An Adaptive Channel Selection Method for Assimilating the Hyperspectral Infrared Radiances

Linfan Zhou aKey Laboratory of Mesoscale Severe Weather, Ministry of Education, Nanjing University, Nanjing, China
bSchool of Atmospheric Sciences, Nanjing University, Nanjing, China

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Lili Lei aKey Laboratory of Mesoscale Severe Weather, Ministry of Education, Nanjing University, Nanjing, China
bSchool of Atmospheric Sciences, Nanjing University, Nanjing, China
cFrontiers Science Center for Critical Earth Material Cycling, Nanjing University, Nanjing, China

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Jeffrey S. Whitaker dNOAA/Earth System Research Laboratories/Physical Sciences Laboratory, Boulder, Colorado

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Zhe-Min Tan aKey Laboratory of Mesoscale Severe Weather, Ministry of Education, Nanjing University, Nanjing, China
bSchool of Atmospheric Sciences, Nanjing University, Nanjing, China

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Abstract

Hyperspectral infrared (IR) satellites can provide high-resolution vertical profiles of the atmospheric state, which significantly contributes to the forecast skill of numerical weather prediction, especially for regions with sparse observations. One challenge in assimilating the hyperspectral radiances is how to effectively extract the observation information, due to the interchannel correlations and correlated observation errors. An adaptive channel selection method is proposed, which is implemented within the data assimilation scheme and selects the radiance observation with the maximum reduction of variance in observation space. Compared to the commonly used channel selection method based on the maximum entropy reduction (ER), the adaptive method can provide flow-dependent and time-varying channel selections. The performance of the adaptive selection method is evaluated by assimilating only the synthetic Fengyun-4A (FY-4A) GIIRS IR radiances in an observing system simulation experiment (OSSE), with model resolutions from 7.5 to 1.5 km and then 300 m. For both clear-sky and all-sky conditions, the adaptive method generally produces smaller RMS errors of state variables than the ER-based method given similar amounts of assimilated radiances, especially with fine model resolutions. Moreover, the adaptive method has minimum RMS errors smaller than or approaching those with all channels assimilated. For the intensity of the tropical cyclone, the adaptive method also produces smaller errors of the minimum dry air mass and maximal wind speed at different levels, compared to the ER-based selection method.

Significance Statement

Assimilating satellite radiances has been essential for numerical weather prediction. Hyperspectral infrared satellites provide high-resolution vertical profiles for the atmospheric state and can further improve the numerical weather prediction. Due to limited computational resources, and correlated observations and associated errors, efficient and effective ways to assimilate the hyperspectral radiances are needed. An adaptive channel selection method that is incorporated with data assimilation is proposed. The adaptive channel selection can effectively extract the information from hyperspectral radiances under both clear- and all-sky conditions, with increased model resolutions from kilometers to subkilometers.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Lili Lei, lililei@nju.edu.cn

Abstract

Hyperspectral infrared (IR) satellites can provide high-resolution vertical profiles of the atmospheric state, which significantly contributes to the forecast skill of numerical weather prediction, especially for regions with sparse observations. One challenge in assimilating the hyperspectral radiances is how to effectively extract the observation information, due to the interchannel correlations and correlated observation errors. An adaptive channel selection method is proposed, which is implemented within the data assimilation scheme and selects the radiance observation with the maximum reduction of variance in observation space. Compared to the commonly used channel selection method based on the maximum entropy reduction (ER), the adaptive method can provide flow-dependent and time-varying channel selections. The performance of the adaptive selection method is evaluated by assimilating only the synthetic Fengyun-4A (FY-4A) GIIRS IR radiances in an observing system simulation experiment (OSSE), with model resolutions from 7.5 to 1.5 km and then 300 m. For both clear-sky and all-sky conditions, the adaptive method generally produces smaller RMS errors of state variables than the ER-based method given similar amounts of assimilated radiances, especially with fine model resolutions. Moreover, the adaptive method has minimum RMS errors smaller than or approaching those with all channels assimilated. For the intensity of the tropical cyclone, the adaptive method also produces smaller errors of the minimum dry air mass and maximal wind speed at different levels, compared to the ER-based selection method.

Significance Statement

Assimilating satellite radiances has been essential for numerical weather prediction. Hyperspectral infrared satellites provide high-resolution vertical profiles for the atmospheric state and can further improve the numerical weather prediction. Due to limited computational resources, and correlated observations and associated errors, efficient and effective ways to assimilate the hyperspectral radiances are needed. An adaptive channel selection method that is incorporated with data assimilation is proposed. The adaptive channel selection can effectively extract the information from hyperspectral radiances under both clear- and all-sky conditions, with increased model resolutions from kilometers to subkilometers.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Lili Lei, lililei@nju.edu.cn
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