1 research outputs found
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 teste