ECMWF高分辨率模式2m温度预报误差订正方法研究
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摘要
文章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较好地改善了站点温度的预报质量。经过订正后,模式最高温度24、48、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,模式最低温度24、48、72 h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与实况基本吻合。空间误差逐步订正法的订正量在±1℃之内,与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。
In this paper,a comprehensive correction technique by combining moving-biweight correction method with successive correction method for spatial error is 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 1 May 2016 to 1 May 2017.The main conclusions are as follows.(1)The 2 m maximum and minimum temperature forecasts of ECMWF model are 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 shows a significant systematic deviation which is stable at different forecast times.(2)The moving-biweight correction method having longer moving correction period has a better effect on the model temperature,which is ideal for the 20 d forecast.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 shows that the quality of temperature forecasting has 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 accuracies of forecast errors2℃ in 24,48,72 h forecasts are greatly increased from 0.59,0.55,0.52 to 0.75,0.68,0.62, respectively,and the minimum temperature accuracies are increased from 0.84,0.83,0.82 to 0.89,0.87,0.85,respectively.After bias correction,the maximum and minimum temperature accuracies of the 72 h forecast are even greater than that of the 24 h forecast before correction.In general,the deviation of the temperature forecasting is effectively reduced by this proposed comprehensive correction technique,even with a more uniform spatial distribution.(4)For the alpine station,the maximum and minimum temperatures after correction are basically consistent with the observation.In addition,the successive correction method for spatial error has a positive correction effect on temperature correction for the reason that the deviation is within ±1℃,but shows 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.
In this paper,a comprehensive correction technique by combining moving-biweight correction method with successive correction method for spatial error is 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 1 May 2016 to 1 May 2017.The main conclusions are as follows.(1)The 2 m maximum and minimum temperature forecasts of ECMWF model are 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 shows a significant systematic deviation which is stable at different forecast times.(2)The moving-biweight correction method having longer moving correction period has a better effect on the model temperature,which is ideal for the 20 d forecast.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 shows that the quality of temperature forecasting has 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 accuracies of forecast errors2℃ in 24,48,72 h forecasts are greatly increased from 0.59,0.55,0.52 to 0.75,0.68,0.62, respectively,and the minimum temperature accuracies are increased from 0.84,0.83,0.82 to 0.89,0.87,0.85,respectively.After bias correction,the maximum and minimum temperature accuracies of the 72 h forecast are even greater than that of the 24 h forecast before correction.In general,the deviation of the temperature forecasting is effectively reduced by this proposed comprehensive correction technique,even with a more uniform spatial distribution.(4)For the alpine station,the maximum and minimum temperatures after correction are basically consistent with the observation.In addition,the successive correction method for spatial error has a positive correction effect on temperature correction for the reason that the deviation is within ±1℃,but shows 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.
引文
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盛春岩,薛德强,雷霆,等,2006.雷达资料同化与提高模式水平分辨率对短时预报影响的数值对比试验[J].气象学报,64(3):293-307.Sheng C Y,Xue D Q,Lei T,et al,2006.Comparative experiments between effects of doppler radar data assimilation and inceasing horizontal resolution on short range prediction[J].Acta Meteor Sin,64(3):293-307(in Chinese).
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吴启树,韩美,郭弘,等,2016.MOS温度预报中最优训练期方案[J].应用气象学报,27(4):426-434.Wu Q S,Han M,Guo H,et al,2016.The optimal training period scheme of MOS temperature forecast[J].J Appl Meteor Sci,27(4):426-434(in Chinese).
徐道生,陈子通,钟水新,等,2014.对流参数化与微物理过程的耦合及其对台风预报的影响研究[J].气象学报,72(2):337-349.Xu D S,Chen Z T,Zhong S X,et al,2014.Study of the coupling of cumulus convection parameterization with cloud microphysics and its influence on forecast of typhoon[J].Acta Meteor Sin,72(2):337-349(in Chinese).
