| 月尺度动力模式产品解释应用系统及预测技巧 |
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| 引用本文： | 覃志年,陈丽娟,唐红玉,黄颖.月尺度动力模式产品解释应用系统及预测技巧[J].应用气象学报,2010,21(5):614-620. |
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| 作者姓名： | 覃志年 陈丽娟 唐红玉 黄颖 |
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| 作者单位： | 1.广西壮族自治区气候中心, 南宁 530022 |
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| 基金项目： | 科技部科技支撑项目，中国气象局2009年业务建设项目"季节气候预测业务系统建设"共同资助 |
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| 摘 要： | 从短期气候预测业务面临的实际问题出发,针对月尺度气候预测,利用国家气候中心月动力延伸预报(DERF)模式资料,开发了集多种统计预测方法、多种解释应用技术于一体的业务系统。利用该系统的多种预测方法对广西88个站点2005-2008年6月降水距平百分率的独立样本检验结果表明:在解释应用方法中,基于模式输出统计假设方法(MOS)的预报结果优于完全预报法(PP);利用预测站点附近的环流关键区构建的预测因子预报效果最好;经验统计函数法(EOF)和动力与统计相结合的解释应用方法的预测准确率较高且较稳定;同时满足模式预测资料中预测因子和预测对象的高相关关系,以及再分析资料中预测因子和预测对象之间高相关关系确定关键区,并在此基础上建立预测模型的预测效果更佳。解释应用预测准确率一般都在70分以上,高于传统的物理统计预测结果。
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| 关 键 词： | 月尺度 动力延伸预报 解释应用 技巧 |
| 收稿时间： | 1/5/2010 12:00:00 AM |
| 修稿时间： | 7/8/2010 12:00:00 AM |
Downscaling Methods and Application System Based on Monthly scale Dynamical Model Outputs and Forecast Skill Analysis |
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| Qin Zhinian,Chen Lijuan,Tang Hongyu and Huang Ying.Downscaling Methods and Application System Based on Monthly scale Dynamical Model Outputs and Forecast Skill Analysis[J].Quarterly Journal of Applied Meteorology,2010,21(5):614-620. |
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| Authors: | Qin Zhinian Chen Lijuan Tang Hongyu and Huang Ying |
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| Affiliation: | 1.Guangxi Climate Center, Nanning 5300222.Laboratory for Climate Studies, National Climate Center, CMA, Beijing 1000813.Chongqing Climate Center, Chongqing 401147 |
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| Abstract: | In order to solve the practical problems in short range climate prediction, an operational system has been developed for monthly scale climate prediction based on Dynamical Extended Range Forecast (DERF) model output, statistical prediction methods and downscaling techniques. The system has the following features. It provides two subjunctive methods including Perfect Prediction (PP) and Model Output Statistics (MOS) methods. The former supposes that the prediction of model is perfect enough and needn't to be modified. The downscaling model can be built on the historical observed data. The latter supposes that the prediction of model has certain bias and the downscaling model is developed using the hindcast data of model output. Predictants can be determined in two ways. One is called the single station method and predictants are determined at each station within the studied area based on the reasonable physical mechanism. The other is called the regional average method and predictants are determined based on the relationship of regional average features and predictants. Three types of high correlation centers, i.e., positive correlation centers, negative correlation centers and local correlation centers are used to determine key circulation regions which could be taken as predictants. Six downscaling methods are used to obtain predictants from key circulation regions, and seven combinations of correlation coefficients within key circulation regions are used to find optimal prediction result. The stepwise regression, optimal sub tree regression, analogous regression and minimum distance resemblance are used to develop statistic prediction models. Predicted results can be assessed after the data is updated. The output of the prediction methods provided by the system is compared with observed precipitation data at 88 stations of Guangxi in June, 2005—2008. The results of the independent samples show that the skill of the MOS method is much better than the PP method in the downscaling techniques. The best forecast method is based on the predictors which are selected from the key circulation region near the station. The Empirical Orthogonal Functions (EOF) and combined dynamical statistical prediction method are more accurate and stable than the other downscaling methods. In determining key areas which affected predictants, the regions where model output and predictants, reanalysis data and predictants are well correlated are selected. The prediction skill of the downscaling techniques is generally above 70%, which is higher than that of the conventional physical-statistical prediction. |
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| Keywords: | monthly-scale dynamic extended range forecast downscaling skill |
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