Quantifying sources of uncertainty in reanalysis derived wind speed |
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| Affiliation: | 1. Tepper School of Business, Carnegie Mellon University, Pittsburgh, USA;2. Department of Engineering & Public Policy, Carnegie Mellon University, Pittsburgh, USA;1. Key Laboratory of Solar Thermal Energy and Photovoltaic System, Institute of Electrical Engineering, Chinese Academy of Sciences, Zhongguancun, Haidian, Beijing, 100190, China;2. China Electric Power Research Institute, Nanjing, 210003, Jiangsu, China;3. University of Chinese Academy of Sciences, Beijing, 100049, China;1. Centre for Energy Sciences, Department of Mechanical Engineering, Faculty of Engineering, 50603 Kuala Lumpur University of Malaya, Malaysia;2. Mechanical Engineering Department, Collage of Engineering, King Saud University, 11421 Riyadh, Saudi Arabia;3. Dept. of Mechanical Engineering, Dhaka University of Engineering and Technology, Gazipur, 1700, Bangladesh;1. Division of Energy Technology, School of Energy, Environment and Materials, King Mongkut''s University of Technology Thonburi, Bangkok, 10140, Thailand;2. Solar Energy Research Laboratory, Department of Physics, Faculty of Science, Silpakorn University, Nakhon Pathom, 73000, Thailand;1. Huadian Electric Power Research Institute, Hangzhou, Zhejiang 310030, China;2. Department of Energy Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China;3. Key Laboratory of Thermo-Fluid Science and Engineering, Ministry of Education, School of Energy and Power Engineering, Xi''an Jiaotong University, Xi''an 710049, China;1. South China University of Technology, Wushan Road 381#, Tianhe District, Guangzhou 510640, China;2. Jiangsu University, Jiangsu, Xuefu Road 301#, Jingkou District, Zhenjiang 212013, China |
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| Abstract: | Reanalysis data are attractive for wind-power studies because they can offer wind speed data for large areas and long time periods and in locations where historical data are not available. However, reanalysis-predicted wind speeds can have significant uncertainties and biases relative to measured wind speeds. In this work we develop a model of the bias and uncertainty of CFS reanalysis wind speed than can be used to correct the data and identify sources of error. We find the CFS reanalysis data underestimate wind speeds at high elevations, at high measurement heights, and in unstable atmospheric conditions. For example, at a site with an elevation of 500 m and hub height of 80 m, a CFS reanalysis wind speed of 8 m/s is 0.2 m/s higher to 1.3 m/s lower than the measured wind speed. We also find a seasonal bias that correlates with surface roughness length used by the reanalysis model during the spring season. The corrections we propose reduce the average bias of reanalysis wind speed extrapolated to hub height to nearly zero, an improvement of 0.3–0.9 m/s. These corrections also reduce the RMS error by 0.1–0.4 m/s, a small improvement compared to the uncorrected RMS errors of 1.5–2.4 m/s. |
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| Keywords: | CFS reanalysis Wind integration Linear mixed-effect model |
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