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张夏琨,2017.2016年9—11月T639、ECMWF及日本模式中期预报性能检验[J].气象,43(2):240-248.Zhang X K,2017.Performance verification of medium range forecasts by T639,ECMWF and Japan models from September to November 2016[J].Meteor Mon,43(2):240-248(in Chinese).
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陈海山,孙照渤,2004a.陆面模式CLSM的设计及性能检验I.模式设计[J].大气科学,28(6):801-819.Chen H S,Sun Z B,2004a.Design of a comprehensive land surface model and its validation Part I.model description[J].Chin J Atmos Sci,28(6):801-819(in Chinese).
陈海山,孙照渤,2004b.积雪季节变化特征的数值模拟及其敏感性试验[J].气象学报,62(3):269-284.Chen H S,Sun Z B,2004b.Numerical simulation of the evolution of snow cover and its sensitivity experiments[J].Acta Meteor Sin,62(3):269-284(in Chinese).
陈海山,孙照渤,2005.陆面模式CLSM的设计及性能检验II.模式检验[J].大气科学,29(2):272-282.Chen H S,Sun Z B,2005.Design of a comprehensive land surface model and its validation Part II.model validation[J].Chin J Atmos Sci,29(2):272-282(in Chinese).
陈静,薛纪善,颜宏,2003.物理过程参数化方案对中尺度暴雨数值模拟影响的研究[J].气象学报,61(2):203-218.Chen J,Xue J S,Yan H,2003.The impact of physics parameterization schemes on mesoscale heavy rainfall simulation[J].Acta Meteor Sin,61(2):203-218(in Chinese).
段文广,周晓军,石永炜,2012.数据挖掘技术在精细化温度预报中的应用[J].干旱气象,30(1):130-135.Duan W G,Zhou X J,Shi Y W,2012.Application of data mining technique on refined temperature forecast[J].Arid Meteor,30(1):130-135(in Chinese).
官元红,周广庆,陆其峰,等,2014.大气初始场对短期气候数值预测的影响[J].大气科学学报,37(5):631-641.Guan Y H,Zhou G Q,Lu Q F,et al,2014.Influence of initial atmospheric conditions on short-term climate numerical prediction[J].Trans Atmos Sci,37(5):631-641(in Chinese).
黄安宁,张耀存,朱坚,2008.物理过程参数化方案对中国夏季降水日变化模拟的影响[J].地球科学进展,23(11):1174-1184.Huang A N,Zhang Y C,Zhu J,2008.Impacts of physical process parameterizations on simulation of the diurnal variations of summer precipitation over China[J].Adv Earth Sci,23(11):1174-1184(in Chinese).
李佰平,智协飞,2012.ECMWF模式地面气温预报的四种误差订正方法的比较研究[J].气象,38(8):897-902.Li B P,Zhi X F,2012.Comparative study of four correction schemes of the ECMWF surface temperature forecasts[J].Meteor Mon,38(8):897-902(in Chinese).
刘还珠,陈德辉,滕俏彬,1998.不同物理过程参数化对模式台风的影响及其动力结构的研究[J].应用气象学报,9(2):141-150.Liu H Z,Chen D H,Teng Q B,1998.Researches on the influence of parameterization of physical process on modeling typhoon and its dynamical structure[J].J Appl Meteor Sci,9(2):141-150(in Chinese).
马清,龚建东,李莉,等,2008.超级集合预报的误差订正与集成研究[J].气象,34(3):42-48.Ma Q,Gong J D,Li L,et al,2008.Study of bias correction and consensus in regional multi model super ensemble forecast[J].Meteor Mon,34(3):42-48(in Chinese).
马旭林,时洋,和杰,等,2015.基于卡尔曼滤波递减平均算法的集合预报综合偏差订正[J].气象学报,73(5):952-964.Ma X L,Shi Y,He J,et al,2015.The combined descending averaging bias correction based on the kalman filter for ensemble forecast[J].Acta Meteor Sin,73(5):952-964(in Chinese).
