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Laser & Optoelectronics Progress, Volume. 58, Issue 17, 1701002(2021)

Research on Retrieval of Temperature Profile on Cloud Based on FY-4A/GIIRS Data

Pengyu Huang1,3, Qiang Guo1,2、*, Changpei Han1, Chunming Zhang1,3, Tianhang Yang1, and Shuo Huang1
Author Affiliations
  • 1Key Laboratory of Infrared System Detection and Imaging Technologies, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
  • 2National Satellite Meteorological Center, Beijing 100081, China
  • 3University of Chinese Academy of Sciences, Beijing 100049, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (9)
    Equations (5)
    References (26)
    Cited By (2)
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    Figures & Tables(9)
    Schematic diagram of channel weight function

    Fig. 1. Schematic diagram of channel weight function

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    Schematic diagram of neural networks

    Fig. 2. Schematic diagram of neural networks

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    Flow chart of channel optimization

    Fig. 3. Flow chart of channel optimization

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    Flow chart of retrieval of temperature profiles on cloud

    Fig. 4. Flow chart of retrieval of temperature profiles on cloud

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    Root mean square error of simulated brightness temperature before and after adding stratus and cumulus pollution at 975 hPa. (a) Stratus; (b) cumulus

    Fig. 5. Root mean square error of simulated brightness temperature before and after adding stratus and cumulus pollution at 975 hPa. (a) Stratus; (b) cumulus

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    Channel simulation brightness temperature changes after adding cloud pollution with different cloud top pressures. (a) Stratus ME; (b) stratus RMSE; (c) cumulus ME; (d) cumulus RMSE

    Fig. 6. Channel simulation brightness temperature changes after adding cloud pollution with different cloud top pressures. (a) Stratus ME; (b) stratus RMSE; (c) cumulus ME; (d) cumulus RMSE

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    Channel optimization

    Fig. 7. Channel optimization

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    Verification of retrieval accuracy of BP neural network method. (a) (b) (c) Retrieval accuracy of training data; (d) (e) (f) retrieval accuracy of test data

    Fig. 8. Verification of retrieval accuracy of BP neural network method. (a) (b) (c) Retrieval accuracy of training data; (d) (e) (f) retrieval accuracy of test data

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    • Table 1. FY-4A/GIIRS instrument performance parameters

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      Table 1. FY-4A/GIIRS instrument performance parameters

      ParameterIndicator
      Spectral range /cm-1700-1130（long wave）；1650-2250（medium wave）
      Spectral resolution /cm-10.625
      Number of channels689（long wave）；961（medium wave）
      Sensitivity /（mW·m-2·sr-1·cm）0.5-1.12（long wave）；0.1-0.14（medium wave）
      Spatial resolution /km16（Nadir）
      Time resolution /h

      <1（China regions）

      <1/2（Meso-small scale）

      Area of detection /（km×km）

      5000×5000（China regions）

      1000×1000（Meso-small scale）

      Spectral calibration accuracy10-5
      Radiometric calibration accuracy /K1.5
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    Pengyu Huang, Qiang Guo, Changpei Han, Chunming Zhang, Tianhang Yang, Shuo Huang. Research on Retrieval of Temperature Profile on Cloud Based on FY-4A/GIIRS Data[J]. Laser & Optoelectronics Progress, 2021, 58(17): 1701002

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    Paper Information

    Category: Atmospheric Optics and Oceanic Optics

    Received: Dec. 20, 2020

    Accepted: Jan. 13, 2021

    Published Online: Sep. 1, 2021

    The Author Email: Guo Qiang (guoqiang@cma.gov.cn)

    DOI:10.3788/LOP202158.1701002

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology