Abstract: Based on daily maximum and minimum temperature data of 99 national weather stations in Shaanxi, two methods of univariate linear regression and decaying average were used to correct the temperature forecasts of SCMOC (the data of the national meteorological center forecast), GRAPES_Meso (global/regional assimilation and prediction system) and ECMWF (European center for mediumrange weather forecasting). The result show that the prediction accuracy of daily minimum temperature was higher than that of daily maximum temperature for SCMOC, GRAPES_Meso and ECMWF. The accuracy of daily maximum and minimum temperature forecast of SCMOC was obviously highest among them, while that of GRAPES_Meso was lowest. The methods of univariate linear regression and decreasing average could significantly improve the accuracy of air temperature forecasts of GRAPES_Meso and ECMWF, but could not improve the accuracy of SCMOC. The accuracy of ECMWF’s daily maximum and minimum temperature forecast corrected from 2017 to 2019 was higher than that of SCMOC. The accuracy of GRAPES_Meso’s 24hour and 48hour daily maximum temperature prediction corrected in 2019 was higher than that of SCMOC, while the accuracy of daily minimum temperature prediction after correcting was still much lower than that of SCMOC. By comparison, the ability and stability of univariate linear regression method for rectifying air temperature forecast of numerical model were better than that of decreasing average method.

Key words: Key words: bias correction of temperature prediction, univariate linear regression analysis, decaying average method, GRAPES_Meso, SCMOC, ECMWF

摘要： 利用陕西99个国家气象站2017—2019年日最高（低）气温观测资料，采用一元线性回归和递减平均方法，对GRAPES_Meso、ECMWF和SCMOC的日最高（低）气温预报进行订正，并作对比检验。结果表明，SCMOC、GRAPES_Meso和ECMWF的日最低气温预报准确率较日最高气温偏高，其中SCMOC的日最高和最低气温预报准确率最高，ECMWF次之，GRAPES_Meso最低。一元线性回归和递减平均方法对SCMOC的气温预报订正多为负效果，但对GRAPES_Meso和ECMWF的气温预报订正有明显正效果。订正后ECMWF与订正前SCMOC的预报相比，前者日最高和最低气温的预报准确率偏高。订正后GRAPES_Meso与订正前SCMOC的预报相比，前者日最低气温预报准确率偏低、2018年24 h和2019年24、48 h日最高气温预报准确率偏高。一元线性回归法对模式气温预报的订正能力和稳定性优于递减平均法。

关键词: 关键词：气温预报误差订正, 一元线性回归法, 递减平均法, GRAPES_Meso, SCMOC, ECMWF