盛春岩,薛德强,雷霆,等,2006.雷达资料同化与提高模式水平分辨率对短时预报影响的数值对比试验[J].气象学报,64(3):293-307.Sheng C Y,Xue D Q,Lei T,et al,2006.Comparative experiments between effects of doppler radar data assimilation and inceasing horizontal resolution on short range prediction[J].Acta Meteor Sin,64(3):293-307(in Chinese).
托亚,梁海河,马淑芬,等,2003.用雷达观测资料改进MM5初始场的初步试验研究[J].大气科学学报,26(5):661-667.Tuo Y,Liang H H,Ma S F,et al,2003.A Preliminary research on improving MM5 initial fields using radar data[J].Trans Atmos Sci,26(5):661-667(in Chinese).
王丹,黄少妮,高红燕,等,2016.递减平均法对陕西SCMOC精细化温度预报的订正效果[J].干旱气象,34(3):575-583.Wang D,Huang S N,Gao H Y,et al,2016.Analysis on errors of SCMOC refined guidance temperature prediction corrected by the decaying averaging method in Shaanxi Province[J].Arid Meteor-34(3):575-583(in Chinese).
王婧,徐枝芳,范广洲,等,2015.GRAPES_RAFS系统2 m温度偏差订正方法研究[J].气象,41(6):719-726.Wang J,Xu Z F,Fan G Z,et al,2015.Study on bias correction for the 2 m temperature forecast of GRAPES_RAFS[J].Meteor Mon,41(6):719-726(in Chinese).
王敏,李晓莉,范广洲,等,2012.区域集合预报系统2 m温度预报的校准技术[J].应用气象学报,23(4):395-401.Wang M,Li X L,Fan G Z,et al,2012.Calibrating 2 m temperature forecast for the regional ensemble prediction system at NMC[J].J Appl Meteor Sci,23(4):395-401(in Chinese).
王学忠,沙文钰,端义宏,2003.利用卫星云图变分同化初始场对热带气旋路径影响的数值试验[J].热带气象学报,19(1):107-112.Wang X Z,Sha W Y,Duan Y H,2003.A numerical study of tropical cyclone forecasting based on satellite images variational assimilation initial field[J].J Trop Meteor,19(1):107-112(in Chinese).
吴启树,韩美,郭弘,等,2016.MOS温度预报中最优训练期方案[J].应用气象学报,27(4):426-434.Wu Q S,Han M,Guo H,et al,2016.The optimal training period scheme of MOS temperature forecast[J].J Appl Meteor Sci,27(4):426-434(in Chinese).
徐道生,陈子通,钟水新,等,2014.对流参数化与微物理过程的耦合及其对台风预报的影响研究[J].气象学报,72(2):337-349.Xu D S,Chen Z T,Zhong S X,et al,2014.Study of the coupling of cumulus convection parameterization with cloud microphysics and its influence on forecast of typhoon[J].Acta Meteor Sin,72(2):337-349(in Chinese).
薛谌彬,陈娴,吴俞,等,2017.雷达资料同化在局地强对流预报中的应用[J].大气科学,41(4):673-690.Xue C B,Chen X,Wu Y,et al,2017.Application of radar data assimilation in local severe convective weather forecast[J].Chin J Atmos Sci,41(4):673-690(in Chinese).
杨松,杞明辉,姚德宽,2003.误差订正在预报集成中的应用研究[J].气象,29(12):22-25.Yang S,Qi M H,Yao D K,2003.Application of error correction in forecast ensemble[J].Meteor Mon,29(12):22-25(in Chinese).
尹姗,2016.2015年12月至2016年2月T639、ECMWF及日本模式中期预报性能检验[J].气象,42(5):637-642.Yin S,2016.Performance verification of medium range forecasting by T639 and ECMWF and Japan model from December 2015 to February2016[J].Meteor Mon,42(5):637-642(in Chinese).
张峰,2016.2016年3—5月T639、ECMWF及日本模式中期预报性能检验[J].气象,42(8):1020-1025.Zhang F,2016.Performance verification of medium range forecasts by T639,ECMWF and Japan models from March to May 2016[J].Meteor Mon,42(8):1020-1025(in Chinese).
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