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

Abstract

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.