| 基于TIGGE资料的北半球地面气温预报的统计降尺度研究 |
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| 引用本文： | 陈晓龙,智协飞.基于TIGGE资料的北半球地面气温预报的统计降尺度研究[J].大气科学学报,2014,37(3):268-275. |
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| 作者姓名： | 陈晓龙 智协飞 |
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| 作者单位： | 气象灾害教育部重点实验室(南京信息工程大学), 江苏 南京 210044;南京信息工程大学 大气科学学院, 江苏 南京 210044;气象灾害教育部重点实验室(南京信息工程大学), 江苏 南京 210044;南京信息工程大学 大气科学学院, 江苏 南京 210044 |
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| 基金项目： | 公益性行业（气象）科研专项（GYHY200906009）；江苏高校优势学科建设工程资助项目（PAPD） |
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| 摘 要： | 基于TIGGE(THORPEX Interactive Grand Global Ensemble,全球交互式大集合)资料中欧洲中期天气预报中心(European Centre for Medium-Range Weather,ECMWF)、日本气象厅(Japan Meteorological Agency,JMA)、美国国家环境预报中心(National Centers for Environmental Prediction,NCEP)和英国气象局(United Kingdom Met Office,UKMO)4个中心的北半球地面2 m气温集合平均预报资料,利用插值技术与回归分析,并引入了消除偏差集合平均(bias-removed ensemble mean,BREM)和多模式超级集合(superensemble,SUP)方法进行统计降尺度预报研究。结果表明,在2007年夏季3个月中,4个单中心的降尺度预报明显地改善了预报效果。引入SUP和BREM两种集成预报方法后,预报误差得到进一步减小。对比综合表现最好的单中心ECMWF的预报,1~7 d的降尺度预报误差改进率均达20%以上。研究还发现,引入SUP方法的降尺度预报效果优于引入BREM方法的降尺度预报,利用双线性插值方法在上述两方案中的预报效果优于其他3种插值方法。
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| 关 键 词： | TIGGE 地面气温 统计降尺度 多模式集成预报 |
| 收稿时间： | 2012/4/30 0:00:00 |
| 修稿时间： | 2013/5/13 0:00:00 |
A statistical downscaling study on the surface temperature forecast in the Northern Hemisphere using the TIGGE data |
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| CHEN Xiao-long and ZHI Xie-fei.A statistical downscaling study on the surface temperature forecast in the Northern Hemisphere using the TIGGE data[J].大气科学学报,2014,37(3):268-275. |
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| Authors: | CHEN Xiao-long and ZHI Xie-fei |
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| Institution: | Key Laboratory of Meteorological Disaster(NUIST), Ministry of Education, Nanjing 210044, China;School of Atmospheric Sciences, NUIST, Nanjing 210044, China;Key Laboratory of Meteorological Disaster(NUIST), Ministry of Education, Nanjing 210044, China;School of Atmospheric Sciences, NUIST, Nanjing 210044, China |
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| Abstract: | Based on the ensemble mean outcomes from forecasts of the surface temperature 2 m over the ground in the Northern Hemisphere,which were provided by ECMWF,JMA,NCEP and UKMO in the TIGGE(THORPEX Interactive Grand Global Ensemble) data archive,a statistical downscaling forecast was studied by using the interpolation,linear regression in conjunction with multimodel superensemble(SUP) and bias-removed ensemble mean(BREM).The results showed that the statistical downscaling technique significantly improved the forecast skill of four single models during three months in the summer of 2007.The SUP and BREM methods further reduced the errors of the single model downscaling forecasts.The improvement percentage of the 1-7 d forecast error of the downscaling forecast with BREM and SUP forecast schemes of the best single model ECMWF was over 20%.In addition,the forecast skill of the statistical downscaling with SUP forecast was superior to that with BREM forecast and the forecast skiu by using the bilinear interpolation method was better than that by using the other three interpolation methods in both two schemes. |
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| Keywords: | TIGGE surface temperature statistical downscaling multimodel ensemble forecasts |
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