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Channel selection for AIUS temperature inversion based on GF-5

  • role： First author第一作者
  • Affiliation：

    National Key Laboratory of Remote Sensing and Digital Earth Institute, Chinese Academy of Sciences, Beijing 100101, China

    Chinese Academy of Sciences, Beijing 100049, China

  • Email：2025423746@qq.com
  • Introduction：曹西凤，1996生，女,硕士研究生，研究方向为定量遥感的理论和应用。E-mail: 2025423746@qq.com
CAO Xifeng12,  
  • role： Corresponding author通信作者
  • Affiliation：

    National Key Laboratory of Remote Sensing and Digital Earth Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：Lixy01@radi.ac.cn
  • Introduction：李小英，1975生, 女，副研究员，研究方向为太赫兹波段临边探测原理与大气成分反演算法。E-mail: Lixy01@radi.ac.cn
LI Xiaoying1*

résumé

Atmospheric ultraspectral sounder (AIUS) of GF-5 satellite was successfully launched in May 2018. AIUS is the first very high-resolution infrared-band occultation observation sensor developed in China that provides strong data support for investigating atmospheric distribution state. Air temperature is an important parameter that characterizes the thermodynamic state of the atmosphere. Its distribution state directly affects the interaction between long- and short-wave radiation of the earth-atmosphere system and subsequently influences global balance of radiation energy. Direct inversion of temperature using hyperspectral data is limited by its large size, inconvenient storage, and requirement of channel selection due to the correlation between different spectral information. The strong influence of interfering components on the accuracy of actual air temperature inversion results in low inversion accuracy. Channel selection algorithm based on information entropy is combined with sensitivity analysis of target and interfering components in this study to carry out experiments, complete the channel selection of AIUS occultation observation data, and provide a theoretical foundation for future AIUS temperature inversion investigations. First, the sensitivity analysis of target and interference components based on the RFM model is conducted to explore the feasibility of channel selection and perform preliminary channel selection. Second, channel selection is carried out on the basis of information entropy theory, and the results are analyzed and discussed. Finally, the temperature inversion effect is verified using optimization algorithm on the basis of the channel selection results. Occultation observation is highly sensitive to temperature changes, and channel selection is feasible in the occultation observation mode. The increase in number of channels logarithmically increases the amount of information and is close to saturation when the number of channels is equal to 1000. One hundred AIUS channels are selected for temperature inversion to improve operational efficiency and meet precision requirements.

mots-clés

remote sensing;GF-5;AIUS;occultation detection;temperature;sensitivity;channel;information entropy

