The Cluster Low-Streams Regression Method for Fast Computations of Top-of-the-Atmosphere Radiances in Absorption Bands

Abstract

Atmospheric composition sensors provide a huge amount of data. A key component of trace gas retrieval algorithms are radiative transfer models (RTMs), which are used to simulate the spectral radiances in the absorption bands. Accurate RTMs based on line-by-line techniques are time-consuming. In this paper we analyze the efficiency of the cluster low-streams regression (CLSR) technique to accelerate computations in the absorption bands. The idea of the CLRS method is to use the fast two-stream RTM model in conjunction with the line-by-line model and then to refine the results by constructing the regression model between two- and multi-stream RTMs. The CLSR method is applied to the Hartley-Huggins, O2 A-, water vapour and CO2 bands for the clear sky and several aerosol types. The median error of the CLSR method is below 0.001 %, the interquartile range (IQR) is below 0.1 %, while the performance enhancement is two orders of magnitude.