Retrieval of Surface Spectral Emissivity in Polar Regions Based on the Optimal Estimation Method

• Main Authors: Xie, Yan, Huang, Xianglei, Chen, Xiuhong, L’ecuyer, Tristan S., Drouin, Brian J., Wang, Jun
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: The TIMS Data User- s Workshop 2022
• Subjects: optimal estimation; surface spectral emissivity; PREFIRE mission; far- IR; Atmospheric and Oceanic Sciences; Science; Arctic
• Online Access: https://hdl.handle.net/2027.42/171894; https://doi.org/10.1029/2021JD035677

Surface 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 ...