留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

冬奥延庆赛区风的预报技巧比较分析

熊敏诠 冯文 刘凑华

熊敏诠,冯文,刘凑华. 2022. 冬奥延庆赛区风的预报技巧比较分析. 气象学报,80(2):289-303 doi: 10.11676/qxxb2022.018
引用本文: 熊敏诠,冯文,刘凑华. 2022. 冬奥延庆赛区风的预报技巧比较分析. 气象学报,80(2):289-303 doi: 10.11676/qxxb2022.018
Xiong Minquan, Feng Wen, Liu Couhua. 2022. Analysis of wind prediction skills for the Winter Olympics playing area in Yanqing Beijing. Acta Meteorologica Sinica, 80(2):289-303 doi: 10.11676/qxxb2022.018
Citation: Xiong Minquan, Feng Wen, Liu Couhua. 2022. Analysis of wind prediction skills for the Winter Olympics playing area in Yanqing Beijing. Acta Meteorologica Sinica, 80(2):289-303 doi: 10.11676/qxxb2022.018

冬奥延庆赛区风的预报技巧比较分析

doi: 10.11676/qxxb2022.018
基金项目: 海南省南海气象防灾减灾重点实验室开放基金项目(SCSF202005)、国家气象中心预报员专项(Y202134)
详细信息
    作者简介:

    熊敏诠,主要从事气象预测工作。E-mail:minquanxiong@sina.com

  • 中图分类号: P425 P457.5

Analysis of wind prediction skills for the Winter Olympics playing area in Yanqing Beijing

  • 摘要: 为了提高2 min平均的10 m风预报精度,开展了多种建模和检验方法比较。根据欧洲数值中心集合预报系统产品及北京海陀山的5个测站资料,使用一元回归、岭回归、神经网络、粒子群-神经网络等方法建模,进行2021年2月逐日的未来3天6 h间隔预报误差订正,并从多个角度分析预报精度差异。结果为:(1)系统误差、预报准确率检验表明,建模订正后的预报误差均明显减小。(2)频率关系图揭示,回归法在弱风区(大概率事件)有较好的订正能力,神经网络法在不同风速区都有正向的订正效果。(3)大风过程预报的对比显示,建模方法能有效订正风向的预报。

     

  • 图 1  (a) 格点 (红点)、测站 (蓝点) 位置和地形;(b) 放大后的测站 (红点) 位置

    Figure 1.  (a) The locations of grids (red) and stations (blue),(b) the enlarged topography of observed stations (red points)

    图 2  岭迹

    Figure 2.  Ridge trace

    图 3  (a) 东西风分量的预报平均误差,(b) 南北风分量的预报平均误差,(c) 风向预报准确率,(d) 风速预报准确率

    Figure 3.  (a) Mean errors of east-west wind component forecast,(b) mean errors of south-north wind component forecast,(c) accuracy rates of wind direction forecast, and (d) accuracy rates of wind speed forecast of five stations

    图 4  不同测点 (a.1489站, b.1701站, c.1703站,d.1705站,e.1708站) 的合成风频率关系 (a1—e1. 观测和预报概率分布函数对比,a2—e2. 频率匹配映射关系)

    Figure 4.  Frequency diagram:probability distribution function (a1—e1),frequency matching mapping (a2—e2) at five stations (a. 1489,b. 1701,c. 1703, d. 1705,e. 1708)

    Continued

    图 5  不同测点的东西分量和南北分量的频率关系 (a. 1703站东西风分量, b. 1705站东西风分量, c. 1703站南北风分量,d. 1705站南北风分量; a1—d1. 概率分布函数,a2—d2. 概率匹配映射关系)

    Figure 5.  Frequency diagram:probability distribution (a1—d1),probability match (a2—d2)(a. east-west wind component at 1703, b. east-west wind component at 1705,c. south-north wind component at 1703,d. south-north wind component at 1705)

    Continued

    图 6  分别用DMO (a1—e1)、RID (a2—e2) 和 PSO (a3—e3) 方法计算的5个台站的风矢量分布统计 (a. 1489站,b. 1701站,c. 1703站,d. 1705站,e. 1708站)

    Figure 6.  Distributions of wind forecasts by DMO (a1—e1),RID (a2—e2) and PSO (a3—e3) methods at the stations of 1489 (a), 1701(b), 1703(c),1705 (d) and 1708 (e)

    Continued

    图 7  2021年2月15—21日的风(单位:m/s)预报准确率和稳定性对比 (a—c. 1701站的DMO、RID、PSO法预报,d—f. 1705站的DMO、RID、PSO法预报;a1—f1. 预报误差 (预报减实况),a2—f2. 实况和预报)

    Figure 7.  Accuracy and consistency distributions of wind(unit:m/s) forecast from 15 to 21 Feb 2021 (a—c. forecasts by DMO,RID and PSO methods at 1701,d—f. forecasts by DMO,RID and PSO methods at 1705; a1—f1. forecast error,a2—f2. observation and forecast)

    Continued

  • [1] 黄嘉佑,李庆祥. 2007. 一种诊断序列非均一性的新方法. 高原气象,26(1):62-66 doi: 10.3321/j.issn:1000-0534.2007.01.007

