To study and analyze the weight of atmospheric radiative bright temperature with the whole tropospheric atmospheric radiative bright temperature at different integration heights, this paper uses sounding data from Shanghai station for four seasons from March 2018 to February 2019 and uses the MonoRTM atmospheric radiative transfer model to simulate the brightness temperature of domestic QFW-6000 microwave radiometer water vapor and oxygen channels at different heights in all seasons. The experimental results show that the brightness temperature weights of the water vapor and oxygen channel frequencies in the four seasons are slightly different. The individual differences in the brightness temperature weights of the eight-channel frequencies of water vapor are small, and they all contribute to the brightness temperature at the height of 0-10 km, which shows that the inversion of the humidity profile using the frequency of the water vapor channel needs to calculate the brightness temperature to the height of 10 km. The individual differences in the bright temperature weights of the eight-channel frequencies of oxygen are large, and the three-channel frequencies have little contribution to the brightness temperature at altitudes above 3 km, indicating that using the three oxygen channel frequencies to invert the atmospheric temperature profile needs to calculate the brightness temperature to the height of 3 km.
In order to expand the adaptability of the microwave radiometer (MWR) to the marine environment, the MWR tropospheric temperature and humidity profile system for the marine environment was developed. An idea of retrieving the 0~10km temperature and humidity profile by integrating the atmospheric experience database and the differential evolution algorithm (DE) was proposed. Through a large number of temperature and humidity profile simulation calculations, the feasibility of the algorithm was finally proved. The simulation results show that the root mean squared error (RMSE) of temperature profile retrieved by DE is 4.3K, the mean absolute error (MAE) is 3.4K, the RMSE of humidity retrieved by DE is 22.9%, and the MAE is 19.1%. The algorithm based on the fusion of atmospheric experience database and DE has the characteristics of independent of historical data, which provides a new idea for the marine atmospheric detection lacking of historical data.
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.
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