References

  1. 1.
    Cameron J, Collard A and English S. 2005. Operational use of AIRS observations at the Met Office//Proceedings of the 4th International TOVS Study Conferences. Beijing: [s.n.]: 25-31
  2. 2.
    Crevoisier C, Chedin A and Scott N A. 2003. AIRS channel selection for CO2 and other trace-gas retrievals. Quarterly Journal of the Royal Meteorological Society, 129(593): 2719-2740
  3. 3.
    Du H D, Huang S X and Shi H Q. 2008. Method and experiment of channel selection for high spectral resolution data. Acta Physica Sinica, 57(12): 7685-7692
  4. 4.
    Dudhia A. 2017. The reference forward model (RFM). Journal of Quantitative Spectroscopy and Radiative Transfer, 186: 243-253
  5. 5.
    Fourrié N, Thépaut J N. 2002. Validation of the NESDIS Near-Real-Time AIRS Channel Selection. Reading: European Centre for Medium Range Weather Forecasts
  6. 6.
    He Q R. 2017. Study on Retrieving the Atmospheric Temperature and Humidity Profiles from Measurements of Microwave Humidity and Temperature Sounder on FY-3C Satellite. Beijing: National Space Science Center, the Chinese Academy of Sciences: 1-5
  7. 7.
    Huang J. 2003. The Study on the Algorithm of Retrieving the Temperature and Moisture Profile from Satellite Infrared Measurements. Lanzhou: Lanzhou University: 1-4
  8. 8.
    Jiang D M. 2007. Approach to High Spectral Resolution Infrared Remote Sensing of Atmospheric Temperature and Humidity Profiles. Nanjing: Nanjing University of Information Science and Technology: 1-15
  9. 9.
    Li X Y, Chen L F, Guo A Y, Su L, Jia S L, Tao J H and Zhang Y. 2015. Sub-millimeter wave limb sounding simulation of the plume flow of a high-flying vehicle. Journal of Remote Sensing, 19(1): 54-61
  10. 10.
    Li X Y, Chen L F, Su L, Zhang Y and Tao J H. 2013. Overview of sub-millimeter limb sounding. Journal of Remote Sensing, 17(6): 1325-1344
  11. 11.
    Li X Y, Cheng T H, Xu J, Shi H L, Zhang X Y, Ge S L, Zou M M, Wang H M, Wang Y P, Zhu S Y and Miao J. 2018. Trace gas retrieval from AIUS: algorithm description and O3 retrieval assessment. Preprints
  12. 12.
    Liu H, Dong C H, Zhang W J and Zhang P. 2008. Retrieval of clear-air atmospheric temperature profiles using AIRS observations. Acta Meteorologica Sinica, 66(4): 513-519
  13. 13.
    Rabier F, Fourrié N, Chafäi D and Prunet P. 2002. Channel selection methods for infrared atmospheric sounding interferometer radiances. Quarterly Journal of the Royal Meteorological Society, 128(581): 1011-1027
  14. 14.
    Rodgers C D. 1996. Information content and optimization of high-spectral-resolution measurements//Proceedings of SPIE 2830, Optical Spectroscopic Techniques and Instrumentation for Atmospheric and Space Research II. Denver, CO, United States: SPIE: 136-147
  15. 15.
    Wang H M, Li X Y, Chen L F, Wang Y P, Zhang Y, Zou M M, Yu C and Zhu S Y. 2017. Simulating sensitivity of O3 and HCl with THz limb sounding. Journal of Remote Sensing, 21(5): 653-664
  16. 16.
    Wang H M, Li X Y, Xu J, Zhang X Y, Ge S L, Chen L F, Wang Y P, Zhu S Y, Miao J and Si Y D. 2018. Assessment of retrieved N2O, NO2, and HF profiles from the atmospheric infrared ultraspectral sounder based on simulated spectra. Sensors, 18(7): 2209
  17. 17.
    Wang Y P. 2017. Research on Temperature/Pressure and Ozone Retrieval Algorithm Based on Atmospheric Infrared Ultraspectral Spectrometer. Beijing: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences: 1-7
  18. 18.
    Xu P. 2005. Retrieval of Atmospheric Temperature and Humidity Profiles from NOAA/ATOVS and Data Assimilating Experiments in Meteorological Mesoscale Model. Qingdao: Ocean University of China: 1-4
  19. 19.
    Zhang J W, Wang G, Zhang H, Huang J, Chen J and Wu L L. 2011. Experiment on hyper-spectral atmospheric infrared sounder channel selection based on the cumulative effect coefficient of principal component. Transactions of Atmospheric Science, 34(1): 36-42
  20. 20.
    Zhang K. 2016. New Methods in Retrieving Atmospheric Temperature and Moisture Profiles from Satellite Observations. Beijing: Chinese Academy of Meteorological Sciences: 1-6
  21. 21.
    Zhang S P. 2009. Hyperspectral atmospheric sounding information channel seletion study. Scientia Meteorologica Sinica, 29(4): 475-481
  22. 22.
    Zhou A M. 2017. Atmospheric Temperature and Humidity Profiles Retrieval from Hyperspectral Infrared Simulation Data Based on FY-4. Nanjing: Nanjing University of Information Science and Technology: 1-10
  23. 23.
    Zhou Y C. 2010. Retrieval of Atmospheric Temperature and Humidity Profiles by Ground Based Multi-Channel Microwave Sounders. Beijing: Graduate School of Chinese Academy of Sciences (Space Science and Application Research Center): 1-4

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