A Novel Channel-Synthesizing Method for Reducing Uncertainties in Satellite Radiative Transfer Modeling

Most sounding channels sensitive to atmosphere layers close to Earth's surface are also sensitive to Earth's surface properties. Biases and uncertainties in Earth's surface emissivity and skin temperature may degrade the values of these observations being assimilated into weather prediction models. A method that combines several individual channels into a synthesized channel is proposed here to reduce such uncertainties. The effectiveness of such channel-synthesizing method is first demonstrated through perfect model experiments, where brightness temperatures are simulated and compared before and after noises added to surface emissivity and skin temperature. Real-case experiments that compare simulated brightness temperature and satellite observations further show that the synthesized channel can effectively reduce the mean bias of simulated brightness temperature from 1 to 3 K for individual GOES-R channels to near zero for the synthesized channel, suggesting great potential of the approach for more effective assimilation of surface-sensitive sounding channels.