气象与环境学报2018，Vol.34Issue(1)：22-29,8.DOI:10.3969/j.issn.1673-503X.2018.01.003
三种数值模式气温预报产品的检验及误差订正方法研究
Research on air temperature product examination of three numerical forecast and a method of error correction
摘要
Abstract
Using the observed data and three numerical weather prediction products of T7 online (T7),ECMWF and T639,the air temperature accuracy rates and prediction errors were tested and analyzed from January of 2014 to December of 2015 in Benxi city.With the BP neural network algorithm,a air temperature prediction error cor-recting model was established based on the error analyses of the models.The results show that,for the annual test, the accuracy rate of the minimum air temperature is higher than that of the maximum air temperature for the three models.The forecasting effects of the minimum air temperatures in summer and autumn are better than those in winter and spring.While for the maximum air temperature predictions,the performance of T7 is superior to those of EC and T639.The predication accuracy rate of these models decreases under a large air temperature disturb-ance.The forecasting errors of average minimum air temperatures for these models are less than 2.00 ℃.There is a big difference in the maximum air temperature forecasting among these models.T7 has the smallest error in the maximum air temperature forecasting.The systematic deviation of T639 is higher,with -1.34 ℃ and -2.87 ℃for the minimum and maximum air temperatures,respectively.After correcting,the average absolute error decreases from 2.40 ℃ to 1.40 ℃,the systematic deviation and root-mean-square error are significantly reduced,and the forecasting accuracy rate obviously is improved from 50 % to 80 %.It indicates that this approach is valuable to be used in operation.关键词
BP神经网络/气温/误差/检验Key words
BP neural network/Air temperature/Error/Test分类
天文与地球科学引用本文复制引用
王焕毅,谭政华,杨萌,张翘,蒋林杉..三种数值模式气温预报产品的检验及误差订正方法研究[J].气象与环境学报,2018,34(1):22-29,8.基金项目
本溪市气象局项目"本溪冬季低于-20℃温度预报研究"(BXSQXJ201406)资助. （BXSQXJ201406）
