Radiometrically Consistent Climate Fingerprinting Using CrIS and AIRS Hyperspectral Observations
We introduce a novel spectral fingerprinting scheme that can be used to derive long-term atmospheric temperature and water vapor anomalies from hyperspectral infrared sounders such as Cross-track Infrared Sounder (CrIS) and Atmospheric Infrared Sounder (AIRS). It is a challenging task to derive clim...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/8/1291 |
| _version_ | 1797570279067615232 |
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| author | Wan Wu Xu Liu Qiguang Yang Daniel K. Zhou Allen M. Larar |
| author_facet | Wan Wu Xu Liu Qiguang Yang Daniel K. Zhou Allen M. Larar |
| author_sort | Wan Wu |
| collection | DOAJ |
| description | We introduce a novel spectral fingerprinting scheme that can be used to derive long-term atmospheric temperature and water vapor anomalies from hyperspectral infrared sounders such as Cross-track Infrared Sounder (CrIS) and Atmospheric Infrared Sounder (AIRS). It is a challenging task to derive climate trends from real satellite observations due to the difficulty of carrying out accurate cloudy radiance simulations and constructing radiometrically consistent radiative kernels. To address these issues, we use a principal component based radiative transfer model (PCRTM) to perform multiple scattering calculations of clouds and a PCRTM-based physical retrieval algorithm to derive radiometrically consistent radiative kernels from real satellite observations. The capability of including the cloud scattering calculations in the retrieval process allows the establishment of a rigorous radiometric fitting to satellite-observed radiances under all-sky conditions. The fingerprinting solution is directly obtained via an inverse relationship between the atmospheric anomalies and the corresponding spatiotemporally averaged radiance anomalies. Since there is no need to perform Level 2 retrievals on each individual satellite footprint for the fingerprinting approach, it is much more computationally efficient than the traditional way of producing climate data records from spatiotemporally averaged Level 2 products. We have applied the spectral fingerprinting method to six years of CrIS and 16 years of AIRS data to derive long-term anomaly time series for atmospheric temperature and water vapor profiles. The CrIS and AIRS temperature and water vapor anomalies derived from our spectral fingerprinting method have been validated using results from the PCRTM-based physical retrieval algorithm and the AIRS operational retrieval algorithm, respectively. |
| first_indexed | 2024-03-10T20:22:40Z |
| format | Article |
| id | doaj.art-d7af2d0d05274976ac581b040c920fba |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:22:40Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d7af2d0d05274976ac581b040c920fba2023-11-19T22:04:16ZengMDPI AGRemote Sensing2072-42922020-04-01128129110.3390/rs12081291Radiometrically Consistent Climate Fingerprinting Using CrIS and AIRS Hyperspectral ObservationsWan Wu0Xu Liu1Qiguang Yang2Daniel K. Zhou3Allen M. Larar4Science Systems and Applications, Inc. (SSAI), Hampton, VA 23666, USANASA Langley Research Center, Hampton, VA 23681, USAScience Systems and Applications, Inc. (SSAI), Hampton, VA 23666, USANASA Langley Research Center, Hampton, VA 23681, USANASA Langley Research Center, Hampton, VA 23681, USAWe introduce a novel spectral fingerprinting scheme that can be used to derive long-term atmospheric temperature and water vapor anomalies from hyperspectral infrared sounders such as Cross-track Infrared Sounder (CrIS) and Atmospheric Infrared Sounder (AIRS). It is a challenging task to derive climate trends from real satellite observations due to the difficulty of carrying out accurate cloudy radiance simulations and constructing radiometrically consistent radiative kernels. To address these issues, we use a principal component based radiative transfer model (PCRTM) to perform multiple scattering calculations of clouds and a PCRTM-based physical retrieval algorithm to derive radiometrically consistent radiative kernels from real satellite observations. The capability of including the cloud scattering calculations in the retrieval process allows the establishment of a rigorous radiometric fitting to satellite-observed radiances under all-sky conditions. The fingerprinting solution is directly obtained via an inverse relationship between the atmospheric anomalies and the corresponding spatiotemporally averaged radiance anomalies. Since there is no need to perform Level 2 retrievals on each individual satellite footprint for the fingerprinting approach, it is much more computationally efficient than the traditional way of producing climate data records from spatiotemporally averaged Level 2 products. We have applied the spectral fingerprinting method to six years of CrIS and 16 years of AIRS data to derive long-term anomaly time series for atmospheric temperature and water vapor profiles. The CrIS and AIRS temperature and water vapor anomalies derived from our spectral fingerprinting method have been validated using results from the PCRTM-based physical retrieval algorithm and the AIRS operational retrieval algorithm, respectively.https://www.mdpi.com/2072-4292/12/8/1291climate fingerprintinglong term recordinfrared soundershyperspectral retrieval algorithms |
| spellingShingle | Wan Wu Xu Liu Qiguang Yang Daniel K. Zhou Allen M. Larar Radiometrically Consistent Climate Fingerprinting Using CrIS and AIRS Hyperspectral Observations Remote Sensing climate fingerprinting long term record infrared sounders hyperspectral retrieval algorithms |
| title | Radiometrically Consistent Climate Fingerprinting Using CrIS and AIRS Hyperspectral Observations |
| title_full | Radiometrically Consistent Climate Fingerprinting Using CrIS and AIRS Hyperspectral Observations |
| title_fullStr | Radiometrically Consistent Climate Fingerprinting Using CrIS and AIRS Hyperspectral Observations |
| title_full_unstemmed | Radiometrically Consistent Climate Fingerprinting Using CrIS and AIRS Hyperspectral Observations |
| title_short | Radiometrically Consistent Climate Fingerprinting Using CrIS and AIRS Hyperspectral Observations |
| title_sort | radiometrically consistent climate fingerprinting using cris and airs hyperspectral observations |
| topic | climate fingerprinting long term record infrared sounders hyperspectral retrieval algorithms |
| url | https://www.mdpi.com/2072-4292/12/8/1291 |
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