基于DERF2.0的乌鲁木齐2013年春季降温过程的延伸期预报检验
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摘要
基于DERF 2.0模式数据,应用均一化标准差、均方根误差等方法,以2013年乌鲁木齐春季逐日温度及24 h变温检验为背景,初步评估了该模式对延伸期春季强降温过程的预报能力。结果表明:(1)逐日气温预报整体偏差随预报时效推进而增大,延伸期预报偏差明显大于中期。(2)旬平均温度的中期预报偏差普遍在-2～-8℃,延伸期的预报偏差最小在0℃左右,最大为-15.5℃。(3)日平均气温以及最高、最低气温的逐日偏差均以冷偏差为主,偏差范围为5～-15℃,延伸期预报偏差范围为-5～-15℃。模式对升温阶段的预报冷偏差随升温加剧而增大,对降温阶段的预报偏差随降温加剧而减小。24 h变温偏差主要在±5℃范围内变化,延伸期的24 h变温偏差比中期预报偏大可达|8|℃以上。(4)DERF 2.0模式对中短期温度预报有一定水平,延伸期预报能力下降,可参考价值较弱。(5)对强降温过程的结束日的温度预报偏差小,而对过程初始日的温度预报冷偏差大,造成对降温过程的预报暖偏差大,强降温过程普遍漏报。
Based on DERF2.0 data,using the method of Normalized Standard Deviation(NSTD),RMSE and so on,with the background of Spring daily temperature and 24 h temperature-change evaluation in 2013,a preliminary assessment of DERF 2.0 extended-range prediction ability for spring strong cooling process is made. The results show that:(1) The overall daily temperature bias is increased with impel of forecast time,extended-range forecast bias is more than medium-range obviously.(2) The bias of ten-day average temperature generally in -2 ～-8 ℃,extended-range forecast minimum bias is about 0℃,the maximum bias is -15.5 ℃.(3)Daily bias of T_(2m)、T_(max) and T_(min) is mainly cold,concentrated in 5 ～ 15 ℃,extended-range forecast bias is mainly change between -5～ 15 ℃. Cold bias of warming stage is increase with temperature rise,but the bias of cooling stage is decrease with temperature drop. 24 h temperature-change bias is mainly changes in 5 ℃,the extended-range forecast bias is |8| ℃ larger than medium-range.(4)The DERF 2.0 model has certain prediction ability to the mid-shot-term temperature,but the ability for extended-range forecast is too weak to reference.(5)There is a small ending-T forecast bias and large cold initial-T bias for strong cooling process,as aresult,it made a large process warm bias and missing report.
Based on DERF2.0 data,using the method of Normalized Standard Deviation(NSTD),RMSE and so on,with the background of Spring daily temperature and 24 h temperature-change evaluation in 2013,a preliminary assessment of DERF 2.0 extended-range prediction ability for spring strong cooling process is made. The results show that:(1) The overall daily temperature bias is increased with impel of forecast time,extended-range forecast bias is more than medium-range obviously.(2) The bias of ten-day average temperature generally in -2 ～-8 ℃,extended-range forecast minimum bias is about 0℃,the maximum bias is -15.5 ℃.(3)Daily bias of T_(2m)、T_(max) and T_(min) is mainly cold,concentrated in 5 ～ 15 ℃,extended-range forecast bias is mainly change between -5～ 15 ℃. Cold bias of warming stage is increase with temperature rise,but the bias of cooling stage is decrease with temperature drop. 24 h temperature-change bias is mainly changes in 5 ℃,the extended-range forecast bias is |8| ℃ larger than medium-range.(4)The DERF 2.0 model has certain prediction ability to the mid-shot-term temperature,but the ability for extended-range forecast is too weak to reference.(5)There is a small ending-T forecast bias and large cold initial-T bias for strong cooling process,as aresult,it made a large process warm bias and missing report.
引文
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[16]姜燕敏,吴昊旻.20个CMIP5模式对中亚地区年平均气温模拟能力评估[J].气候变化研究进展,2013,9(2):110-116.
[2]章大全,陈丽娟.基于DERF2.0的月平均温度概率订正预报[J].大气科学,2016,40(5):1022-1032.
[3]章大全,王永光.基于DERF2.0的长江中下游春播期气候预测[J].应用气象学报,2016,27(2):182-190.
[4]唐红玉,董新宁,周秀华,等.月动力延伸预报500 hPa高度场检验及其在重庆月气候预测中降尺度应用[J].沙漠与绿洲气象,2016,10(3):1-8.
[5]陈思蓉,周秀华,陆虹,等.月动力延伸预测产品DERF2.0对广西气温和降水的预测评估[J].气象研究与应用,2016,37(3):16-19.
[6]李忠燕,白慧,张娇艳,等.DERF 2.0对贵州气温、降水的预测性能评估[J].贵州气象,2017,41(3):1-9.
[7]李瑞雪,张明军,金爽,等.乌鲁木齐市近47年气温降水特征与突变分析[J].干旱区资源与环境,2009,23(10):111-115.
[8]毛炜峄,陈颖.1951-2015年乌鲁木齐市寒潮过程频数及强度气候特征[J].干旱气象,2016,34(3):403-411.
[9]毛炜峄,白素琴,陈鹏翔.1951-2015年乌鲁木齐市降温过程频数及强度气候特征[J].气象,2016,42(11):1351-1363.
[10]郑玉萍,宫恒瑞.乌鲁木齐市寒潮气候特征分析[J].沙漠与绿洲气象,2015,9(6):56-61.
[11]吴丹宇.乌鲁木齐地区气象因素变化与急性心肌梗死发病的相关性研究[D].乌鲁木齐:新疆医科大学,2017.
[12]高红艳,田文庆,张丽.乌鲁木齐市急性心肌梗塞发病与气象因素关系的分析[J].新疆医学,2001,31(1):8-10.
[13]张宏芳,陈海山.21个气候模式对东亚夏季环流模拟的评估I:气候态[J].气象科学,2011,21(2):119-128.
[14]孙林海,刘一鸣.区域气候模式对中国夏季平均气温和降水的评估分析[J].气象,2008,34(11):31-39.
[15]吴昊旻,黄安宁,何清,等.10个CMIP5模式预估中亚地区未来50a降水时空变化特征[J].干旱区地理,2013,36(4):669-678.
[16]姜燕敏,吴昊旻.20个CMIP5模式对中亚地区年平均气温模拟能力评估[J].气候变化研究进展,2013,9(2):110-116.