A fast and accurate vector radiative transfer model for simulating the near-infrared hyperspectral scattering processes in clear atmospheric conditions. (February 2020)

• Record Type: Journal Article
• Authors: Bai, Wenguang; Zhang, Peng; Zhang, Wenjian; Ma, Gang; Qi, Chengli; Liu, Hui
• Is Part Of: Journal of quantitative spectroscopy & radiative transfer. Volume 242(2020)
• Publication Date: 2020-02
• Subjects: Satellite near-infrared CO2 remote sensing -- Fast vector multiple scattering radiative transfer calculation -- Featured monochromatic radiances; Spectrum analysis -- Periodicals; Radiation -- Periodicals; Analyse spectrale -- Périodiques; Rayonnement -- Périodiques; Radiation; Spectrum analysis; Periodicals; 543.0858
• DOI: 10.1016/j.jqsrt.2019.106736
• Languages: English
• ISSNs: 0022-4073

Abstract

Highlights: Proposed a method for efficient and accurate calculations of vector multiple scattering in near-infrared band. Accuracy of 0.1% is achieved with computation speed improved by 1–2 order. Capture the realistic characteristics of atmospheric radiation from measurements of near-infrared band. Abstract: This study develops a Weighting Featured Monochromatic Radiance (WFMR) method to efficiently and accurately compute the vector radiance, including multiple scattering at the near-infrared band. The monochromatic radiances within a spectral band that are of a high correlation are grouped to represent the average band radiance. The simulated radiance from WFMR is validated against a large variety of OCO-2 (the Orbiting Carbon Observation 2) profiles. It is shown that the dominant errors introduced by the WFMR method are below 0.1%, 0.025%, and 0.12% for O2 A band, weak and strong CO2 absorption bands, respectively, and the computational speed is, however, about 80 times faster than that of the reference line-by-line calculation. The technique performs very well and captures the realistic characteristics of the atmospheric radiation distribution. Besides, for the WFMR method, its numerical accuracy can be defined, and its linearization to form the Jacobians matrix is very straightforward. These advantages are particularly well suited for remote sensing applications.