| 摘要: |
| 运用概率统计的方法，检验了的ECWMF、GRAPES和JAPAN模式对于贵州省范围内84个四个站点的风速预报的平均绝对误差、标准差、相关系数和正确率，EC模式平均误差在-1.9～1.7之间，GAPES模式在-3.1～0之间，JAPAN模式在-3～1.9之间；平均绝对误差EC模式在0.6～2.1之间，GAPES模式在1.1～3.3之间，JAPAN模式在0.6～3.1之间，全省站点相关系数平均数EC模式和JAPAN模式为0.5，GRAPES模式为0.4，风速预报误差绝对值为2m?s-1的准确率全省平均值EC为83%，最低为织金的54%；GRAPES模式为56%%，最低为长顺的32%，JAPAN模式为75%，最低为长顺的29%。并分9个地州对三个模式的预报效果预报进行对比，发现EC模式的预报效果明显优于其他两个模式。其中，EC模式对铜仁地区的预报效果最好，黔西南的预报效果最差，GRAPES模式对遵义地区的预报效果最好，毕节地区的预报效果最差；JAPAN模式对遵义地区的预报效果最好，黔西南地区的预报效果最差。三个模式中有两个在黔西南地区的预报效果不佳。运用BP神经网络方式对三种模式风速预报进行订正，对于三个模式订正后的预报效果均有明显改善，其中误差、正确率的改善较为明显，相关系数的提高较小。 |
| 关键词: EC模式；GAPES模式；JANPA模式；检验；订正 |
| DOI： |
| 投稿时间：2019-05-27修订日期：2019-08-05 |
| 基金项目:数值预报风速产品在贵州的检验及订正(黔气科登[2018]07-07号) |
|
| Verification and Revision of Numerical Models for Wind Speed Forecast in Guizhou Province |
| Xia Xiaoling,Shang yuanyuan,Zheng yi |
| (Guizhou Meteorological Service Center) |
| Abstract: |
| ABSTRACT: In this paper, the average absolute error, standard deviation, correlation coefficient and accuracy of ECWMF, GRAPES and JAPAN models for wind speed prediction of 84 stations in Guizhou Province are tested by statistics. The average error of EC model is between - 1.9 and 1.7, GAPES model is between - 3.1 and 0, JAPAN model is between - 3 and 1.9, and the average absolute error of EC model is between 0.6 and 2.1. Among them, GAPES model is between 1.1 and 3.3, JAPAN model is between 0.6 and 3.1, EC model and JAPAN model are 0.5, GRAPES model is 0.4, the accuracy of wind speed prediction error absolute value is 2 m * S-1 is 83%, the lowest is 54% of weaving gold, GRAPES model is 56%, the lowest is 32% of Changshun, JAPAN model is 75%, the lowest is 29% of Changshun. By comparing the forecasting results of the three models in 9 prefectures, it is found that the forecasting effect of the EC model is better than that of the other two models. Among them, EC model has the best forecasting effect in Tongren area, southwest Guizhou is the worst, GRAPES model has the best forecasting effect in Zunyi area and Bijie area is the worst; JAPAN model has the best forecasting effect in Zunyi area and the worst forecasting effect in southwest Guizhou. Two of the three models have poor forecasting effect in southwestern Guizhou. The BP neural network is used to revise the wind speed prediction of the two models, and the results of the three models are improved obviously. The improvement of error and accuracy is obvious, and the improvement of correlation coefficient is small. |
| Key words: EC;mode, GAPES;mode, JANPA;mode, Examination, Correction |
