Multi-Channel Regression Inversion Method for Passive Remote Sensing of Ice Water Path in the Terahertz Band

Abstract

Retrieval of ice cloud properties using passive terahertz wave radiometer from space has gained increasing attention currently. A multi-channel regression inversion method for passive remote sensing of ice water path (IWP) in the terahertz band is presented. The characteristics of the upward terahertz radiation in the clear-sky and cloudy-sky are first analyzed using the Atmospheric Radiative Transfer Simulator (ARTS). Nine representative center frequencies with different offsets are selected to study the changes of terahertz radiation caused by microphysical parameters of ice clouds. Then, multiple linear regression method is applied to the inversion of IWP. Combinations of different channels are selected for regression to eliminate the influence of other factors (i.e., particle size and cloud height). The optimal fitting equation are obtained by the stepwise regression method using two oxygen absorption channels (118.75 ± 1.1 GHz, 118.75 ± 3.0 GHz), two water vapor absorption channels (183.31 ± 1.0 GHz, 183.31 ± 7.0 GHz), and two window channels (243.20 ± 2.5 GHz, 874.4 ± 6.0 GHz). Finally, the errors of the proposed inversion method are evaluated. The simulation results show that the absolute errors of this method for the low IWP cases are below 7 g/m2, and the relative errors for the high IWP cases are generally ranging from 10 to 30%, indicating that the multi-channel regression inversion method can achieve satisfactory accuracy.