| ECMWF模式地面气温预报的四种误差订正方法的比较研究 |
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| 引用本文： | 李佰平,智协飞.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,2012,38(8):897-902. |
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| 作者姓名： | 李佰平 智协飞 |
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| 作者单位： | 南京信息工程大学气象灾害省部共建教育部重点实验室,南京,210044 |
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| 基金项目： | 国家科技支撑项目（2009BAC51B03）和公益性行业（气象）科研专项（GYHY200906007）共同资助 |
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| 摘 要： | 采用均方根误差对欧洲中期天气预报中心（ECWMF）确定性预报模式2007年1月至2010年12月的地面气温预报结果进行评估，并分别利用一元线性回归、多元线性回归、单时效消除偏差和多时效消除偏差平均的订正方法，对ECMWF模式地面气温预报结果进行订正。结果表明，4种订正方法都能有效地减小地面气温多个时效预报的误差，改进幅度约为1℃。在短期预报中仅考虑最新预报结果的一元线性回归订正方法要优于考虑多个预报结果的多元集成预报订正方法。在中期预报中考虑多个预报结果的多元集成预报订正方法更优，更稳定。在模式预报误差较大的情况下，多时效集成的订正方法能更稳定地减小误差。
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| 关 键 词： | ECMWF模式 误差订正 线性回归 消除偏差平均 |
| 收稿时间： | 2/9/2012 12:00:00 AM |
| 修稿时间： | 2012/4/11 0:00:00 |
Comparative Study of Four Correction Schemes of the ECMWF Surface Temperature Forecasts |
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| LI Baiping and ZHI Xiefei.Comparative Study of Four Correction Schemes of the ECMWF Surface Temperature Forecasts[J].Meteorological Monthly,2012,38(8):897-902. |
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| Authors: | LI Baiping and ZHI Xiefei |
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| Institution: | Key Laboratory of Meteorological Disaster of Ministry of Education, NUIST, Nanjing 210044;Key Laboratory of Meteorological Disaster of Ministry of Education, NUIST, Nanjing 210044 |
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| Abstract: | The surface temperature outcomes of determined forecasts of the European Centre for Medium-range Weather Forecasts(ECMWF) during the period from January 2007 to December 2010 are examined with rootmean -square error(RMSE) and the error is corrected by utilizing the methods of unitary linear regression,multiple linear regression,unitary bias-removed with unitary lead time forecast and bias-removed mean with multi-lead time forecasts,respectively.The results show that all of the four methods could considerably reduce the ECMWF forecast errors for multi-lead time forecasts,generally about 1°C.The forecast skill of the unitary linear regression is higher than that of two consensus forecast methods considering multi-lead time forecast outcomes for short-range forecast.While two kinds of consensus forecasts have higher and more stable forecast skills for medium-range forecast. The correction methods considering multi-lead time forecast outcomes could reduce the forecast error more stably especially when the error is large. |
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| Keywords: | ECMWF model error correction linear regression bias removed mean |
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