|
|
|
|
|
|
A Study on the Computational Model for High Spectral Infrared Sounder by Fourier Transform Technique and its Influence Factors |
WANG Qi, LIU Lei*, GAO Tai-chang, HU Shuai, ZENG Qing-wei |
College of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, China |
|
|
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.
|
Received: 2018-04-17
Accepted: 2018-09-19
|
|
Corresponding Authors:
LIU Lei
E-mail: liuleidll@gmail.com
|
|
[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. |
[1] |
TAO Jing-zhe1, 3, SONG De-rui1, 3, SONG Chuan-ming2, WANG Xiang-hai1, 2*. Multi-Band Remote Sensing Image Sharpening: A Survey[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 2999-3008. |
[2] |
LI Yu-tang1, WANG Lin-zhu1, 2*, LI Xiang3, WANG Jun1. Characterization and Comparative Analysis of Non-Metallic Inclusions in Zirconium Deoxidized Steel[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2916-2921. |
[3] |
SUN Bang-yong1, YU Meng-ying1, YAO Qi2*. Research on Spectral Reconstruction Method From RGB Imaging Based on Dual Attention Mechanism[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2687-2693. |
[4] |
DU Guo-jun, ZHANG Yu-gui, CUI Bo-lun, JIANG Cheng, OU Zong-yao. Spectral Calibration of Hyperspectral Monitor (HSM) on Carbonsat[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1556-1562. |
[5] |
LI Hu1, 2, 3, LIU Xue-feng1, 3*, YAO Xu-ri4, 5*, ZHAI Guang-jie1, 3. Block Compressed Sensing Computed-Tomography Imaging Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 348-355. |
[6] |
CHU Zhi-hong1, 2, ZHANG Yi-zhu2, QU Qiu-hong3, ZHAO Jin-wu1, 2, HE Ming-xia1, 2*. Terahertz Spectral Imaging With High Spatial Resolution and High
Visibility[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 356-362. |
[7] |
XIE Ying-ke1, 2, WANG Xi-chen2, LIANG Heng-heng2, WEN Quan3. A Near-Infrared Micro-Spectrometer Based on Integrated Scanning
Grating Mirror and Improved Asymmetric C-T Structure[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 563-568. |
[8] |
ZHU Wen-qing1, 2, 3, ZHANG Ning1, 2, 3, LI Zheng1, 2, 3*, LIU Peng1, 3, TANG Xin-yi1, 3. A Multi-Task Convolutional Neural Network for Infrared and Visible Multi-Resolution Image Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 289-296. |
[9] |
DENG Xian-ze1, 2, DENG Xi-guang1, 2*, YANG Tian-bang1, 2, CAI Zhao3, REN Jiang-bo1, 2, ZHANG Li-min1, 2. To Reveal the Occurrence States and Enrichment Mechanisms of Metals in Modules From Clarion-Clipperton Zone in Eastern Pacific by High
Resolution Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2522-2527. |
[10] |
WANG Chong, DU Huan, WANG Jing, WANG Jing, WANG Jing-hua. Using Fiber Grating Cascade Structure to Realize Fiber Delay Line[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 2241-2246. |
[11] |
LI Jin-hua1, 2, ZHANG Min-juan1, 2, WANG Zhi-bin1, 2, LI Shi-zhong1, 2*. The Effect of Instrument Resolution on Passive Ranging of Oxygen A Band[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1974-1978. |
[12] |
FAN Xian-guang1, 2, HUANG Yan-rui1, LIU Long1, XU Ying-jie1, WANG Xin1, 2*. An Interpolation Method for Raman Imaging Using Voigt Function[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1478-1483. |
[13] |
HU Li-hong1, ZHANG Jin-tong1, WANG Li-yun2, ZHOU Gang3, WANG Jiang-yong1*, XU Cong-kang1*. Optimization of Working Parameters of Glow Discharge Optical Emission Spectrometry of High Barrier Aluminum Plastic Film[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 954-960. |
[14] |
HUANG Han1, CHEN Hong-yan2*, LI Xiao-lu1, LIU Jia-hao1, ZHAO Yong-jia2, CHEN Liang3. Calculation and Study of Methane Absorption Coefficient at Variable Pressure and Temperature Under 3 016.49 cm-1 Wave Number[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2462-2468. |
[15] |
LIU Yang 1, 2, 3, 4, FENG Hai-kuan1, 3, 4*, SUN Qian1, 3, 4, YANG Fu-qin5, YANG Gui-jun1, 3, 4. Estimation Study of Above Ground Biomass in Potato Based on UAV Digital Images With Different Resolutions[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(05): 1470-1476. |
|
|
|
|