GSTDTAP  > 地球科学
DOI10.1016/j.atmosres.2019.04.011
Improving WRF model turbine-height wind-speed forecasting using a surrogate- based automatic optimization method
Di, Zhenhua1; Ao, Juan2; Duan, Qingyun1; Wang, Jin2; Gong, Wei1; Shen, Chenwei1; Gan, Yanjun3; Liu, Zhao2
2019-09-15
发表期刊ATMOSPHERIC RESEARCH
ISSN0169-8095
EISSN1873-2895
出版年2019
卷号226页码:1-16
文章类型Article
语种英语
国家Peoples R China
英文摘要

Improving turbine-height wind-speed forecasting using a mesoscale numerical weather prediction (NWP) model is important for wind-power prediction because of the cubic correlation between wind power and wind speed. This study investigates how a surrogate-based automatic optimization method can be used to improve wind-speed forecasting by an NWP model by optimizing its parameters. A key challenge in optimizing NWP model parameters is the tremendous computational requirements of such an exercise. A global sensitivity method known as the Multivariate Adaptive Regression Spline (MARS) method was first used to identify the most sensitive parameters among all tunable parameters chosen from seven physical parameterization schemes of the Weather Research and Forecast (WRF) model. Then, a highly effective and efficient optimization method known as adaptive surrogate modeling-based optimization (ASMO) was used to tune the sensitive parameters. In a case study carried out over Eastern China, the seven parameters that were most sensitive to wind-speed simulation were identified from among 27 tunable parameters. Those seven parameters were optimized using the ASMO method. The present study indicates that the hourly wind-speed simulation accuracy was improved by 8.7% during the calibration phase and by 7.58% during the validation phase. In addition, clear physical interpretations were provided to explain why the optimal parameters lead to improved wind speed forecasts. Overall, this study has demonstrated that automatic optimization method is a highly effective and efficient way to improve wind-speed forecasting by an NWP model.


英文关键词WRF Parameter optimization Surrogate modeling-based optimization Turbine-height wind-speed forecasting
领域地球科学
收录类别SCI-E
WOS记录号WOS:000469904100001
WOS关键词PLANETARY BOUNDARY-LAYER ; CONVECTIVE PARAMETERIZATION ; SENSITIVITY-ANALYSIS ; WEATHER RESEARCH ; PBL SCHEMES ; ENSEMBLE ; SYSTEM ; SIMULATION
WOS类目Meteorology & Atmospheric Sciences
WOS研究方向Meteorology & Atmospheric Sciences
引用统计
文献类型期刊论文
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/187030
专题地球科学
作者单位1.Beijing Normal Univ, Fac Geog Sci, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China;
2.Beijing Goldwind Sci & Creat Windpower Equipment, Beijing 100176, Peoples R China;
3.Chinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing 100081, Peoples R China
推荐引用方式
GB/T 7714
Di, Zhenhua,Ao, Juan,Duan, Qingyun,et al. Improving WRF model turbine-height wind-speed forecasting using a surrogate- based automatic optimization method[J]. ATMOSPHERIC RESEARCH,2019,226:1-16.
APA Di, Zhenhua.,Ao, Juan.,Duan, Qingyun.,Wang, Jin.,Gong, Wei.,...&Liu, Zhao.(2019).Improving WRF model turbine-height wind-speed forecasting using a surrogate- based automatic optimization method.ATMOSPHERIC RESEARCH,226,1-16.
MLA Di, Zhenhua,et al."Improving WRF model turbine-height wind-speed forecasting using a surrogate- based automatic optimization method".ATMOSPHERIC RESEARCH 226(2019):1-16.
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