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气象:2019,45(9):1310-1321
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递减平均法与一元线性回归法对ECMWF温度预报订正能力对比
王丹,王建鹏,白庆梅,高红燕
(陕西省气象服务中心,西安 710014; 陕西省气象台,西安 710014; 西安市气象台,西安 710014)
Comparative Correction of Air Temperature Forecast from ECMWF Model by the Decaying Averaging and the Simple Linear Regression Methods
WANG Dan,WANG Jianpeng,BAI Qingmei,GAO Hongyan
(Shaanxi Meteorological Service Center, Xi’an 710014; Shaanxi Meteorological Observatory, Xi’an 710014; Xi’an Meteorological Observatory, Xi’an 710014)
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投稿时间:2018-06-13    修订日期:2019-06-18
中文摘要: 基于ECMWF细网格模式的定时最高(低)气温预报产品,针对2017年陕西99个国家级气象站的日最高(低)气温预报,检验和比较了递减平均法和一元线性回归法两种方法对气温预报误差的订正效果。结果表明,两种方法都显著地提高了日最高(低)气温的预报准确率,随着预报时效的延长,订正能力逐渐减弱。技巧评分与模式对气温的预报能力有显著的负相关关系,秦岭及其以南地区的日最高气温预报和秦岭以北地区的日最低气温预报的准确率偏低,其技巧评分一般超过40%,极大值超过70%。两种方法都有效降低了系统误差,较小误差范围的站次增多,较大误差范围的站次减少,对日最高气温在预报绝对误差≤2℃误差范围的订正能力较为突出,对日最低气温在预报绝对误差≥3℃误差范围的订正更有优势。一元线性回归法对日最高气温预报的订正能力略优于递减平均法,对日最低气温预报的订正能力不及递减平均法,利用这两种方法对气温预报进行混合订正的效果更佳。
Abstract:The decaying averaging and the simple linear regression methods were used to correct air temperature forecast in a fine-mesh grid point forecast system of Shaanxi Meteorological Service. Based on the dataset of daily 2 m maximum and minimum air temperature forecasts of 99 national meteorological stations in Shaanxi Province from ECMWF high resolution model in 2017, the abilities of the two methods to correct temperature prediction errors were analyzed and compared. The results showed that the prediction accuracies of daily 2 m maximum and minimum air temperatures are improved significantly by the two methods, whosecorrection abilities are gradually weakened with the extension of prediction time. There is a significant negative correlation between accuracy of the temperature forecast and skill-score of the two methods. The accuracies are all low for the daily 2 m maximum air temperature in Qinling Mountains and the area south to it and daily 2 m minimum air temperature in the north to Qinling Mountains, in which the skill-score is usually more than 40% and its maximum value is even larger than 70%. The systematic deficiencies of daily 2 m maximum and minimum air temperature forecasts are effectively reduced. As a result, the frequency with a smaller error range is increased, while the frequency with a larger error range is decreased. More advantages of the two methods are attained when the absolute errors are less than 2℃ for daily 2 m maximum air temperature forecast and more than 3℃ for daily 2 m minimum air temperature forecast. The ability of the simple linear regression method to correct daily 2 m maximum air temperature forecast is slightly better than that of the decaying averaging method, whose ability to correct daily 2 m minimum air temperature forecast is better than the simple linear regression method. The mixed correction of temperature forecast by the two methods is more effective.
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基金项目:陕西省自然科学基金项目(2019JM-342)、中国气象局预报员专项项目(CMAYBY2019-117)和陕西省气象局精细化气象格点预报攻关团队共同资助
引用文本:
王丹,王建鹏,白庆梅,高红燕,2019.递减平均法与一元线性回归法对ECMWF温度预报订正能力对比[J].气象,45(9):1310-1321.
WANG Dan,WANG Jianpeng,BAI Qingmei,GAO Hongyan,2019.Comparative Correction of Air Temperature Forecast from ECMWF Model by the Decaying Averaging and the Simple Linear Regression Methods[J].Meteor Mon,45(9):1310-1321.