###
DOI:
本文二维码信息
ECMWF高分辨率模式2m温度预报误差订正方法研究
(1.江西省气象台;2.江西省基础地理信息中心;3.江西省测绘地理信息工程技术研究中心;4.流域生态与地理环境监测国家测绘地理信息局重点实验室;5.陕西省气象台;6.陕西省西安市气象台)
Bias Correction Method for the 2m Temperature Forecast of ECMWF High Resolution Model
(1.Jiangxi Provincial Meteorological Observatory;2.Jiangxi Provincial Geomatics Center;3.Jiangxi Province Engineering Research Center of Surveying, mapping and GeoInformation;4.Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, National Administration of Surveying, Mapping and Geoinformation;5.Shaanxi Provincial Meteorological Observatory;6.Xi'an Meteorological Observatory of Shaanxi Province)
摘要
相似文献
本文已被:浏览 72次   下载 22
投稿时间:2017-11-28    修订日期:2019-03-26
中文摘要: 本文提出了一种结合滑动双权重平均订正法和空间误差逐步订正法的综合订正技术,并对2016年5月1日至2017年5月1日期间24~168 h预报时效内欧洲中期天气预报中心(ECMWF)高分辨率模式的2 m最高和最低温度进行偏差订正和误差分析,主要结论如下:(1)ECMWF模式在江西省的温度预报整体上比实况偏低,最高温度尤为明显,模式温度的空间分布表现出显著的系统性偏差,且偏差在不同预报时效是稳定的,订正ECMWF模式温度具有可行性。(2)滑动双权重平均订正法中较长的滑动订正周期对模式温度预报有更好的订正效果,采用滑动订正周期20 d是比较理想的。滑动双权重平均订正法具有持续的订正能力,但在季节过渡期间订正效果可能并不理想,而空间误差逐步订正法能进一步提高滑动双权重平均订正法的预报订正质量。(3)温度预报准确率表明,滑动双权重平均订正法和空间误差逐步订正法综合订正技术很好地改善了站点温度的预报质量。模式最高温度经过订正后,24 h、48 h、72 h 预报误差≤2 ℃的准确率分别从0.59、0.55、0.52提高到0.75、0.68、0.62,大幅度提高了预报准确率。对模式最低温度的订正相对有限,24 h、48 h、72 h 的预报准确率分别从0.84、0.83、0.82提高到0.89、0.87、0.85。订正后72 h最高温度和最低温度的预报准确率都大于订正前模式24 h的准确率。总体而言,该综合订正技术较好地订正了模式误差,且误差在空间分布上较均匀。(4)对于高山站而言,经过订正后的最高和最低温度跟实况基本吻合。空间误差逐步订正法的订正量在±1 ℃之内,跟滑动双权重平均订正后的偏差呈现一定的负相关,有正的订正效果。该综合订正法已成功运用于江西省精细化气象要素客观预报业务系统中。
Abstract:In this paper, a comprehensive correction technique by combining moving-biweight correction method with successive correction method for spatial error was proposed to correct and analyze the bias of 2 m maximum and minimum temperature forecast of ECMWF (European Centre for Medium-Range Weather Forecasts) high resolution model within 24 ~ 168 h forecast time lengths during the period from 1st May, 2016 to 1st May, 2017. The main conclusions are as follows: (1) The 2 m maximum and minimum temperature forecast of ECMWF model was obviously lower, on average, than the observation in Jiangxi Province. It is feasible to correct the bias of model temperature considering that the spatial distribution of ECMWF model temperature showed a significant systematic deviation which was stable at different forecast times. (2) The moving-biweight correction method with longer moving correction period had a better effect on the model temperature, which was ideal to adopt 20 d. By combining the successive correction method for spatial error, the quality of temperature forecasting with the moving-biweight correction method could be further improved, even though the effect of moving-biweight correction method might not be satisfactory during the seasonal transition period. (3) The temperature accuracy showed that the quality of temperature forecasting had been significantly improved after bias correction conducted by the comprehensive correction technique by combining moving-biweight correction method with successive correction method for spatial error. After bias correction, the maximum temperature accuracy of forecast error less than or equal to 2 ℃ in 24, 48, 72 h forecast was greatly increased from 0.59, 0.55, 0.52 to 0.75, 0.68, 0.62, respectively, while the improvement on the minimum temperature was relatively limited, with temperature accuracy increased from 0.84, 0.83, 0.82 to 0.89, 0.87, 0.85, respectively. After bias correction, the maximum and minimum temperatures accuracy of 72 h forecast was even greater than that of 24 h forecast before correction. In general, the deviation of the temperature forecasting was effectively reduced by this proposed comprehensive correction technique, even with a more uniform spatial distribution. (4) For the alpine station, the maximum and minimum temperature after correction were basically consistent with the observation. In addition, the successive correction method for spatial error had a positive correction effect on temperature correction for the reason that the deviation was within ± 1 ℃ as well as showed a certain negative correlation with that of moving-biweight correction method. This comprehensive correction technique has been successfully applied to the objective forecasting operational system of fine meteorological elements in Jiangxi Province.
文章编号:201711280519     中图分类号:    文献标志码:
基金项目:气象预报业务关键技术发展专项(YBGJXM(2017)03-07),中国气象局预报员专项(CMAYBY2018-040),江西省科技计划项目(20171BBE50062,20171BBE50073),国家科技支撑计划课题(2015BAH50F01),江西省气象局重点项目“江西城镇气象要素预报技术研究”,江西省气象局面上项目“省际雷达资料质量控制和三维组网技术应用研究”
引用文本:
薛谌彬,陈娴,张瑛,郑婧,马晓华,张雅斌,潘留杰,0.[en_title][J].Meteor Mon,():-.
XUE Chenbin,CHEN Xian,ZHANG Ying,ZHENG Jing,MA Xiaohua,ZHANG Yabin,PAN Liujie,0.Bias Correction Method for the 2m Temperature Forecast of ECMWF High Resolution Model[J].Meteor Mon,():-.