Analysis of wind prediction skills for the Winter Olympics playing area in Yanqing Beijing
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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。Abstract: In order to improve the forecast accuracy of 10 m wind, multiple models and verification methods are applied. Based on the ECMWF ensemble prediction system, the wind predict accuracy from direct model outputs at five stations over the Haituo mountain in Beijing is compared with that from four post-processing methods including one-variable regression, ridge regression, neural networks and particle swarm optimization-neural networks. The differences among these forecasts are discussed based on several verification methods. First, the verifications of systematic error and forecasting accuracy show that the prediction errors from regress and neural networks methods are much smaller than that from direct model outputs. Second, the wind frequency diagrams show that the forecast accuracy is improvement by regress methods in weak wind condition and by neural networks in the whole wind speed variance. The difference in the wind forecast varies greatly with wind direction for the different post-processing methods. The bias of wind direction forecast from direct model output in strong wind weather can be corrected by regress and neural networks methods. Finally, possible methods for improving wind forecast accuracy in complex terrain region are discussed.
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Key words:
- Wind /
- Regression /
- Neural networks /
- Winter Olympics
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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 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）
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）
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）
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）
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