Accelerating radiative transfer calculations in the thermal infrared through principal component analysis of inherent optical properties

Abstract

Principal component analysis (PCA) is a linear mathematical transformation that reduces data dimensionality and enables easy identification of data variability; PCA converts a correlated mean-subtracted data set into a series of principal components (PCs). PCA can be performed on inherent optical properties to enhance the computational efficiency of radiative transfer calculations. Prior work has demonstrated the efficacy of this technique for the computation of radiances and Jacobians in the solar scattering regime, and for estimating broadband fluxes. Here, we extend the methodology to the thermal emission regime. We perform simulations for a nominal (model-based) aerosol scattering scenario, and for a scenario with enhanced aerosol scattering that is representative of strong pollution. The PCA-based approach provides top-of-the-atmosphere radiance results that are typically within 0.1% of numerically exact computations, while enabling a three-orders-of-magnitude speedup. We also investigate the accuracy levels achieved using variable numbers of PCs for different gas absorption optical depth regimes. Results show that more PCs are required for optical depths close to unity, while low and high optical depth scenarios can be simulated accurately with only one PC.