| 中国区域性极端降水事件及人口经济暴露度研究 |
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| 引用本文： | 景丞,姜彤,王艳君,陈静,蹇东南,罗岚心,苏布达.中国区域性极端降水事件及人口经济暴露度研究[J].Acta Meteorologica Sinica,2016,74(4):572-582. |
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| 作者姓名： | 景丞 姜彤 王艳君 陈静 蹇东南 罗岚心 苏布达 |
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| 作者单位： | Department of Atmospheric Sciences,Nanjing University of Information Science & Technology;Department of Atmospheric and Oceanic Sciences,University of Wisconsin-Madison;Department of Atmospheric and Oceanic Science,University of Maryland;Geophysical Fluid Dynamics Laboratory,NOAA |
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| 基金项目： | Supported by the National(Key)Basic Research and Development(973)Program of China(2012CB417201);National Natural Science Foundation of China(41375053) |
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| 摘 要： | This paper tests the idea of substituting the atmospheric observations with atmospheric reanalysis when setting up a coupled data assimilation system.The paper focuses on the quantification of the effects on the oceanic analysis resulted from this substitution and designs four different assimilation schemes for such a substitution.A coupled Lorenz96 system is constructed and an ensemble Kalman filter is adopted.The atmospheric reanalysis and oceanic observations are assimilated into the system and the analysis quality is compared to a benchmark experiment where both atmospheric and oceanic observations are assimilated.Four schemes are designed for assimilating the reanalysis and they differ in the generation of the perturbed observation ensemble and the representation of the error covariance matrix.The results show that when the reanalysis is assimilated directly as independent observations,the root-mean-square error increase of oceanic analysis relative to the benchmark is less than 16%in the perfect model framework;in the biased model case,the increase is less than 22%.This result is robust with sufficient ensemble size and reasonable atmospheric observation quality(e.g.,frequency,noisiness,and density).If the observation is overly noisy,infrequent,sparse,or the ensemble size is insufficiently small,the analysis deterioration caused by the substitution is less severe since the analysis quality of the benchmark also deteriorates significantly due to worse observations and undersampling.The results from different assimilation schemes highlight the importance of two factors:accurate representation of the error covariance of the reanalysis and the temporal coherence along each ensemble member,which are crucial for the analysis quality of the substitution experiment.
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| 关 键 词： | data assimilation reanalysis data ensemble Kalman filter |
| 收稿时间： | 2015/12/18 0:00:00 |
| 修稿时间： | 2016/4/2 0:00:00 |
A study on regional extreme precipitation events and the exposure of population and economy in China |
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| JING Cheng,JIANG Tong,WANG Yanjun,CHEN Jing,JIAN Dongnan,LUO Lanxin and SU Buda.A study on regional extreme precipitation events and the exposure of population and economy in China[J].Acta Meteorologica Sinica,2016,74(4):572-582. |
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| Authors: | JING Cheng JIANG Tong WANG Yanjun CHEN Jing JIAN Dongnan LUO Lanxin and SU Buda |
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| Institution: | LIU Huaran;LU Feiyu;LIU Zhengyu;LIU Yun;ZHANG Shaoqing;Department of Atmospheric Sciences,Nanjing University of Information Science & Technology;Department of Atmospheric and Oceanic Sciences,University of Wisconsin-Madison;Department of Atmospheric and Oceanic Science,University of Maryland;Geophysical Fluid Dynamics Laboratory,NOAA; |
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| Abstract: | This paper tests the idea of substituting the atmospheric observations with atmospheric reanalysis when setting up a coupled data assimilation system. The paper focuses on the quantification of the effects on the oceanic analysis resulted from this substitution and designs four different assimilation schemes for such a substitution. A coupled Lorenz96 system is constructed and an ensemble Kalman filter is adopted. The atmospheric reanalysis and oceanic observations are assimilated into the system and the analysis quality is compared to a benchmark experiment where both atmospheric and oceanic observations are assimilated. Four schemes are designed for assimilating the reanalysis and they differ in the generation of the perturbed observation ensemble and the representation of the error covariance matrix. The results show that when the reanalysis is assimilated directly as independent observations, the root-mean-square error increase of oceanic analysis relative to the benchmark is less than 16% in the perfect model framework; in the biased model case, the increase is less than 22%. This result is robust with sufficient ensemble size and reasonable atmospheric observation quality (e.g., frequency, noisiness, and density). If the observation is overly noisy, infrequent, sparse, or the ensemble size is insufficiently small, the analysis deterioration caused by the substitution is less severe since the analysis quality of the benchmark also deteriorates significantly due to worse observations and undersampling. The results from different assimilation schemes highlight the importance of two factors: accurate representation of the error covariance of the reanalysis and the temporal coherence along each ensemble member, which are crucial for the analysis quality of the substitution experiment. |
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| Keywords: | Extreme precipitation events IAD method Population and economy Exposure China |
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