| 基于DERF2.0的月平均温度概率订正预报 |
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| 引用本文： | 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. |
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| 作者姓名： | 章大全 陈丽娟 |
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| 作者单位： | 1.中国气象局国家气候中心, 北京 100081 |
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| 基金项目： | 国家重点基础研究发展计划项目2012CB955203，公益性行业（气象）科研专项GYHY201306032、GYHY201406022，国家自然科学基金项目41205039，中国气象局短期气候预测创新团队项目 |
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| 摘 要： | 国家气候中心第二代月动力延伸模式回算资料的分析表明，二代模式月平均温度预报与观测实况仍然存在较大偏差，模式预报有较大改进空间。本文采用非参数百分位映射法对模式月平均温度预报进行概率订正，该方法基于模式集合平均给出的确定性预报，结合模式回算资料各集合成员计算得到的模式概率密度分布，给出确定性预报在模式概率密度分布中的百分位值，并将百分位值投影到观测资料的概率密度分布中，得到模式预报的概率订正值。对订正前后模式预报的检验评估显示，该订正方案不仅有效降低了模式预报与实况的均方根误差（RMSE），对月平均温度距平分布的预报技巧也有所改善，不同超前时间模式预报的预测技巧评分（PS）和距平相关系数（ACC）均有提升，同时模式预报误差的大小对订正效果无明显影响。从分月的订正预报结果来看，对夏季各月的温度预测技巧的提升整体高于冬季各月。
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| 关 键 词： | 月动力延伸模式 检验 概率 订正 |
| 收稿时间： | 2015/7/10 0:00:00 |
Bias Correction in Monthly Means of Temperature Predictions of the Dynamic Extended Range Forecast Model |
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| ZHANG Daquan and CHEN Lijuan.Bias Correction in Monthly Means of Temperature Predictions of the Dynamic Extended Range Forecast Model[J].Chinese Journal of Atmospheric Sciences,2016,40(5):1022-1032. |
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| Authors: | ZHANG Daquan and CHEN Lijuan |
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| Institution: | 1.National Climate Center, China Meteorological Administration, Beijing 1000812.National Climate Center, China Meteorological Administration, Beijing 100081;Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044 |
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| Abstract: | Bias analysis of hindcast data from the monthly Dynamic Extended Range Forecast model (DERF2.0) indicates that the monthly mean temperature prediction performance is not good enough to be used operationally and needs to be improved. Quantile mapping of the non-parameter method is applied to correct the DERF2.0 model bias of monthly mean temperature. The first key step of this method is to utilize deterministic model output and calculate the cumulative density function (CDF) of hindcast data. Then, we can obtain the quantile mapping of the deterministic model output on the CDF of hindcast data. The second step is to calculate the CDF of observation data and map the quantile result of model output to the CDF of observational data. The model bias can be reduced to a certain extent after the above procedures. Hindcast verification shows that the method can significantly reduce the root mean square error (RMSE) of model output and improve the predictive skill of spatial distributions of monthly mean temperature anomalies (1983-2012). Prediction skill (PS) and anomaly correlation coefficient (ACC) scores between model output and observations with different lead times have been improved, and this improvement remains stable with different magnitudes of model bias. Comparison of model prediction skills of different months shows that the enhancement of prediction performance in summer is greater than in winter. |
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| Keywords: | Monthly Dynamic Extended Range Forecast model Verification Probability Bias correction |
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