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  • 副教授
  • 性别:女
  • 毕业院校:中国科学院大学
  • 学历:博士研究生毕业
  • 学位:理学博士学位
  • 在职信息:在岗
  • 所在单位:大气物理学院
  • 办公地点:气象楼805
  • 电子邮箱:003144@nuist.edu.cn
  • 2022-01-10曾获荣誉当选:南京信息工程大学年度优秀教职工
  • 2021-12-01曾获荣誉当选:南京信息工程大学-优秀班主任

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Geostationary hyperspectral infrared sounder channel selection for capturing fast-changing atmospheric information

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DOI码:10.1109/TGRS.2021.3078829

发表刊物:IEEE Transactions on Geoscience and Remote Sensin

刊物所在地:美国

关键字:channel selection; geostationary satellite; hyperspectral infrared (IR) sounder

摘要:Various methodologies have been developed for selecting a subset of channels from a hyperspectral infrared (IR) sounder for assimilation. The information entropy iterative method was considered optimal for channel selection. However, this method only considers the decrease in uncertainty in the atmospheric state caused by measurements at a single time, without considering the dynamic effect of measurements over a period of time; therefore, it might not be optimal for hyperspectral IR sounders onboard geosynchronous satellites that mainly aim to observe rapidly changing weather events. An alternative channel selection method is developed by adding an M index, which reflects the Jacobian variance over time; the adjusted algorithm is ideal for the Geosynchronous Interferometric Infrared Sounder (GIIRS), which is the first high-spectral-resolution advanced IR sounder onboard a geostationary weather satellite. Comparisons between the conventional algorithm (information entropy iterative method) and the adjusted algorithm show that the channels selected from GIIRS by the adjusted algorithm will have larger brightness temperature diurnal variations and better information content than the conventional algorithm, based on the same background error covariance matrix, the observational error covariance matrix, and the channel blacklist. The adjusted algorithm is able to select the channels for monitoring atmospheric temporal variation while retaining the information content from the conventional method. The 1-D variational (1Dvar) retrieval experiment also verifies the superiority of this adjusted algorithm; it indicates that using the channel selected by the adjusted algorithm could enhance the water vapor profile retrieval accuracy, especially for the lower and middle troposphere atmosphere.

全部作者:Di D. , Li J. , Han W., Yin, R.

第一作者:Di Di

通讯作者:李俊

论文编号:10.1109/TGRS.2021.3078829

卷号:60

页面范围:1-10

ISSN号:0196-2892

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发表时间:2021-01-01