Sensitivity analysis of airborne microwave retrieval of stratiform precipitation to the melting layer parameterization

A sensitivity analysis for airborne microwave passive and active retrievals of hydrometeor profiles with respect to melting-layer parameterizations is carried out using synthetic data. The parameterizations of the melting layer include the effects of snow density, particle size distributions of hydrometeors as well as different permittivity models for mixed-phase particles. The hydrometeor profiles are obtained from a two-dimensional cloud ensemble model simulating a convective-stratiform rainfall event over the East Mediterranean sea. The statistical analysis reveals that the Maxwell-Garnett mixing formulas with water matrix and ice inclusions may be chosen for graupel, while a new permittivity model from Meneghini and Liao is suitable for snowflakes. A new Bayesian inversion framework is set up for both airborne microwave radiometric, radar, and combined radar-radiometer retrievals of hydrometeor profiles. Using the cloud profiles as control training data set, a numerical analysis was carried out by testing the inversion algorithms on each melting model data set. Results are discussed in terms of estimate sensitivity, defined as the statistical deviation bounds of the retrieved profiles from the control case ones. Relatively high values of estimate sensitivity to the melting-layer parameterizations are found for all hydrometeor species, especially for low snow-density and Maxwell-Garnett dielectric model test cases. The need of including various melting-layer characterizations within a comprehensive training data set and its implications for model-based Bayesian retrieval algorithms is finally argued and numerically tested.