A STUDY ON A MONTHLY FORECASTING METHOD OF METEOROLOGICAL ELEMENTS NEAR SURFACE BASED ON NUMERICAL MODEL
-
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。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.
-
Key words:
- wind electricity forecast /
- monthly scale /
- WRF /
- CFS /
- statistical correction
-
表 1 全国和不同区域地表要素绝对误差
地表要素 全国 华东 华中 华北 东北 西北 华南 西南 青藏 2 m温度/℃ 4.57 3.58 3.85 4.32 5.65 4.89 3.92 5.37 7.51 2 m相对湿度/% 18.69 14.94 16.36 21.17 20.09 18.50 14.57 20.60 36.10 10 m风速/(m/s) 2.48 2.80 2.55 2.26 2.27 1.96 2.95 2.79 3.08 
下载: 导出CSV
-
[1] 任俊龙.区域中长期风电发电量概率预测方法[D].大连:大连理工大学, 2014: 1-3. [2] 常蕊, 朱蓉, 柳艳香, 等.基于均生函数的风电场风速短临预报模型[J].气象, 2013, 39(2): 226-233. [3] 孙川永.风电场风电功率短期预报技术研究[D].兰州:兰州大学, 2009: 5-9. [4] 刘永前, 韩爽, 胡永生.风电场出力短期预报研究综述[J].现代电力, 2007, 24(5): 6-11. [5] 韩爽.风电场功率短期预测方法研究[D].北京:华北电力大学, 2008: 3-4. [6] 孟祥星, 田成微, 冬雷, 等.灰色理论用于风力发电容量中长期预测的研究[J].电力系统保护与控制, 2011, 39(21): 81-85. [7] 田武文, 吴素良, 王娜.月尺度降水的客观预测方法研究[J].高原气象, 2010, 29(4): 1 072-1 077. [8] 王慧娟, 吴洪星, 仵建勋.降尺度方法在月预报中的应用研究[J].气象水文海洋仪器, 2011(1): 27-31. [9] 王冀, 宋瑞艳, 郭文利.统计降尺度方法在北京月尺度预测中的应用[J].气象, 2011, 37(6): 693-700. [10] 金荣花, 马杰, 毕宝贵. 10~30d延伸期预报研究进展和业务现状[J].沙漠与绿洲气象, 2010, 4(2): 1-5. [11] 康志明, 鲍媛媛, 周宁芳.我国中期和延伸期预报业务现状以及发展趋势[J].气象科技进展, 2013, 3(1): 18-24. [12] MURPHY J. An evaluation of statistical and dynamical techniques for downscaling local climate[J]. J Clim, 12(8): 2 256-2 284. [13] BOE J, L. TERRAY L, HABETS F, et al. Statistical and dynamical downscaling of the Seine basin climate for hydro-meteorological studies[J]. Int J Climatol, 2007, 27(12): 1 643-1 655. [14] WANG X L, SWAIL V R, COX A. Dynamical versus statistical downscaling methods for ocean waveheights[J]. Int J Climatol, 2010, 30(3): 317-332. [15] SALATHE E P, STEED R, MASS C F, et al. A high-resolution climate model for the U.S. Pacific Northwest: Mesoscale feedbacks and local responses to climate change[J]. J Clim, 2008, 21(21): 5 708-5 726. [16] WANG W, C B, MICHAEL D, et al. ARW Users Guide V3-20130107[Z].http://www2.mmm.ucar.edu/wrf/users/docs/user_guide_V3.4/ARWUsersGuideV3.pdf, 2012, 5.1-5.3. [17] 肖玉华, 康岚, 徐琳娜, 等.西南区域中尺度数值模式预报性能及其与天气过程关系初探[J].气象, 2013, 39(10):1 258-1 264. [18] YUAN X, LIANG X Z, WOOD E F. WRF ensemble downscaling seasonal forecasts of China winter precipitation during 1982—2008[J]. Clim Dyn, 2011, 39(7): 2 041-2 058. [19] SAHA S, MOORTHI S, WU X R, et al. The NCEP climate forecast system version 2[J]. J Clim, 2014, 27(6): 2 185-2 208. [20] LUO L, TANG W, LIN Z, et al. Evaluation of summer temperature and precipitation predictions from NCEP CFSv2 retrospective forecast over China[J]. Clim Dyn, 2013, 41(7-8): 2 213-2 230. [21] LANG Y, YE A, GONG W, et al. Evaluating skill of seasonal precipitation and temperature predictions of NCEP CFSv2 forecasts over 17 hydroclimatic regions in China[J]. J Hydrometeorology, 2014, 15(4): 1 546-1 559. [22] 乐群, 曹俊武, 林振山, 等.中国月平均温度的气候噪声和潜在可预报性[J].气象学报, 1999, 57(5): 604-612. [23] 孙逸涵, 何晓凤, 周荣卫.不同背景场近地层风速的预报效果检验[J].资源科学, 2013, 35(12): 2 481-2 490. [24] 王婧, 徐枝芳, 范广洲, 等. GRAPES_RAFS系统2m温度偏差订正方法研究[J].气象, 2015, 41(6): 719-726. [25] 何晓凤, 周荣卫, 孙逸涵. 3个全球模式对近地层风场预报能力的对比检验[J].高原气象, 2014, 33(5): 1 316-1 322. -
点击查看大图
图(11) / 表(1)
计量
- 文章访问数: 896
- HTML全文浏览量: 44
- PDF下载量: 467
- 被引次数: 0
粤公网安备 4401069904700003号