Laser & Optoelectronics Progress, Volume. 62, Issue 7, 0701001(2025)
Neural Network Inversion Method for Atmospheric Temperature and Relative Humidity Profiles Based on FY-3E/HIRAS
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Fig. 2. Variation in temperature inversion error with height on the training set. (a) Clear sky; (b) cloudy sky
Fig. 3. Variation in temperature inversion error with height on the validation set. (a) Clear sky; (b) cloudy sky
Fig. 4. Variation in RMSE of temperature inversion results with height on the training set. (a) Clear sky; (b) cloudy sky
Fig. 5. Variation in RMSE of temperature inversion results with height on the validation set. (a) Clear sky; (b) cloudy sky
Fig. 6. Variation in relative humidity inversion error with height on the training set. (a) Clear sky; (b) cloudy sky
Fig. 7. Variation in relative humidity inversion error with height on the validation set. (a) Clear sky; (b) cloudy sky
Fig. 8. Variation in RMSE of relative humidity inversion with height in the training set. (a) Clear sky; (b) cloudy sky
Fig. 9. Variation in RMSE of relative humidity inversion with height in the validation set. (a) Clear sky; (b) cloudy sky
Table 1. Parameters of BPNN inversion model
View tableTable 1. Parameters of BPNN inversion model
Parameter S ettingHidden layer transfer function tansig Output layer transfer function purelin Training Algorithm trainscg Number of nodes in the input layer 220 Number of nodes in the hidden layer 300
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Yike Zou, Ying Wu, Jingwen Ma, Yuanyuan Huang, Jinghui Ning, Qijia Fu. Neural Network Inversion Method for Atmospheric Temperature and Relative Humidity Profiles Based on FY-3E/HIRAS[J]. Laser & Optoelectronics Progress, 2025, 62(7): 0701001
Paper Information
Category: Atmospheric Optics and Oceanic Optics
Received: Sep. 18, 2024
Accepted: Nov. 5, 2024
Published Online: Mar. 14, 2025
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CSTR:32186.14.LOP242009
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