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

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3个地区开展了反演试验。以探空为标准的反演结果对比显示,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。

Research of the infrared high spectral(IASI) satellite remote sensing atmospheric temperature and humidity profiles based on the one-dimensional variational algorithm

Atmospheric temperature and humidity profiles are important atmospheric parameters and play an important role in numerical weather forecasting and weather warning.In order to obtain high-precision profile data of atmospheric temperature and water vapor mixing ratio,this paper studied a variational retrieval method of atmospheric temperature and water vapor mixing ratio profiles based on the Metop-A/IASI infrared hyperspectral data.Based on the radiance data of IASI hyperspectral sensors,combined with the forecasting technology of CRTM model and WRF model,using one dimensional variational method,this paper studied the quality control of satellite data,background error covariance localization and observation error covariance methods,constructed a variational retrieval system for atmospheric temperature and water vapor mixing radio profiles,and carried out the retrieval tests in Beijing,Qingdao and Shenyang.Comparison of retrieval results with sounding data as a standard shows that,using the WRF model forecast values as the background field,the average absolute error of temperature(water vapor mixing ratio) is less than 0.6 K(0.021 g/kg),and the root mean square error is 0.89 K(0.02 g/kg).The experimental results show that based on the one dimensional variational method,the Metop-A/IASI infrared hyperspectral data can be used for the high-precision detection of atmospheric temperature and water vapor mixing ratio profiles.