    Huang J Y,Li Q X. 2007. A new diagnosis method for non-homogeneity on a series. Plateau Meteor,26(1):62-66 (in Chinese) doi: 10.3321/j.issn:1000-0534.2007.01.007
    [2] 贾春晖,窦晶晶,苗世光等. 2019. 延庆-张家口地区复杂地形冬季山谷风特征分析. 气象学报,77(3):475-488 doi: 10.11676/qxxb2019.033

    Jia C H,Dou J J,Miao S G,et al. 2019. Analysis of characteristics of mountain-valley winds in the complex terrain area over Yanqing-Zhangjiakou in the winter. Acta Meteor Sinica,77(3):475-488 (in Chinese) doi: 10.11676/qxxb2019.033
    [3] 李庆祥. 2020. 用外部强迫因子对近百年陆地降水变化的统计建模试验. 科学通报,65(21):2266-2278 doi: 10.1360/TB-2020-0305

    Li Q X. 2020. Statistical modeling experiment of land precipitation variations since the start of the 20th century with external forcing factors. Chinese Sci Bull,65(21):2266-2278 (in Chinese) doi: 10.1360/TB-2020-0305
    [4] 王振会,丁裕国,周胜鹏. 1994. 利用PRESS准则和岭回归方法建立大气遥感最优反演方程. 南京气象学院学报,17(1):101-109

    Wang Z H,Ding Y G,Zhou S P. 1994. Establishment of the optimum retrieval equation for atmospheric remote-sensing based on press criterion and ridge regression. J Nanjing Inst Meteor,17(1):101-109 (in Chinese)
    [5] 魏凤英,黄嘉佑. 2010. 大气环流降尺度因子在中国东部夏季降水预测中的作用. 大气科学,34(1):202-212 doi: 10.3878/j.issn.1006-9895.2010.01.19

    Wei F Y,Huang J Y. 2010. A study of downscaling factors of atmospheric circulations in the prediction model of summer precipitation in eastern China. Chinese J Atmos Sci,34(1):202-212 (in Chinese) doi: 10.3878/j.issn.1006-9895.2010.01.19
    [6] 魏鸣,管理,梁学伟等. 2019. 基于支持向量机的雷达地物回波识别研究. 大气科学学报,42(4):631-640

    Wei M,Guan L,Liang X W,et al. 2019. Ground clutter identification based on the support vector machine method with Doppler weather radar data. Trans Atmos Sci,42(4):631-640 (in Chinese)
    [7] 乌日柴胡,王建捷,孙靖等. 2019. 北京山区与平原冬季近地面风的精细观测特征. 气象学报,77(6):1107-1123 doi: 10.11676/qxxb2019.059

    Wu R C H,Wang J J,Sun J,et al. 2019. An observational investigation of fine features of near surface winds in winter over Beijing area. Acta Meteor Sinica,77(6):1107-1123 (in Chinese) doi: 10.11676/qxxb2019.059
    [8] 熊敏诠. 2017. 基于集合预报系统的日最高和最低气温预报. 气象学报,75(2):211-222 doi: 10.11676/qxxb2017.023

    Xiong M Q. 2017. Calibrating daily 2 m maximum and minimum air temperature forecasts in the ensemble prediction system. Acta Meteor Sinica,75(2):211-222 (in Chinese) doi: 10.11676/qxxb2017.023
    [9] 熊敏诠,代刊,唐健. 2020. 春季中国日最高气温延伸期预报误差分析及订正. 热带气象学报,36(6):795-804

