摘要： 在基于红外高光谱辐射数据进行大气遥感方面的研究中，准确模拟红外高光谱数据是很重要的一步。分析了红外高光谱辐射仪的测量原理，建立了基于Atmospheric Radiation Transfer Simulator(ARTS）的考虑仪器干涉图截断与离散化处理过程的正向模型。在该正向模型中，首先采用高光谱辐射传输模式ARTS模拟得到离散化理想光谱，通过逆傅里叶变换将理想光谱转化为干涉图，对干涉图加窗截断处理，模拟仪器响应函数对干涉图的影响，最后采用傅里叶变换得到仪器测量光谱。在这一过程中，窗口函数的选择取决于仪器的干涉图截断方式。未经过切趾处理的仪器，其对应的窗口函数为矩形窗口；经过切趾函数处理，可以减少干涉图截断造成的能量泄露现象。逆傅里叶变换与傅里叶变换过程中必须满足Nyquist采样定律。基于已建立的正向模型，模拟了Atmospheric Emitted Radiance Interferometer (AERI)在Southern Great Plains (SGP)站点的108组晴空辐射数据，并与AERI的实测结果进行比较分析，结果发现理想光谱与AERI实测光谱在吸收线上差异较大，最大残差达到35 mW·sr-1·m-2·(cm-1)-1（简称RU）以上，增加干涉图截断过程后，模拟光谱与实测光谱的最大残差减小到10 RU以内。截断过程的增加对模拟光谱的精度有明显提高，尤其在吸收线上，模拟光谱明显被平滑，模拟精度显著提高。进一步分析六种常用窗口函数截断处理的结果与AERI实测数据的残差，结果发现，模拟过程中选择窗口函数为矩形窗口时，模拟光谱与AERI实测数据残差最小，基本可以约束在5 RU以内，确定了AERI的干涉图截断方式可以近似看作矩形截断。另外，在理想光谱转换为干涉图的过程中，理想光谱分辨率的选择决定了干涉图信息的采样率以及ARTS的计算效率，因此综合考虑模型计算精度和模型计算效率，确定最佳的理想光谱分辨率对于提高模型计算效能是非常必要的；基于此，本文模拟了不同理想光谱分辨率下的仪器测量光谱，对比分析了模拟光谱与AERI实测光谱的残差分布，并讨论了光谱分辨率对模型计算耗时的影响。结果表明，对于AERI，在对应的正向模型中设置理想光谱分辨率为0.241 1 cm-1时，可在保证模型准确度的前提下，最大化模型计算效率。
关键词：辐射传输模式；正向模型；切趾函数；分辨率
Abstract：In the research of atmospheric remote sensing based on the hyper-spectral infrared radiance, it is an important step to accurately simulate the hyper-spectral infrared radiance. In this paper, the measurement principle of hyper-spectral infrared radiometer is analyzed, and a forward model is established based on ARTS by concerning about the process of interferograms-truncated and discretization. In the forward model, an ideal discretization spectrum was simulated by ARTS firstly, and then the spectrum was transformed into an interference figure through the inverse Fourier transform. After the interference figure was truncated by a specific window function, the Fourier transform was further applied to obtain the simulated spectrum (called “target spectrum” in this paper). During this process, the window function type is dependent on the method of truncation of interference figure in the instrument measurement, (for example, a rectangular function corresponds to an interference figure without a truncation process by apodization function), and both the inverse Fourier transform and the Fourier transform must satisfy the Nyquist sampling law. Based on the forward model, 108 groups of clear sky radiation data in SGP site have been simulated, and the simulation results were compared with the actual measurement results of AERI. The results showed that there was notable difference between ideal spectrum and the spectrum measured by AERI on the gases absorption line, and the maximum residual reached around 35 RU. When a truncation process was added to the simulated spectrum, the maximum residual was constrained within 10 RU, indicating that the truncation process can improve accuracy of the simulated spectrum, especially in the gases absorption lines. Furthermore, the simulated spectrum obtained from six commonly used window function was compared with the spectrum measured by AERI. The results showed that the spectrum processed with rectangular window was most close to AERI measured spectrum, which meant that the window function used in AERI can be seen as a rectangle function. Because the ideal spectral resolution determines the sample rate of the interference figure and computation efficiency of ARTS in transformation of the ideal spectrum to interference figure, it is necessary to find an appropriate ideal spectral resolution to guarantee both the modeling accuracy and efficiency. For this purpose, instrument measurement spectrum were simulated with different ideal spectral resolution, and the residuals between simulated radiations and AERI measured radiations were analyzed. Meanwhile, the influence of spectral resolution on the computational time was discussed. The results showed that when the ideal spectral resolution is set as 0.241 1 cm-1 in the forward mode, the model calculation efficiency can be maximized on the premise of modeling accuracy.
Key words：Radiative transfer model; Forward model; Apodizing function; Resolution
王 琦，刘 磊，高太长，胡 帅，曾庆伟. 基于ARTS的傅里叶红外高光谱计算模型研究及其影响因素分析[J]. 光谱学与光谱分析, 2019, 39(06): 1711-1716.
WANG Qi, LIU Lei, GAO Tai-chang, HU Shuai, ZENG Qing-wei. A Study on the Computational Model for High Spectral Infrared Sounder by Fourier Transform Technique and its Influence Factors. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(06): 1711-1716.
[1] LIU Yang, GUAN Li（刘 旸, 官 莉）. Meteorological Monthly（气象）, 2011, 37(3): 318.
[2] QI Cheng-li, GU Ming-jian, HU Xiu-qing, et al（漆成莉, 顾明剑, 胡秀清, 等）. Advances in Meteorological Science and Technology（气象科技进展）, 2016, 6(1): 88.
[3] Griffith D W T, Deutscher N M, Caldow C, et al. Atmospheric Measurement Techniques Discussions, 2012, 5(3): 3717.
[4] WANG Jian-yu, LI Chun-lai, JI Hong-zhen（王建宇, 李春来, 姬弘桢, 等）. Journal of Infrared and Milimeter Waves（红外与毫米波学报）, 2015, 34(1): 51.
[5] Lee L, Chen H, Chen S, et al. Applied Optics, 2012, 51(20): 4622.
[6] Ruzmaikin A, Aumann H H, Manning E M. Journal of the Atmospheric Sciences, 2014, 71(7): 2516.
[7] Hilton F, Armante R, August T, et al. Bulletin of the American Meteorological Society, 2012, 93(3): 347.
[8] Marshall J L, Jung J, Lord S, et al. Bulletin of the American Meteorological Society, 2015, 88(3): 329.
[9] Qi C, Gu M, Wu C, et al. Calibration Method of High Spectral Infrared Atmospheric Sounder Onboard FY-3D Satellite, 3rd International Symposium of Space Optical Instruments and Applications, 2017.
[10] Dong C, Yang J, Zhang W, et al. Bulletin of the American Meteorological Society, 2010, 90(10): 1531.
[11] Gero P J, Turner D D. Journal of Climate, 2011, 24(18): 4831.
[12] Wang G, Zhang J. Advances in Space Research, 2014, 54(1): 49.
[13] Li Shulei, Liu L, Gao T. Journal of Atmospheric & Environmental Optics, 2016.
[14] Long C N, McFarlane S A, Genio A D, et al. Bulletin of the American Meteorological Society, 2013, 94(5): 695.
