Evaluation of Modeled Hyperspectral Infrared Spectra Against All-Sky AIRS Observations Using Different Cloud Overlap Schemes

Hyperspectral infrared sounding contains information about clouds, which plays an important role in modulating Earth's climate. However, there is a great deal of uncertainty in modeling the radiative effect of clouds due to its complex dependence on various parameters. Therefore, cloudy scenarios are often neglected in retrievals of infrared spectral measurements and in data assimilation. One-dimensional radiative transfer (RT) models have a limited capability to represent the cloud three-dimensional multilayer structure. This issue is typically resolved by using a multiple independent column approach, which is computationally demanding. Therefore, it is necessary to find a balance between computational speed and accuracy for infrared RT all-sky radiance simulations. In this study, we utilize the Community Radiative Transfer Model with four different cloud overlap schemes and compare against observations made by the Atmospheric Infrared Sounder (AIRS) using a statistical metric called the first Wasserstein distance. Our results show that the average cloud overlap scheme performs the best and successfully predicts the overall probability distribution of brightness temperature over nonfrozen oceans for a wide range of wavelengths. The mean absolute differences are less than 0.7 K for 846 selected AIRS channels between 790 cm^-1 and 1231 cm^-1.