    Xiong M Q,Dai K,Tan J. 2020. Analyzing and calibrating extended-range forecast of China's daily maximum temperature in spring. J Trop Meteor,36(6):795-804 (in Chinese)
    [10] Bao L,Gneiting T,Grimit E P,et al. 2010. Bias correction and Bayesian model averaging for ensemble forecasts of surface wind direction. Mon Wea Rev,138(5):1811-1821 doi: 10.1175/2009MWR3138.1
    [11] Clerc M,Kennedy J. 2002. The particle swarm-explosion,stability,and convergence in a multidimensional complex space. IEEE Trans Evol Comput,6(1):58-73 doi: 10.1109/4235.985692
    [12] Gibbons D G. 1981. A simulation study of some ridge estimators. J Amer Stat Assoc,76(373):131-139 doi: 10.1080/01621459.1981.10477619
    [13] Glahn H R,Lowry D A. 1972. The use of Model Output Statistics (MOS) in objective weather forecasting. J Appl Meteor,11(8):1203-1211 doi: 10.1175/1520-0450(1972)011<1203:TUOMOS>2.0.CO;2
    [14] Gneiting T,Raftery A E,Westveld A H,et al. 2005. Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon Wea Rev,133(5):1098-1118 doi: 10.1175/MWR2904.1
    [15] Gneiting T,Stanberry L I,Grimit E P,et al. 2008. Assessing probabilistic forecasts of multivariate quantities,with an application to ensemble predictions of surface winds. TEST,17(2):211-235 doi: 10.1007/s11749-008-0114-x
    [16] Hamill T M,Whitaker J S. 2006. Probabilistic quantitative precipitation forecasts based on reforecast analogs:Theory and application. Mon Wea Rev,134(11):3209-3229 doi: 10.1175/MWR3237.1
    [17] He X G,Guan H D,Qin J X. 2015. A hybrid wavelet neural network model with mutual information and particle swarm optimization for forecasting monthly rainfall. J Hydrol,527:88-100 doi: 10.1016/j.jhydrol.2015.04.047
    [18] Holman B P,Lazarus S M,Splitt M E. 2018. Statistically and dynamically downscaled,calibrated,probabilistic 10-m wind vector forecasts using ensemble model output statistics. Mon Wea Rev,146(9):2859-2880 doi: 10.1175/MWR-D-17-0338.1
    [19] Kennedy J,Eberhart R. 1995. Particle swarm optimization∥Proceedings of the First IEEE International Conference on Neural Networks. Perth,Australia:IEEE,1942-1948
    [20] Klausner Z,Kaplan H,Fattal E. 2009. The similar days method for predicting near surface wind vectors. Meteor Appl,16(4):569-579 doi: 10.1002/met.158
    [21] Kretzschmar R,Ecker P,Cattani D,et al. 2004. Neural network classifiers for local wind prediction. J Appl Meteor Climatol,43(5):727-738 doi: 10.1175/2057.1
    [22] Kwong K M,Wong M H Y,Liu J N K,et al. 2012. An artificial neural network with chaotic oscillator for wind shear alerting. J Atmos Ocean Technol,29(10):1518-1531 doi: 10.1175/2011JTECHA1501.1
    [23] Lorenz E N. 1963. Deterministic nonperiodic flow. J Atmos Sci,20(2):130-141 doi: 10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2
    [24] Marzban C,Stumpf G J. 1996. A neural network for tornado prediction based on Doppler radar-derived attributes. J Appl Meteor,35(5):617-626 doi: 10.1175/1520-0450(1996)035<0617:ANNFTP>2.0.CO;2
    [25] Marzban C,Stumpf G J. 1998. A neural network for damaging wind prediction. Wea Forecasting,13(1):151-163 doi: 10.1175/1520-0434(1998)013<0151:ANNFDW>2.0.CO;2
    [26] Masters T. 1993. Practical Neural Network Recipes in C++. New York:Academic Press,493pp
    [27] McCann D W. 1992. A neural network short-term forecast of significant thunderstorms. Wea Forecasting,7(3):525-534 doi: 10.1175/1520-0434(1992)007<0525:ANNSTF>2.0.CO;2
    [28] Monache L D,Anthony E F,Rife D L,et al. 2013. Probabilistic weather prediction with an analog ensemble. Mon Wea Rev,141(10):3498-3516 doi: 10.1175/MWR-D-12-00281.1
    [29] Scheuerer M,Möller D. 2015. Probabilistic wind speed forecasting on a grid based on ensemble model output statistics. Ann Appl Stat,9(3):1328-1349
    [30] Schuhen N,Thorarinsdottir T L,Gneiting T. 2012. Ensemble model output statistics for wind vectors. Mon Wea Rev,140(10):3204-3219 doi: 10.1175/MWR-D-12-00028.1
    [31] Sheikhan M,Mohammadi N. 2013. Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data. Neural Comput Appl,23(3-4):1185-1194 doi: 10.1007/s00521-012-0980-8
    [32] Silva M T,Gill E W,Huang W M. 2018. An improved estimation and gap-filling technique for sea surface wind speeds using NARX neural networks. J Atmos Ocean Technol,35(7):1521-1532 doi: 10.1175/JTECH-D-18-0001.1
    [33] Sloughter J M,Gneiting T,Raftery A E. 2010. Probabilistic wind speed forecasting using ensembles and Bayesian model averaging. J Amer Stat Assoc,105(489):25-35 doi: 10.1198/jasa.2009.ap08615
    [34] Thorarinsdottir T L,Gneiting T. 2010. Probabilistic forecasts of wind speed:Ensemble model output statistics by using heteroscedastic censored regression. J Roy Stat Soc:Ser A (Stat Soc),173(2):371-388 doi: 10.1111/j.1467-985X.2009.00616.x
    [35] Veldkamp S,Whan K,Dirksen S,et al. 2021. Statistical postprocessing of wind speed forecasts using convolutional neural networks. Mon Wea Rev,149(4):1141-1152 doi: 10.1175/MWR-D-20-0219.1
    [36] Xiong M Q,Dai K. 2020. The optimization algorithm based on neural networks in post-processing ensemble forecasts∥Wang Y,Fu M X,Xu L X,et al. Signal and Information Processing,Networking and Computers. Singapore:Springer,772-780
  • 加载中
图(11)
计量
  • 文章访问数:  164
  • HTML全文浏览量:  30
  • PDF下载量:  75
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-05-19
  • 录用日期:  2022-03-02
  • 修回日期:  2021-11-13
  • 网络出版日期:  2022-03-15

目录

    /

    返回文章
    返回