摘要
引进美国威斯康星大学的IMAPP(InternationalMOD IS/AIRS Preprocessing Package)软件包,介绍了利用高光谱分辨率大气红外探测器AIRS(Atmospheric Infrared Sounder)观测辐射值,用特征向量统计法反演大气温度、湿度等垂直廓线的算法,采用亮度温度分类和扫描角分类回归后,减小了反演误差。并将其应用到中国地区,通过与无线电探空值及欧洲中期天气预报中心ECMWF(European Center ofMed ium-rangeW eather Forecasts)客观分析场的比较,结果表明:该方法所获得的温度、水汽反演结果与探空观测及ECMWF大气廓线分布一致,且AIRS因其高光谱分辨率(即高垂直空间分辨率)显示了精细的大气结构。
The paper retrieves atmospheric temperature and moisture profiles and surface skin temperature from the high-spectral-resolution Atmospheric Infrared Sounder （AIRS） observations with a statistical technique based on principal component analysis. The synthetic regression coefficients for the statistical retrieval are obtained by using a fast radiative transfer model with atmospheric characteristics taken from a dataset of global radiosondes of atmospheric temperature and moisture profiles. Retrievals are evaluated by comparison with radiosonde observations and European Center of Medium-range Weather Forecasts （ECMWF） analyses. AIRS retrievals of temperature and moisture are in general agreement with the distributions from ECMWF analysis fields and radiosonde observations, but the AIRS depicts more detailed structure due to its high spectral resolution （hence, high vertical spatial resolution）.
出处
《南京气象学院学报》
CSCD
北大核心
2006年第6期756-761,共6页
Journal of Nanjing Institute of Meteorology
基金
国家自然科学基金资助项目(40605009)
江苏省自然科学基金资助项目(BK2006575)
关键词
AIRS
特征向量回归
大气廓线反演
atmospheric infrared sounder
principal component regression
atmospheric profile retrieval
