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.
Highlights
The information about the atmospheric trace gases can be retrieved from the spectral radiances measured at the top of the atmosphere
The Cluster Low-Streams Regression (CLSR) method is described in detail in [11] and can be summarized as follows: First, let us consider a LBL spectrum {ILS(λi)}Ni=1 computed at N spectral points {λi}Ni=1 by means of a low-stream radiative transfer models (RTMs)
In this paper we examine the application of the CLSR method for several aerosol types and we extend the analysis to the Hartley-Huggins and water vapour bands
Summary
The information about the atmospheric trace gases can be retrieved from the spectral radiances measured at the top of the atmosphere. Alternatives to computationally expensive LBL models are the k-distribution method [2, 3] and the principal component analysis (PCA)-based RTMs [4,5,6,7,8,9], in which the redundancies in hyper-spectral data are eliminated and the spectrum can be computed by using a small number of RTM calls. These methods are reviewed in [10]. The CLSR method is applied to several atmospheric models containing different aerosol types
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