Abstract
Abstract. This study introduces an effective channel selection method for hyperspectral infrared sounders. The method is illustrated for the Atmospheric InfraRed Sounder (AIRS) instrument. The results are as follows. (1) Using the improved channel selection (ICS), the atmospheric retrievable index is more stable, with the value reaching 0.54. The coverage of the weighting functions is more evenly distributed over height with this method. (2) Statistical inversion comparison experiments show that the accuracy of the retrieval temperature, using the improved channel selection method in this paper, is consistent with that of 1D-Var channel selection. In the stratosphere and mesosphere especially, from 10 to 0.02 hPa, the accuracy of the retrieval temperature of our improved channel selection method is improved by about 1 K. The accuracy of the retrieval temperature of ICS is also improved at lower heights. (3) Statistical inversion comparison experiments for four different regions illustrate latitudinal and seasonal variations and better performance of ICS compared to the numerical weather prediction (NWP) channel selection (NCS) and primary channel selection (PCS) methods. The ICS method shows potential for future applications.
Highlights
Since the successful launch of the first meteorological satellite, TIROS, in the 1960s, satellite observation technology has developed rapidly
(2) Statistical inversion comparison experiments show that the accuracy of the retrieval temperature, using the improved channel selection method in this paper, is consistent with that of 1D-Var channel selection
(3) Statistical inversion comparison experiments for four different regions illustrate latitudinal and seasonal variations and better performance of improved channel selection (ICS) compared to the numerical weather prediction (NWP) channel selection (NCS) and primary channel selection (PCS) methods
Summary
Since the successful launch of the first meteorological satellite, TIROS, in the 1960s, satellite observation technology has developed rapidly. Today’s main methods for channel selection use only the weighting function to study appropriate numerical methods, such as the data precision matrix method (Menke, 1984), singular value decomposition method (Prunet et al, 2010; Zhang et al, 2011; Wang et al, 2014) and the Jacobi method (Aires et al, 1999; Rabier et al, 2010). Channel selection mostly uses the information content and delivers the largest amount of information for the selected channel combination during the retrieval (Rodgers, 1996; Du et al, 2008; He et al, 2012; Richardson et al, 2018) This method has made great breakthroughs in both theory and practice, and the concept of information content itself does consider all the height dependencies of the kernel matrix K (Rodgers, 2000). Statistical inversion comparison experiments are used to verify the effectiveness of the method
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