Articles | Volume 7, issue 1
https://doi.org/10.5194/wes-7-37-2022
https://doi.org/10.5194/wes-7-37-2022
Research article
 | 
19 Jan 2022
Research article |  | 19 Jan 2022

Local-thermal-gradient and large-scale-circulation impacts on turbine-height wind speed forecasting over the Columbia River Basin

Ye Liu, Yun Qian, and Larry K. Berg

Related authors

Offshore low-level jet observations and model representation using lidar buoy data off the California coast
Lindsay M. Sheridan, Raghavendra Krishnamurthy, William I. Gustafson Jr., Ye Liu, Brian J. Gaudet, Nicola Bodini, Rob K. Newsom, and Mikhail Pekour
Wind Energ. Sci., 9, 741–758, https://doi.org/10.5194/wes-9-741-2024,https://doi.org/10.5194/wes-9-741-2024, 2024
Short summary
Tracking precipitation features and associated large-scale environments over southeastern Texas
Ye Liu, Yun Qian, Larry K. Berg, Zhe Feng, Jianfeng Li, Jingyi Chen, and Zhao Yang
EGUsphere, https://doi.org/10.5194/egusphere-2024-112,https://doi.org/10.5194/egusphere-2024-112, 2024
Short summary
Development of a plant carbon-nitrogen interface coupling framework in a coupled biophysical-ecosystem-biogeochemical model (SSiB5/Triffid/DayCent-SOM v1.0): Its parameterization, implementation, and evaluation
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
EGUsphere, https://doi.org/10.5194/egusphere-2023-2634,https://doi.org/10.5194/egusphere-2023-2634, 2023
Short summary
Where does the dust deposited over the Sierra Nevada snow come from?
Huilin Huang, Yun Qian, Ye Liu, Cenlin He, Jianyu Zheng, Zhibo Zhang, and Antonis Gkikas
Atmos. Chem. Phys., 22, 15469–15488, https://doi.org/10.5194/acp-22-15469-2022,https://doi.org/10.5194/acp-22-15469-2022, 2022
Short summary
A plant carbon-nitrogen interface coupling framework in a coupled biophysical-ecosystem-biogeochemical model, SSiB version5/TRIFFID/DayCent-SOM: Its parameterization, implementation, and evaluation
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
EGUsphere, https://doi.org/10.5194/egusphere-2022-1111,https://doi.org/10.5194/egusphere-2022-1111, 2022
Preprint archived
Short summary

Related subject area

Design methods, reliability and uncertainty modelling
Effectively using multifidelity optimization for wind turbine design
John Jasa, Pietro Bortolotti, Daniel Zalkind, and Garrett Barter
Wind Energ. Sci., 7, 991–1006, https://doi.org/10.5194/wes-7-991-2022,https://doi.org/10.5194/wes-7-991-2022, 2022
Short summary
Efficient Bayesian calibration of aerodynamic wind turbine models using surrogate modeling
Benjamin Sanderse, Vinit V. Dighe, Koen Boorsma, and Gerard Schepers
Wind Energ. Sci., 7, 759–781, https://doi.org/10.5194/wes-7-759-2022,https://doi.org/10.5194/wes-7-759-2022, 2022
Short summary
Fast yaw optimization for wind plant wake steering using Boolean yaw angles
Andrew P. J. Stanley, Christopher Bay, Rafael Mudafort, and Paul Fleming
Wind Energ. Sci., 7, 741–757, https://doi.org/10.5194/wes-7-741-2022,https://doi.org/10.5194/wes-7-741-2022, 2022
Short summary
A simplified, efficient approach to hybrid wind and solar plant site optimization
Charles Tripp, Darice Guittet, Jennifer King, and Aaron Barker
Wind Energ. Sci., 7, 697–713, https://doi.org/10.5194/wes-7-697-2022,https://doi.org/10.5194/wes-7-697-2022, 2022
Short summary
Influence of wind turbine design parameters on linearized physics-based models in OpenFAST
Jason M. Jonkman, Emmanuel S. P. Branlard, and John P. Jasa
Wind Energ. Sci., 7, 559–571, https://doi.org/10.5194/wes-7-559-2022,https://doi.org/10.5194/wes-7-559-2022, 2022
Short summary

Cited articles

Al-Dousari, A., Al-Nassar, W., Al-Hemoud, A., Alsaleh, A., Ramadan, A., Al-Dousari, N., and Ahmed, M.:. Solar and wind energy: challenges and solutions in desert regions, Energy, 176, 184–194, 2019. 
Ancell, B. and Hakim, G. J.: Comparing adjoint- and ensemble-sensitivity analysis with applications to observation targeting, Mon. Weather Rev., 135, 4117–4134, https://doi.org/10.1175/2007MWR1904.1, 2007. 
Baker, R. W., Hewson, E. W., Butler, N. G., and Warchol, E. J.: Wind power potential in the Pacific Northwest, J. Appl. Meteorol., 17, 1814–1826, https://doi.org/10.1175/1520-0450(1978)017<1814:WPPITP>2.0.CO;2, 1978. 
Banta, R. M., Pichugina, Y. L., Kelley, N. D., Hardesty, R. M., and Brewer, W. A.: Wind Energy Meteorology: Insight into Wind Properties in the Turbine-Rotor Layer of the Atmosphere from High-Resolution Doppler Lidar, B. Am. Meteorol. Soc., 94, 883–902, https://doi.org/10.1175/bams-d-11-00057.1, 2013. 
Banta, R. M., Pichugina, Y. L., Brewer, W. A., James, E. P., Olson, J. B., Benjamin, S. G., Carley, J. R., Bianco, L., Djalalova, I. V., Wilczak, J. M., Hardesty, R. M., Cline, J., and Marquis, M. C.: Evaluating and Improving NWP Forecast Models for the Future: How the Needs of Offshore Wind Energy Can Point the Way, B. Am. Meteorol. Soc., 99, 1155–1176, https://doi.org/10.1175/bams-d-16-0310.1, 2018. 
Download
Short summary
Uncertainties in initial conditions (ICs) decrease the accuracy of wind speed forecasts. We find that IC uncertainties can alter wind speed by modulating the weather system. IC uncertainties in local thermal gradient and large-scale circulation jointly contribute to wind speed forecast uncertainties. Wind forecast accuracy in the Columbia River Basin is confined by initial uncertainties in a few specific regions, providing useful information for more intense measurement and modeling studies.
Altmetrics
Final-revised paper
Preprint