Assessment of atmospheric emissivity models for clear-sky conditions with reanalysis data

Abstract

Abstract Atmospheric longwave downward radiation (Ld) is one of the significant components of net radiation (Rn), and it drives several essential ecosystem processes. Ld can be estimated with simple empirical methods using atmospheric emissivity (εa) submodels. In this study, eight εa global models were evaluated, and the one with the best performance was calibrated on a global scale using a parametric instability analysis approach. Climatic data were obtained from a dynamically consistent scale resolution of basic atmospheric quantities and computed parameters known as NCEP/NCAR reanalysis (NNR) data. The model's goodness of fit was evaluated with monthly average values of the NNR data. The εa Brutsaert model resulted in the best performance, and then it was calibrated. The seasonal global trend of Brutsaert’s εa equation calibrated coefficient ranged between 1.2 and 1.4, and five homogeneous zones with similar behavior (clusters) were found with the K-means analysis. Finally, the calibrated Brutsaert’s εa equation improved the Rn estimation, with an error reduction, at the worldwide scale, of 64%. Meanwhile, the error reduction for every cluster ranged from 18 to 77%. Hence, Brutsaert’s equation coefficient should not be considered a constant value for use in εa estimation, nor in time nor space.