Microwave remote sensing techniques for vapor, liquid and ice parameters

The objective of this dissertation is to develop a comprehensive physical inverse model for microwave remote sensing of atmospheric components. This novel approach provides a rigorous basis for understanding and extracting the physical information content of radiometer and/or radar measurements of the atmospheric media. To this end, a comprehensive parametric radiative transfer model (forward model) is developed first and then used to examine the sensitivity of downwelling microwave radiation to realistic variations of environments and to evaluate the information content carried in the ground-based radiometer observations. A physical inversion approach is designed using Artificial Neural Network (ANN) inversion techniques. Both explicit and iterative inverse methods are used to study the non-uniqueness and non-convexness of the inversion on synthetic data. This physical inverse approach is applied to model NOAA's two- and three-channel ground-based radiometers (20.6, 31.65 and 90 GHz). The new physical inverse model is able to retrieve vertically integrated water vapor, cloud liquid water and ice water content simultaneously. Excellent model validations on water vapor and liquid water path retrievals were obtained based on radiosonde observations and NOAA's operational statistical models.; A combined iterative radar/radiometer method is further developed to vertically profile cloud microphysics. The combined model use retrievals from radiometer as initial guess and search iteratively for desired microphysical profiles which are consistent with radar measurements. Case studies found that this radar/radiometer technique agrees very well with aircraft in situ measurement of liquid drop size spectra and reasonably well with other published studies on empirical relationships between radar reflectivity and ice or liquid cloud parameters.; The predominant feature of a physical inverse model developed in this dissertation is that it can easily handle the non-linearity of radiative transfer and address the non-uniqueness of inversion. Besides, the physical model does not depend on in situ measurements and is thus site-independent. In principle, different remote sensor observations and climatological statistics can be incorporated into the inversion model.