Microwave radiometrie technique to retrieve vapor, liquid and ice, part i - development of a Neural Network-Based Inversion Method

With the advent of the microwave radiometer, passive remote sensing of clouds and precipitation has become an indispensable tool in a variety of meteorological and oceanographical applications. There is wide interest in the quantitative retrieval of water vapor, cloud liquid, and ice using brightness temperature observations in scientific studies such as earth's radiation budget and microphysical processes of winter and summer clouds. Emission and scattering characteristics of hydrometeors depend on the frequency of observation. Thus, a multifrequency radiometer has the capability of profiling cloud microphysics. Sensitivities of vapor, liquid, and ice with respect to 20. 6, 31. 65 and 90 GHz brightness temperatures are studied. For the model studies, the atmosphere is characterized by vapor density and temperature profiles and layers of liquid and ice components. A parameterized radiative transfer model is used to quantify radiation emanating from the atmosphere. It is shown that downwelling scattering of radiation by an ice layer results in enhancement at 90 GHz brightness temperature. Once absorptive components such as vapor and liquid are estimated accurately, then it is shown that the ice water path can be retrieved using ground-based three-channel radiometer observations. In this paper we developed two- and three-channel neural network-based inversion models. Success of a neural network-based approach is demonstrated using a simulated time series of vapor, liquid, and ice. Performance of the standard explicit inversion model is compared with an iterative inversion model. In Part II of this paper, actual radiometer, and radar field measurements are utilized to show practical applicability of the inverse models. © 1997 IEEE.