Open AccessRetrieval of from simulated Orbiting Carbon Observatory measurements using the fast linearized R‐2OS radiative transfer model
Author(s) -
Natraj Vijay,
Boesch Hartmut,
Spurr Robert J. D.,
Yung Yuk L.
Publication year - 2008
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2007jd009017
Subject(s) - radiative transfer , radiance , physics , scalar (mathematics) , scattering , atmospheric radiative transfer codes , smoothing , polarization (electrochemistry) , computational physics , computation , aerosol , remote sensing , optics , algorithm , meteorology , computer science , mathematics , geometry , geology , statistics , chemistry
In a recent paper, we introduced a novel technique to compute the polarization in a vertically inhomogeneous, scattering‐absorbing medium using a two orders of scattering (2OS) radiative transfer (RT) model. The 2OS computation is an order of magnitude faster than a full multiple scattering scalar calculation and can be implemented as an auxiliary code to compute polarization in operational retrieval algorithms. In this paper, we employ the 2OS model for polarization in conjunction with a scalar RT model (Radiant) to simulate backscatter measurements in near infrared (NIR) spectral regions by space‐based instruments such as the Orbiting Carbon Observatory (OCO). Computations are performed for six different sites and two seasons, representing a variety of viewing geometries, surface and aerosol types. The aerosol extinction (at 13000 cm −1 ) was varied from 0 to 0.3. The radiance errors using the Radiant/2OS (R‐2OS) RT model are an order of magnitude (or more) smaller than errors arising from the use of the scalar model alone. In addition, we perform a linear error analysis study to show that the errors in the retrieved column‐averaged dry air mole fraction of CO 2 using the R‐2OS model are much lower than the “measurement” noise and smoothing errors appearing in the inverse model. On the other hand, we show that use of the scalar model alone induces errors that could dominate the retrieval error budget.