A Microwave Radiometer Residual Inversion Neural Network Based on a Deadband Conditioning Model

Microwave radiometers are passive remote sensing devices that are widely used in marine atmospheric observations. The accuracy of its inversion of temperature and humidity profiles is an important indicator of its performance. Back Propagation (BP) neural networks are widely used in the study of microwave radiometer inversion problems. However, the BP network which is carried by the radiometer inversion suffers from profile data collapse. To address this, this study introduced a residual network to improve the accuracy of water vapor vertical profiles. Aiming at the problem of large inversion temperature error due to the effect of turbulence on the light-travel phase induced by stationary fronts along the seashore in the subtropical monsoon climate region, we used historical data to establish the seasonal a priori mean profile and design a dead-zone residual adjustment model. The accuracy of the residual network and the deadband-adjusted residual network was verified using the meteorological records of the Taizhou region from 2013–2018, with the experimental data and BP hierarchical network as the comparison term. We found no data collapse in the temperature and humidity profile inversion results of the residual network. Relative to the initial BP hierarchical algorithm, where the error of water vapor in the range 6–10 km was reduced by 80%, the dead zone residual adjustment model in the inverse-temperature phenomenon reduced the sum of squares error by 21%, compared with the ordinary residual network inversion results. Our findings provide new insights into the accuracy improvement of radiometer remote sensing.