| Application of ATOVS Microwave Radiance Assimilation to Rainfall Prediction in Summer 2004 |
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| 作者姓名： | 齐琳琳 孙建华 |
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| 作者单位： | [1]Chinese Academy of Meteorological Sciences, Beijing 100081 [2]Laboratory of Cloud Physics and Severe Storms, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029 [3]Institute of Aviation Meteorology, Beijing 100085 |
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| 基金项目： | 国家重点基础研究发展计划(973计划)，中国科学院基金 |
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| 摘 要： | Experiments are performed in this paper to understand the influence of satellite radiance data on the initial field of a numerical prediction system and rainfall prediction. First, Advanced Microwave Sounder Unit A （AMSU-A） and Unit B （AMSU-B） radiance data are directly used by three-dimensional variational data assimilation to improve the background field of the numerical model. Then, the detailed effect of the radiance data on the background field is analyzed. Secondly, the background field, which is formed by application of Advanced Television and Infrared Observation Satellite Operational Vertical Sounder （ATOVS） microwave radiance assimilation, is employed to simulate some heavy rainfall cases. The experiment results show that the assimilation of AMSU-A （B） microwave radiance data has a certain impact on the geopotential height, temperature, relative humidity and flow fields. And the impacts on the background field are mostly similar in the different months in summer. The heavy rainfall experiments reveal that the application of AMSU-A （B） microwave radiance data can improve the rainfall prediction significantly. In particular, the AMSU-A radiance data can significantly enhance the prediction of rainfall above 10 mm within 48 h, and the AMSU-B radiance data can improve the prediction of rainfall above 50 mm within 24 h. The present study confirms that the direct assimilation of satellite radiance data is an effective way to improve the prediction of heavy rainfall in the summer in China.
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| 关 键 词： | heavy rainfall§satellite radiance data§direct assimilation§rainfall prediction experiments |
| 收稿时间： | 3 November 2005 |
Application of ATOVS microwave radiance assimilation to rainfall prediction in Summer 2004 |
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| Linlin Qi,Jianhua Sun.Application of ATOVS Microwave Radiance Assimilation to Rainfall Prediction in Summer 2004[J].Advances in Atmospheric Sciences,2006,23(5):815-830. |
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| Authors: | Linlin Qi Jianhua Sun |
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| Institution: | Chinese Academy of Meteorological Sciences, Beijing 100081, Institute of Aviation Meteorology, Beijing 100085,Laboratory of Cloud Physics and Severe Storms§Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029 |
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| Abstract: | Experiments are performed in this paper to understand the influence of satellite radiance data on the initial field of a numerical prediction system and rainfall prediction. First, Advanced Microwave Sounder Unit A (AMSU-A) and Unit B (AMSU-B) radiance data are directly used by three-dimensional variational data assimilation to improve the background field of the numerical model. Then, the detailed effect of the radiance data on the background field is analyzed. Secondly, the background field, which is formed by application of Advanced Television and Infrared Observation Satellite Operational Vertical Sounder (ATOVS) microwave radiance assimilation, is employed to simulate some heavy rainfall cases.The experiment results show that the assimilation of AMSU-A (B) microwave radiance data has a certain impact on the geopotential height, temperature, relative humidity and flow fields. And the impacts on the background field are mostly similar in the different months in summer. The heavy rainfall experiments reveal that the application of AMSU-A (B) microwave radiance data can improve the rainfall prediction significantly. In particular, the AMSU-A radiance data can significantly enhance the prediction of rainfall above 10 mm within 48 h, and the AMSU-B radiance data can improve the prediction of rainfall above 50 mm within 24 h. The present study confirms that the direct assimilation of satellite radiance data is an effective way to improve the prediction of heavy rainfall in the summer in China. |
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| Keywords: | heavy rainfall satellite radiance data direct assimilation rainfall prediction experiments |
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