Application of a PCA-Based Fast Radiative Transfer Model to XCO2 Retrievals in the Shortwave Infrared

Abstract

In this work, we extend the principal component analysis (PCA)-based approach to accelerate radiative transfer (RT) calculations by accounting for the spectral variation of aerosol properties. Using linear error analysis, the errors induced by this fast RT method are quantified for a large number of simulated Greenhouse Gases Observing Satellite (GOSAT) measurements (N≈ 30,000). The computational speedup of the approach is typically 2 orders of magnitude compared to a line-by-line discrete ordinates calculation with 16 streams, while the radiance residuals do not exceed 0.01% for the most part compared to the same baseline calculations. We find that the errors due to the PCA-based approach tend to be less than ±0.06 ppm for both land and ocean scenes when two or more empirical orthogonal functions are used. One advantage of this method is that it maintains the high accuracy over a large range of aerosol optical depths. This technique shows great potential to be used in operational retrievals for GOSAT and other remote sensing missions.