In order to achieve better understanding of the hydrological cycle and the distribution of global precipitation, various microwave satellite platforms have been launched in the past to allow significant advance in precipitation measurement by directly measuring upwelling microwave radiances and reflectivity from space-borne radiometers and radar, respectively. Nevertheless, ambiguities in precipitation estimation from the only use of sets of microwave brightness temperature (T_B) measurements could lead to significant error. These ambiguities can be reduced with the addition of complementary data sets that until this point have not been employed in retrieval algorithms. In this paper, we investigate the potential improvements to estimating precipitation that are possible by combining observed T_B measurements with other available sources of information.

Passive microwave precipitation retrieval from satellite-borne microwave radiometers is often accomplished by the use of physically-based algorithms, which are based on Cloud Radiation Databases (CRD's). CRD's are composed of thousands of vertical microphysical profiles, which are produced by various Cloud Resolving Model (CRM) simulations, and their corresponding T_B's which are calculated by radiative transfer models using the microphysical profiles as input.

Unfortunately, the relationship between the simulated microphysical profiles and the simulated multi-spectral T_B's is not strictly unique. Therefore during precipitation retrieval, given a set of observed T_B's, one can often match a set of microphysical profiles with strongly differing precipitation outcomes. To improve precipitation estimation, additional constraints are needed.

Fortunately, such constraints are virtually always available in the form of recent or short term projections of the synoptic situation which dramatically reduces the number of applicable profiles in the data base, when the profiles include the synoptic situation in effect when the profiles were simulated. The Cloud Dynamics and Radiation Database (CDRD) approach is an attempt to include this additional information in the CRD to increase the available constraints in selecting applicable database entries used in the estimation procedure. This additional information includes the dynamical, theromodynamical, hydrological (DTH) structure of the atmosphere, which is stored as DTH tags in the CDRD. By using a Bayesian physical-statistical estimation method, it is expected that more appropriate microphysical profiles can be chosen and thus precipitation retrieval uncertainties can be reduced.

In this study, we estimate quantitatively the degree to which uncertainty in precipitation estimation can be reduced through the addition of these dynamic and thermodynamic constraints. This will be accomplished through a procedure whereby we statistically analyze a CDRD of approximately 100 CRM simulations to determine the impact which several of the strongest dynamic and thermodynamic constraints have on the variance in the simulated precipitation rate that is associated with the simulated multispectral T_B's.