Experiments on Microwave Radiometer Temperature Profile Correction by Integrating Multi-Source Observational Data
In the present study,a correction method was developed to improve the accuracy of ground-based microwave radiometers in measuring temperatures.Using temperature products from the FY-4A meteorological satellite,a back propagation(BP)neural network,and a genetic algorithm,we conducted temperature correction simulation experiments to correct the temperature profiles measured by two MP-3000 ground-based microwave radiometers located at the meteorological stations in Hangzhou and Nanjing,respectively,and obtained accurate and continuous vertical profiles of atmospheric temperature.The corrected temperature profiles were then compared with temperature data from radiosonde measurements and the Aircraft Meteorological Data Relay(AMDAR)data from the Civil Aviation Administration of China.The results show that:(1)Microwave radiometer temperature products exhibited inherent inaccuracies,with larger discrepancies during rainy conditions and the greatest average deviation observed at the altitude of 2 km for both stations.(2)The temperature measured by the microwave radiometer,after being corrected through BP neural network simulation,was a significant enhancement compared to the original temperature.At Hangzhou station,the reductions in mean absolute error,mean squared error,and root mean square error(RMSE)were observed in the ranges of 45%～55%,65%～78%,and 41%～53%,respectively,while at Nanjing station,these metrics decreased by 58%～66%,83%～88%,and 55%～59%respectively.(3)The simulation model of the neural network,after its initial weights and thresholds were optimized using a genetic algorithm,demonstrated further improvements.There was a significant enhancement in the rain model,with RMSE reductions of 11%-15%.The proposed correction method for microwave radiometers seems to be suitable for broader applications across microwave radiometer stations.
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