Pleim JE (2010) Evaluation of planetary boundary layer scheme sensitivities for the purpose of parameter estimation. Mon Weather Rev 138(9):3400–3417

    Article  Google Scholar 

  • Paulson CA (1970) The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer. J Appl Meteorol 9:857–861

    Article  Google Scholar 

  • Pimentel D, Herz M, Glickstein M, Zimmerman M, Allen R, Becker K, Evans J, Hussain B, Sarsfeld R, Grosfeld A, Seidel T (2002) Renewable energy: current and potential issues. Bioscience 52(12):1111–1120

    Article  Google Scholar 

  • Pleim JE (2007) A combined local and nonlocal closure model for the atmospheric boundary layer. Part I: Model description and testing. J Appl Meteorol Climatol 46(9):1383–1395

    Article  Google Scholar 

  • Qian Y, Yan H, Hou Z, Johannesson G, Klein S, Lucas D, Neale R, Rasch P, Swiler L, Tannahill J (2015) Parametric sensitivity analysis of precipitation at global and 813 local scales in the Community Atmosphere Model CAM5. J Adv Model Earth Syst 7:382–411

    Article  Google Scholar 

  • Schumann U, Gerz T (1995) turbulent mixing in stably stratified shear flows. J Appl Meteorol 34(1):33–48

    Article  Google Scholar 

  • Shaw WJ, Lundquist JK, Schreck SJ (2009) Research needs for wind resource characterization. Bull Am Meteorol Soc 90(4):535–538

    Article  Google Scholar 

  • Sims REH, Schock RN, Adegbululgbe A, Fenhann J, Konstantinaviciute I, Moomaw W, Nimir HB, Schlamadinger B (2007) Energy Supply. In: Metz Bet al (eds.), Climate Change 2007: Mitigation. Contribution of working group III to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, UK, 851 pp

  • Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Duda MG, Huang X-Y, Wang W, Powers JG (2008) A description of the Advanced Research WRF version 3. NCAR Tech Note NCAR/TN-475+STR, 113 pp

  • Steeneveld GJ, Mauritsen T, de Bruijn EIF, de Arellano JVG, Svensson G, Holtslag AAM (2008) Evaluation of limited-area models for the representation of the diurnal cycle and contrasting nights in CASES-99. J Appl Meteorol Climatol 47(3):869–887

    Article  Google Scholar 

  • Storm B, Basu S (2010) The WRF model forecast-derived low-level wind shear climatology over the United States Great Plains. Energies 3(2):258–276

    Article  Google Scholar 

  • Storm B, Dudhia J, Basu S, Swift A, Giammanco I (2009) Evaluation of the Weather Research and Forecasting model on forecasting low-level jets: implications for wind energy. Wind Energy 12(1):81–90

    Article  Google Scholar 

  • Stull R (1988) An introduction to boundary layer meteorology. Kluwer Academic Publishers, Dordrecht, 666 pp

  • Wan H, Rasch PJ, Zhang K, Qian Y, Yan H, Zhao C (2014) Short ensembles: an efficient method for discerning climate-relevant sensitivities in atmospheric general circulation models. Geosci Model Dev 7(5):1961–1977

    Article  Google Scholar 

  • Yang B, Qian Y, Lin G, Leung LR, Zhang YC (2012) Some issues in uncertainty quantification and parameter tuning: a case study of convective parameterization scheme in the WRF regional climate model. Atmos Chem Phys 12:2409–2427

    Article  Google Scholar 

  • Yang Q, Berg LK, Pekour M, Fast JD, Newsom RK, Stoelinga M, Finley C (2013) Evaluation of WRF-predicted near-hub-height winds and ramp events over a Pacific Northwest Site with complex terrain. J Appl Meteorol Climatol 52(8):1753–1763

  • Yang B, Zhang Y, Qian Y, Wu T, Huang A, Fang Y (2015) Parametric sensitivity analysis for the Asian summer monsoon precipitation simulation in the Beijing Climate Center AGCM, version 2.1. J Clim 28(14):5622–5644

    Article  Google Scholar 

  • Zhao C, Liu X, Qian Y, Yoon J, Hou Z, Lin G, McFarlane S, Wang H, Yang B, Ma PL, Yan H, Bao J (2013) A sensitivity study of radiative fluxes at the top of atmosphere to cloud-microphysics and aerosol parameters in the community atmosphere model CAM5. Atmos Chem Phys 13(21):10969–10987

    Article  Google Scholar 

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Acknowledgments

The authors acknowledge Qing Yang, Hui Wan, Chun Zhao, and William Gustafson Jr. of Pacific Northwest National Laboratory (PNNL) and Joseph Olson of the National Oceanic and Atmospheric Administration (NOAA) for valuable discussions. This study is based on work supported by U.S. Department of Energy’s Wind and Water Power program. The Pacific Northwest National Laboratory is operated for the U.S. Department of Energy by Battelle Memorial Institute under contract DE-AC05-76RL01830. Lawrence Livermore National Laboratory is operated by Lawrence Livermore National Security, LLC, for the U.S. Department of Energy, National Nuclear Security Administration under Contract DE-AC52-07NA27344. The work of B.Y. at Nanjing University is supported by the National Natural Science Foundation of China (41305084). The PNNL Institutional Computing (PIC) and National Energy Research Scientific Computing Center (NERSC) provided computational resources. The NARR reanalysis were freely obtained from CISL Research Data Archive at http://rda.ucar.edu/datasets/ds608.0/. The WRF model outputs used in this study are stored at a PNNL cluster and are available upon request from the corresponding author. Data from CBWES are available from the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) data archive.

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Correspondence to Larry K. Berg.

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Yang, B., Qian, Y., Berg, L.K. et al. Sensitivity of Turbine-Height Wind Speeds to Parameters in Planetary Boundary-Layer and Surface-Layer Schemes in the Weather Research and Forecasting Model. Boundary-Layer Meteorol 162, 117–142 (2017). https://doi.org/10.1007/s10546-016-0185-2

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