摘要
基于德国天气在线T7online(简称T7)、ECMWF细网格(简称EC)及T639三种数值模式的气温预报产品,结合本溪站气象观测资料,对三种数值模式2014年1月至2015年12月本溪市气温预报的准确率及预报误差进行了检验和分析,根据误差分析结果利用BP神经网络模型建立了本溪市数值模式气温预报误差客观化订正模型。结果表明:对于气温预报的年检验,T7、EC和T639三种数值模式的最低气温预报准确率均高于最高气温的预报准确率;对于气温预报的月检验,三种数值模式对夏季、秋季最低气温的预报效果明显优于冬季和春季,而对于最高气温的预报,T7的气温预报准确率明显优于EC和T639模式;当气温波动较大时,三种数值模式气温的预报准确率均明显下降。三种数值模式对最低气温预报的平均误差均为2.00℃以内,对最高气温的预报准确率存较大差别,T7模式最高气温的预报误差最小,T639模式气温预报的系统偏差最明显,最低气温系统偏差为-1.34℃,最高气温系统偏差为-2.87℃。根据三种数值模式气温预报误差的特征,结合BP神经网络建立本溪市气温误差预报模型对数值模式气温预报结果进行订正,订正后气温平均绝对误差由2.40℃左右降至1.40℃左右,系统偏差和均方根误差均明显缩小,气温预报准确率由50%左右提高至80%以上,数值模式气温预报准确率明显提高,具有较好的应用价值。
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 correcting model was established based on the error analyses of the models. The results show that,f or 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 disturbance.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.
出处
《气象与环境学报》
2018年第1期22-29,共8页
Journal of Meteorology and Environment
基金
本溪市气象局项目"本溪冬季低于-20℃温度预报研究"(BXSQXJ201406)资助
关键词
BP神经网络
气温
误差
检验
BP neural network
Air temperature
Error
Test
作者简介
王焕毅,男,1984年生,工程师,主要从事短期天气预报、预警研究,E-mail：wanghybx@163.com.;通信作者：谭政华,E-mail：balfulosa@163.com.
