| DRP-4DVar方法同化AIRS反演资料在一次江淮流域暴雨中的应用 |
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| 引用本文： | 卢冰,刘娟娟,王斌,李俊.DRP-4DVar方法同化AIRS反演资料在一次江淮流域暴雨中的应用[J].气候与环境研究,2013,18(5):562-570. |
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| 作者姓名： | 卢冰 刘娟娟 王斌 李俊 |
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| 作者单位： | 中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室, 北京 100029;中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室, 北京 100029;中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室, 北京 100029;国家卫星气象中心, 北京 100081;美国威斯康星大学麦迪逊分校气象卫星合作研究所, 美国麦迪逊 53706 |
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| 基金项目： | 国家重点基础研究发展规划项目2010CB951604;公益性行业(气象)科研专项GYHY(QX)200906009;美国国家海洋和大气局卫星与信息服务局NESDIS合作项目NA10NES4400013 |
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| 摘 要： | 利用经济省时的降维投影四维变分同化方法(DRP-4DVar),在2009年7月22～23日江淮流域的一次大暴雨过程中同化晴空条件下高光谱大气红外探测仪(AIRS)反演温度、湿度廓线,改进此次强降水过程的模拟。试验结果分析显示,同化AIRS反演的温度及湿度场后,基于四维变分同化系统的模式约束,能够改进湿度场、高度场、高低层散度场。从累积降水量偏差图及同化试验增量图可以看到,正降水量偏差对应于正湿度增量、负位势高度增量及低层负散度高层正散度增量,负降水量偏差则与之相反。同化试验较参照试验可更好地模拟出暴雨的天气形势、对暴雨的落区及强度有更好的反映。此外,从单次同化与连续同化的试验对比结果看出,连续同化试验结果较单次同化结果有进一步的改进,说明不断加入新的观测资料可以更好地模拟强降水过程。
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| 关 键 词： | 降维投影 四维变分 高光谱大气红外探测仪(AIRS)反演资料 暴雨 |
| 收稿时间： | 2011/5/11 0:00:00 |
| 修稿时间： | 2013/3/30 0:00:00 |
Assimilation of AIRS Sounding Retrievals on a Heavy Rainfall over Changjiang and Huaihe River Basin by Using DRP-4DVar Approach |
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| LU Bing,LIU Juanjuan,WANG Bin and LI Jun.Assimilation of AIRS Sounding Retrievals on a Heavy Rainfall over Changjiang and Huaihe River Basin by Using DRP-4DVar Approach[J].Climatic and Environmental Research,2013,18(5):562-570. |
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| Authors: | LU Bing LIU Juanjuan WANG Bin and LI Jun |
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| Institution: | State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081;Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison, Madison 53706, USA |
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| Abstract: | Hyperspectral infrared sounders such as Atmospheric InfraRed Sounder (AIRS) provide unprecedented global atmospheric temperature and moisture soundings with high vertical resolution and accuracy. The dimension-reduced projection four-dimensional variational data assimilation (DRP-4DVar) approach has been used to assimilate clear-sky AIRS sounding retrievals in a heavy rainfall storm over Changjiang and Huaihe River basin from 0000 UTC 22 July to 0000 UTC 23 July 2009. Atmospheric soundings of temperature and moisture from AIRS improve the precipitation forecast. Three experiments have been performed to simulate the heavy rain process: A control experiment with the initial conditions from NCEP-FNL, a single assimilation experiment, and a cycle assimilation experiment using AIRS sounding retrievals. Results from experiments show that humidity, geopotential height, and divergence of the initial field are enhanced through assimilating the AIRS temperature and moisture profiles. The 24-h increment in precipitation is consistent with the increment in humidity, geopotential height, and divergence. In this storm event, assimilation experiments have been able successfully simulate the synoptic situation leading to heavy rainfall; the location and intensity of the heavy rainfall event are better simulated when the AIRS data are assimilated. Furthermore, a cycle assimilation framework can absorb more observational data and performs better than a single assimilation framework. |
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| Keywords: | Dimension-reduced projection Four-dimensional variational data assimilation AIRS retrieval data Heavy rainfall |
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