Improving the instantaneous vertical profiling of precipitation for passive-microwave retrievals

Current passive-microwave retrieval approaches that are based on the a-priori knowledge provided by a representative database of columns of atmospheric variables and their corresponding microwave signatures, depend crucially on the realism of the representation of the cloud and precipitation in the column. Most approaches rely on columns produced by cloud-permitting model simulations, which suffer from oversimplifications of the very variables to which the microwave brightness temperatures are most sensitive, namely the type, concentration and relative sizes of the hydrometeors. Others rely on retrieved descriptions obtained from imperfect remotely-sensed measurements. The TRMM radar being a single-channel instrument cannot, by itself identify the phase of the condensation. The GPM radar that offers an additional Ka-band frequency should be slightly better. This presentation will summarize our approach of using higher-sensitivity ground-based measurements to address these shortcomings. During the CINDY-DYNAMO experiment (2011-2012), the dual-wavelength polarimetric radar SPolKa was deployed, providing a unique set of observations. This allows insight into rainfall characteristics for different species. Using a Bayesian approach with Mie-scattering calculations, the rainfall rate, cloud liquid water, mean diameter and other parameters can be retrieved from SPolKa data. Different assumptions (on hydrometeor habit and size distributions) for those simulations were used, and their impact on the retrievals evaluated. This preliminary study will eventually enable the elaboration of a passive-microwave retrievals' database, accounting for the quantitative vertical distribution of precipitation.