| 基于数值模式的月尺度近地层气象要素预报技术研究 |
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| 引用本文： | 陈浩,何晓凤.基于数值模式的月尺度近地层气象要素预报技术研究[J].热带气象学报,2017,33(1):74-83. |
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| 作者姓名： | 陈浩 何晓凤 |
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| 作者单位： | 1.中国气象局公共气象服务中心, 北京100081 |
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| 基金项目： | 国家自然科学基金41305008气象关键技术集成与应用项目CMAGJ2015Z13 |
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| 摘 要： | 利用WRF模式对美国NCEP发布的CFS气候预测业务产品在中国区域内进行动力降尺度预报，可得到预报时效为45天的逐6小时、30 km分辨率基础气象要素预测产品。再利用全国气象站观测资料和3个风电场70 m高度风速、温度观测资料对2015年冬季预测结果进行检验评估和分析，最后通过线性方法对地面要素预测结果和70 m高度风速、温度预测结果进行统计订正。结果表明:（1）2 m温度和相对湿度的全国预报平均绝对误差分别为4.71 ℃和18.81%，在华东、华中和华南地区误差较小；（2）10 m风速预报平均绝对误差为2.42 m/s，在东北、华北和西北地区误差较小；（3）线性订正后，2 m气温、相对湿度和10 m风速的预报绝对误差分别减小1.05 ℃、5.29%和1.47 m/s，并且订正后误差随时间变化更平稳；（4）订正后70 m高度风速和温度的预报绝对误差均减小，风速平均误差减小最大可达1.29 m/s（B塔），气温平均绝对误差减小最大可达3 ℃（C塔）。研究结果表明，基于CFS产品和WRF模式的、与月尺度风电预报关系密切的气象要素预报性能较好，未来可将该方法尝试于风电场的月尺度功率预测产品研发。
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| 关 键 词： | 风电预报 月尺度 WRF CFS 统计订正 |
| 收稿时间： | 2016-09-12 |
A STUDY ON A MONTHLY FORECASTING METHOD OF METEOROLOGICAL ELEMENTS NEAR SURFACE BASED ON NUMERICAL MODEL |
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| Institution: | 1.Public Meteological Sevice Center of CMA, Beijing 100081, China2.Huaxintianli Energy and Meteorological Technology Center, Beijing 100081, China |
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| Abstract: | A non-hydrostatic Weather Research and Forecasting model (WRF) was used toconduct a downscaling monthly forecast based on the products of Climate Forecast System (CFS), made operational at NCEP for the winter from 1 December of 2014 to 28 February of 2015, for continental China to generate 6-hourly predictionsof meteorological elements witha horizontal resolution of 30×30 kilometersfor a valid period of 45 days. The downscaled temperature and relative humidity, both at the 2m height, and wind speed at the 10m height were validated against 2820 national meteorological stations from China Meteorological Administration, and temperature and wind speed at the 70m height were validated against 3 wind masts in Northern China, Southern China and Southwestern China. A linear approach was conducted to all forecasts to correct downscaling bias. The results are shown as follows: (1) The absolute errors of temperature and relative humidity at the 2m height, which was 4.71 ℃and 18.81%, respectively, were smaller over Eastern China, Middle China and Southern China. (2) The absolute errors of wind speed at the 2m height was 2.42 m/s and smaller over Northeastern China, Northern China and Northwestern China. (3) The linear correction reduced the temperature at the 2m height from WRF downscaling forecasts by 1.05 ℃ and relative humidity at the 2m height by 5.29% and wind speed at the 10m height by 1.47 m/s. Besides, statistical correction smoothed forecast bias of temperature, relative humidity and wind speed over the 45 days. (4) The linear corrected wind speed and temperature bias at the 70m height decreased wind speed by 1.29 m/s at thewind mast of site B and temperature by 3 ℃ at the wind mast of site C. This paper presents a promising case for monthly forecast in wind power by using RCM downscaling nested in the CGCM. |
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