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Boundary-Layer Meteorology

, Volume 162, Issue 1, pp 117–142 | Cite as

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

  • Ben Yang
  • Yun Qian
  • Larry K. BergEmail author
  • Po-Lun Ma
  • Sonia Wharton
  • Vera Bulaevskaya
  • Huiping Yan
  • Zhangshuan Hou
  • William J. Shaw
Research Article

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.

Keywords

Parametrization schemes Parametric sensitivity Planetary boundary layer Surface layer Turbine-height wind speed Weather Research and Forecasting model 

Notes

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.

Supplementary material

10546_2016_185_MOESM1_ESM.docx (61 kb)
Supplementary material 1 (docx 61 KB)
10546_2016_185_MOESM2_ESM.docx (722 kb)
Supplementary material 2 (docx 722 KB)

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Copyright information

© Springer Science+Business Media Dordrecht (outside the USA) 2016

Authors and Affiliations

  1. 1.CMA-NJU Joint Laboratory for Climate Prediction Studies, Institute for Climate and Global Change Research, School of Atmospheric SciencesNanjing UniversityNanjingChina
  2. 2.Pacific Northwest National LaboratoryRichlandUSA
  3. 3.Lawrence Livermore National LaboratoryLivermoreUSA

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