基于一维变分算法的红外高光谱(IASI)卫星遥感大气温湿廓线研究
投稿时间: 2018-01-02  最后修改时间: 2018-03-08  点此下载全文
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官元红 南京信息工程大学气象灾害预报预警与评估协同创新中心/气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/中国气象局气溶胶与云降水开放重点实验室 guanyh@nuist.edu.cn 
任杰 南京信息工程大学数学与统计学院  
鲍艳松 南京信息工程大学气象灾害预报预警与评估协同创新中心/气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/中国气象局气溶胶与云降水开放重点实验室 ysbao@nuist.edu.cn 
陆其峰 中国气象局中国遥感卫星辐射测量和定标重点开放实验室/国家卫星气象中心  
肖贤俊 中国气象局中国遥感卫星辐射测量和定标重点开放实验室/国家卫星气象中心  
基金项目:国家重点研发计划;上海航天科技创新基金;中国气象局中国遥感卫星辐射测量和定标重点开放实验室开放课题;2017年重点研发计划项目
中文摘要:大气温湿度廓线是大气重要参数,在数值天气预报及天气预警中具有重要的应用价值。为获得高精度的大气温度与水汽混合比廓线数据,研究了基于Metop-A/IASI红外高光谱资料的大气温度与水汽混合比廓线变分反演方法。利用IASI高光谱传感器温度和水汽探测通道资料,结合CRTM模式和WRF模式预报技术,使用一维变分方法,研究了卫星资料质量控制、背景误差协方差本地化、观测误差协方差计算等方法,构建了大气温度及水汽混合比廓线变分反演系统,并在北京、青岛、沈阳三个地区开展了反演试验。以探空为标准的反演结果对比试验显示,使用WRF模式预报值作为背景场,温度的平均误差绝对值小于0.6 K,均方根误差(RMSE)为0.89 K;水汽混合比的平均误差绝对值小于0.021 g/kg,均方根误差(RMSE)为0.02 g/kg。试验结果表明:基于一维变分方法,可以利用Metop-A/IASI红外高光谱资料进行大气温度与水汽混合比廓线高精度探测。
中文关键词:Metop-A/IASI,温湿廓线,反演,一维变分
 
sensing atmospheric temperature and humidity profiles based on the one-dimensional variational algorithm
Abstract:Atmospheric temperature and humidity profiles are important parameters of the atmosphere and play an important role in numerical weather forecasting and weather situation awareness. In order to get high precision of temperature and mixture radio profiles, a variational retrieval method of atmospheric temperature and mixture radio profiles based on Metop-A/IASI hyperspectral infrared data has been studied. In this study, based on the radiance data of IASI hyperspectral sensors, combined with CRTM model and WRF model forecasting technology, using one dimensional variational method, carried out background error covariance localization and observation error covariance method research. Building the variational retrieval system of atmosphere temperature and mixture radio profiles, and carry out the retrieval test in Beijing, Qingdao and Shenyang. The contrast test with sounding data as a standard shows that,using the WRF model forecast value as the background field, the average error of temperature is less than 0.6 K, and the root mean square error (RMSE) is 0.89 K. The average error of mixing ratio is less than 0.021g/kg, and RMSE is 0.02 g/kg. The experimental results show that based on the one dimensional variational method, the Metop-A/IASI data can be used for the high-precision detection of atmospheric temperature and mixture radio profiles.
keywords:Metop-A/IASI  Temperature and humidity profiles  Retrieval  One dimensional variational
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