A pseudo-spherical linearized radiative transfer model for trace gas profile retrieval

被引:0
|
作者
Walter, H [1 ]
Landgraf, J [1 ]
Trautmann, T [1 ]
机构
[1] SRON, NL-3584 CA Utrecht, Netherlands
来源
REMOTE SENSING OF CLOUDS AND THE ATMOSPHERE VI | 2002年 / 4539卷
关键词
radiative transfer; adjoint formulation; perturbation theory; limb measurements; spherical geometry; profile retrieval; SCIAMACHY;
D O I
10.1117/12.454450
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
The Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) to be launched on ESA's environmental monitoring satellite ENVISAT-1 in November 2001, will measure the backscattered radiances in the UV, VIS and NIR part of the solar spectrum. Apart from its nadir viewing mode, the instrument can also observe in limb viewing geometry. Such measurements will provide information about atmospheric, trace gas distribution with high vertical resolution. A new generation of radiative transfer models is required in order to enable precise retrievals from UV and VIS limb observations. These models must i) take scattering of light in a spherical shell atmosphere into account, and ii) be linearized with respect to the vertical trace gas distribution to be retrieved. As a first approach we present a linearized radiative transfer model in pseudo-spherical approximation, which employs the forward-adjoint perturbation theory. This method is based on two separate radiative transfer calculations: the usual forward and the adjoint formulation. The derivative of the radiance with respect to the trace gas concentration in a homogeneous model layer can easily be calculated, using the internal radiance field of both calculations. Thus, the proposed model carries out both the linearization with respect to changes in the vertical trace gas distribution and the modeling of backscattered radiances with reasonable numerical effort. Backscattered radiances are simulated with an accuracy better than 0.002 %. For the linearization the model achieves an accuracy better than 0.04 % in the derivatives.
引用
收藏
页码:362 / 368
页数:7
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