Expedient Mid-Wave Infrared Band Generation for AGRI during Stray Light Contamination Periods Using a Deep Learning Model.
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| Title: | Expedient Mid-Wave Infrared Band Generation for AGRI during Stray Light Contamination Periods Using a Deep Learning Model. |
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| Authors: | Xiao, Haixia1,2,3 (AUTHOR), Zhuge, Xiaoyong1,2,3 (AUTHOR) zhugexy@cma.gov.cn, Tang, Fei1,3 (AUTHOR), Guo, Jimin4 (AUTHOR) |
| Source: | Journal of Meteorological Research. Feb2025, Vol. 39 Issue 1, p211-222. 12p. |
| Subject Terms: | *Standard deviations, *Stratus clouds, *Brightness temperature, *Autumnal equinox, *Vernal equinox |
| Abstract: | The Advanced Geosynchronous Radiation Imager (AGRI) onboard China's Fengyun (FY)-4 satellites, which provides observational data across various wavelengths from visible to infrared (IR), holds great potential for diverse applications. However, the FY-4A AGRI mid-wave IR (MWIR) band (3.75 µm) is often contaminated by stray light in the midnight hours during the 1–2 months before and after the vernal or autumnal equinoxes. In this study, a U-Net-based deep learning model was employed to generate an expedient MWIR band from the FY-4A AGRI long-wave IR band. Validation using normal radiance measurements revealed that MWIR brightness temperatures generated by the deep learning model are very close to those observed by the FY-4A AGRI, with mean absolute error of 1.48 K, root mean square error of 2.39 K, and a correlation coefficient of 0.99. When applying the model to periods of stray light contamination, the brightness temperature anomalies found in the FY-4A AGRI MWIR band are effectively eliminated. The findings of this study could support various scientific applications that necessitate use of the MWIR band during midnight hours, such as identification of fog/low stratus cloud. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 183454787 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Expedient Mid-Wave Infrared Band Generation for AGRI during Stray Light Contamination Periods Using a Deep Learning Model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Xiao%2C+Haixia%22">Xiao, Haixia</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhuge%2C+Xiaoyong%22">Zhuge, Xiaoyong</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> zhugexy@cma.gov.cn</i><br /><searchLink fieldCode="AR" term="%22Tang%2C+Fei%22">Tang, Fei</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Guo%2C+Jimin%22">Guo, Jimin</searchLink><relatesTo>4</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Meteorological+Research%22">Journal of Meteorological Research</searchLink>. Feb2025, Vol. 39 Issue 1, p211-222. 12p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink><br />*<searchLink fieldCode="DE" term="%22Stratus+clouds%22">Stratus clouds</searchLink><br />*<searchLink fieldCode="DE" term="%22Brightness+temperature%22">Brightness temperature</searchLink><br />*<searchLink fieldCode="DE" term="%22Autumnal+equinox%22">Autumnal equinox</searchLink><br />*<searchLink fieldCode="DE" term="%22Vernal+equinox%22">Vernal equinox</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The Advanced Geosynchronous Radiation Imager (AGRI) onboard China's Fengyun (FY)-4 satellites, which provides observational data across various wavelengths from visible to infrared (IR), holds great potential for diverse applications. However, the FY-4A AGRI mid-wave IR (MWIR) band (3.75 µm) is often contaminated by stray light in the midnight hours during the 1–2 months before and after the vernal or autumnal equinoxes. In this study, a U-Net-based deep learning model was employed to generate an expedient MWIR band from the FY-4A AGRI long-wave IR band. Validation using normal radiance measurements revealed that MWIR brightness temperatures generated by the deep learning model are very close to those observed by the FY-4A AGRI, with mean absolute error of 1.48 K, root mean square error of 2.39 K, and a correlation coefficient of 0.99. When applying the model to periods of stray light contamination, the brightness temperature anomalies found in the FY-4A AGRI MWIR band are effectively eliminated. The findings of this study could support various scientific applications that necessitate use of the MWIR band during midnight hours, such as identification of fog/low stratus cloud. [ABSTRACT FROM AUTHOR] |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s13351-025-4107-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 211 Subjects: – SubjectFull: Standard deviations Type: general – SubjectFull: Stratus clouds Type: general – SubjectFull: Brightness temperature Type: general – SubjectFull: Autumnal equinox Type: general – SubjectFull: Vernal equinox Type: general Titles: – TitleFull: Expedient Mid-Wave Infrared Band Generation for AGRI during Stray Light Contamination Periods Using a Deep Learning Model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Xiao, Haixia – PersonEntity: Name: NameFull: Zhuge, Xiaoyong – PersonEntity: Name: NameFull: Tang, Fei – PersonEntity: Name: NameFull: Guo, Jimin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20956037 Numbering: – Type: volume Value: 39 – Type: issue Value: 1 Titles: – TitleFull: Journal of Meteorological Research Type: main |
| ResultId | 1 |
