| 中国沿海风能分布特征及其影响因子的数值模拟 |
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| 引用本文： | 孙玉婷,粘新悦,闵锦忠,王世璋,乔小湜.中国沿海风能分布特征及其影响因子的数值模拟[J].大气科学学报,2017,40(6):823-832. |
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| 作者姓名： | 孙玉婷 粘新悦 闵锦忠 王世璋 乔小湜 |
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| 作者单位： | 南京信息工程大学 气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心, 江苏 南京 210044;南京信息工程大学 大气科学学院, 江苏 南京 210044;湖北省公众气象服务中心, 湖北 武汉 430074;南京信息工程大学 气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心, 江苏 南京 210044;南京信息工程大学 大气科学学院, 江苏 南京 210044;南京信息工程大学 气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心, 江苏 南京 210044;南京信息工程大学 大气科学学院, 江苏 南京 210044;南京信息工程大学 气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心, 江苏 南京 210044;南京信息工程大学 大气科学学院, 江苏 南京 210044;南京信息工程大学 气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心, 江苏 南京 210044;南京信息工程大学 大气科学学院, 江苏 南京 210044 |
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| 基金项目： | 国家重点基础研究发展计划（973计划）项目（2013CB430102）；中国气象局武汉暴雨研究所暴雨开放基金项目（HR2008K01） |
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| 摘 要： | 利用欧洲中期天气预报中心0.75°×0.75°再分析资料,对中国海岸线两侧相邻区域内的风能、风速进行研究,讨论不同季节、不同区域风能、风速的分布特征;利用WRF(Weather Research Forecast)模式模拟海表面温度上升和城市化发展对中国东部沿海风能的影响。结果表明:1)中国沿海风能的时空分布不均一,季节变化明显。春季渤海湾区域风能明显大于其他三区(华东沿海、东南沿海和南海北部沿海区域)。夏季渤海湾区域风能显著小于其他三区,而华东沿海区域风能稍大。秋季东南沿海和南海北部沿海区域风能较大。冬季沿海四区风能大小接近。一般而言,秋冬季风能较大、春夏季风能较小,夏季风能显著小于冬季。2)不同区域、不同季节风速的年际变化存在明显差异。除冬季东南沿海区域风速有增大趋势外,其他区域各季节风速都呈缓慢减小趋势,但减小幅度很小。3)海表温度升高在不同季节对风速的影响不同。春季渤海湾和山东半岛、北部湾沿海及杭州湾风速随海温升高而增强。夏季海温升高幅度不同,则风速显著变化区域不同,但大部分沿海区域风速随海温升高而增强。秋冬季风速随海表温度升高而增强,影响区域较稳定:秋季东南沿海和华东沿海区域风速增强,冬季渤海湾和南海北部沿海区域风速增强。4)城市化发展增大了地表摩擦力,使得夏秋季登陆我国的热带气旋迅速减弱,沿海风速随之减小。
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| 关 键 词： | 风能 海表温度 城市化 数值模拟 时空分布 中国沿海 |
| 收稿时间： | 2015/6/8 0:00:00 |
| 修稿时间： | 2017/1/24 0:00:00 |
Distribution characteristics of wind energy along the coast of China and numerical simulation on impact factors |
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| SUN Yuting,NIAN Xinyue,MIN Jinzhong,WANG Shizhang and QIAO Xiaoshi.Distribution characteristics of wind energy along the coast of China and numerical simulation on impact factors[J].大气科学学报,2017,40(6):823-832. |
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| Authors: | SUN Yuting NIAN Xinyue MIN Jinzhong WANG Shizhang and QIAO Xiaoshi |
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| Institution: | Key Laboratory of Meteorological Disaster, Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China;School of Atmospheric Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China;Center for Hubei Public Weather Service, Wuhan 430074, China;Key Laboratory of Meteorological Disaster, Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China;School of Atmospheric Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China;Key Laboratory of Meteorological Disaster, Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China;School of Atmospheric Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China;Key Laboratory of Meteorological Disaster, Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China;School of Atmospheric Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China;Key Laboratory of Meteorological Disaster, Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China;School of Atmospheric Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China |
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| Abstract: | Based on the reanalysis data with horizontal resolution of 0.75°×0.75° from the European Center of Medium-range Weather Forecast(ECMWF),this paper investigates the wind energy and wind speed in the narrow area along the coastal line of China,and discusses the distribution characteristics of wind energy and wind speed in different regions and different seasons.The Weather Research Forecast(WRF) model is used to simulate the influence of sea surface temperature(SST) increase and urbanization development on wind energy in east coast of China.Results show that:(1)The wind energy has significant spatiotemporal differences among different coastal regions of China,with obvious seasonal variations.In spring,the wind energy in Bohai gulf is obviously larger than that in other regions(coastal areas of East China,southeastern China and north of South China Sea).In summer,the wind energy in Bohai gulf is clearly smaller than that in other regions and it is slightly larger in coastal area of East China.In autumn,the wind energy in coastal areas of southeastern China and north of South China Sea is significantly larger.In winter,the wind energy is similar in the four coastal regions of China.Generally,the wind energy is larger(smaller) in autumn and winter(spring and summer),and the wind energy in summer is significantly smaller than that in winter.(2)Interannual variation of wind speed has obvious differences in different coastal areas of China and different seasons.Besides the wind speed has an increasing trend in coastal area of southeastern China in winter,it has a slow decreasing trend in other areas in each season,but the range of reduction is very small.(3)Numerical simulations show that the effects of SST increasing on the wind speed in different seasons are different.The wind speed in Bohai Gulf,Shandong peninsula,Beibu Gulf coast and Hangzhou Bay increases with the increase of SST in spring.The regions of significant wind speed change are different with different SST increasing in summer,but the wind speed in most coastal areas increases with the increase of SST.The increasing of SST has great impacts on the wind speed in summer and autumn.Wind speed increases with SST increasing in autumn and winter,with more stable areas of influence.The wind speed increases in coastal areas of southeastern China and East China in autumn,and it does in coastal areas of Bohai gulf and north of South China Sea in winter.(4)The development of urbanization results in the increases of surface friction force,causing the tropical cyclone that lands on China is rapidly weakening in summer and autumn,in such a way that the wind speed decreases in coastal areas of China. |
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| Keywords: | wind energy SST urbanization numerical simulation spatiotemporal distribution China coast |
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