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Retrieval of Surface Spectral Emissivity in Polar Regions Based on the Optimal Estimation Method
dc.contributor.authorXie, Yan
dc.contributor.authorHuang, Xianglei
dc.contributor.authorChen, Xiuhong
dc.contributor.authorL’ecuyer, Tristan S.
dc.contributor.authorDrouin, Brian J.
dc.contributor.authorWang, Jun
dc.date.accessioned2022-03-07T03:14:10Z
dc.date.available2023-04-06 22:14:08en
dc.date.available2022-03-07T03:14:10Z
dc.date.issued2022-03-16
dc.identifier.citationXie, Yan; Huang, Xianglei; Chen, Xiuhong; L’ecuyer, Tristan S. ; Drouin, Brian J.; Wang, Jun (2022). "Retrieval of Surface Spectral Emissivity in Polar Regions Based on the Optimal Estimation Method." Journal of Geophysical Research: Atmospheres 127(5): n/a-n/a.
dc.identifier.issn2169-897X
dc.identifier.issn2169-8996
dc.identifier.urihttps://hdl.handle.net/2027.42/171894
dc.description.abstractSurface spectral emissivity plays an important role in the polar radiation budget. The significance of surface emissivity in the far- infrared (far- IR) has been recognized by recent studies, yet there have been no observations to constrain far- IR surface spectral emissivity over the entire polar regions. In preparation for the Polar Radiant Energy in the Far- InfraRed Experiment (PREFIRE) mission, this study develops and assesses an optimal estimation- based retrieval algorithm to estimate both mid- IR and far- IR polar surface emissivity from the future PREFIRE measurements. Synthetic PREFIRE spectra are simulated by feeding the ERA5 reanalysis and a global surface emissivity data set to a radiative transfer model. Information content analysis indicates that the far- IR surface emissivity retrievals can be more influenced by the atmospheric water vapor abundance than the mid- IR counterparts. When the total column water vapor is above 1 cm, the far- IR surface emissivity retrievals largely rely on the a priori constraints. Performance of the optimal- estimation algorithm is assessed using 960 synthetic PREFIRE clear- sky radiance spectra over the Arctic. The results based on current best estimate of instrument performance show that all retrievals converge within 15 iterations, the retrieved surface spectral emissivity has a mean bias within ±0.01 and a root- mean- square error less than 0.024. The far- IR surface emissivity retrievals are much more affected by the a priori choice than the mid- IR ones. A properly constructed a priori covariance can also help to improve the computational efficiency. Influences of other factors for future operational retrievals are also discussed.Key PointsAn optimal- estimation algorithm for surface spectral emissivity retrieval is developed and assessed for the forthcoming PREFIRE missionSurface spectral emissivity retrievals in the far- infrared can be significantly influenced by the atmospheric water vapor abundanceCompared to the mid- infrared, the far- infrared surface emissivity retrievals are more affected by the choice of a priori constraints
dc.publisherThe TIMS Data User- s Workshop
dc.publisherWiley Periodicals, Inc.
dc.subject.otheroptimal estimation
dc.subject.othersurface spectral emissivity
dc.subject.otherPREFIRE mission
dc.subject.otherfar- IR
dc.titleRetrieval of Surface Spectral Emissivity in Polar Regions Based on the Optimal Estimation Method
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelAtmospheric and Oceanic Sciences
dc.subject.hlbtoplevelScience
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/171894/1/jgrd57653.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/171894/2/jgrd57653_am.pdf
dc.identifier.doi10.1029/2021JD035677
dc.identifier.sourceJournal of Geophysical Research: Atmospheres
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