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30 August 2023 Research on inversion method of atmospheric temperature and humidity profile based on microwave radiometer
Zixuan Jiao, Bo Wang, Zhiqian Li, Yixiao Sun, Wei Hu, Yingxue Cui
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Proceedings Volume 12797, Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023); 1279729 (2023) https://doi.org/10.1117/12.3007406
Event: 2nd International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023), 2023, Qingdao, China
Abstract
In order to reduce the bias between the measured brightness temperature and the simulated brightness temperature, and improve the detection accuracy of the ground-based microwave radiometer, a first-level data quality control and correction model of the QFW-6000 ground-based microwave radiometer was studied, and the BP neural network model was used to reverse Perform atmospheric temperature and humidity profiles. The experimental results show that after the quality control, the correlation between the measured brightness temperature and the simulated brightness temperature of each channel is significantly improved, and the inversion accuracy of the neural network model is improved to a certain extent after the quality control and correction. The established neural network model improves the inversion accuracy, and the root mean square error ranges of atmospheric temperature and relative humidity are 3.3-6.7K and 15%-23%, respectively.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Zixuan Jiao, Bo Wang, Zhiqian Li, Yixiao Sun, Wei Hu, and Yingxue Cui "Research on inversion method of atmospheric temperature and humidity profile based on microwave radiometer", Proc. SPIE 12797, Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023), 1279729 (30 August 2023); https://doi.org/10.1117/12.3007406
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KEYWORDS
Microwave radiation
Radiometry
Data modeling
Air temperature
Neural networks
Humidity
Relative humidity
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