A Simplified Scheme for Obtaining Precipitation and Vertical Hydrometeor Profiles from Passive Microwave Sensors

Christian Kummerow, William S. Olson, and Louis Giglio


This paper presents a computationally simple technique for retrieving the precipitation and vertical hydrometeor profiles from downward viewing radiometers. The technique is computationally much less expensive than previous profiling schemes and has been designed specifically to allow for tractability of assumptions. In this paper, the emphasis is placed upon passive microwave applications, but the combination of passive with active microwave sensors, infrared sensors, or other a priori information can be adapted easily to the framework described here. The technique is based upon a Bayesian approach. Here, we use many realizations of the Goddard Cumulus Ensemble model to establish a prior probpability density function of rainfall profiles. Detailed three-dimensional radiative transfer calculations are used to determine the upwelling brightness temperatures from the cloud model to establish the similarity of radiative signatures and thus the probability that a given profile is actually observed. In this study, we show that good results may be obtained by weighting proffies from the prior probability density function according to their deviation from the observed brightness temperatures. Examples of the retrieval results are shown for oceanic as well as land situations. Microwave data from the Advanced Microwave Precipitation Radiometer (AMPR) instrument are used to illustrate the retrieval structure results for high-resolution data while SSM/I is used to illustrate satellite applications. Simulations are performed to compare the expected retrieval performance of the SSM/I instrument with that of the upcoming TMI instrument aboard the Tropical Rainfall Measuring Mission (TRMM) to be launched in August 1997. These simulations show that correlations of ~0.77 may be obtained for 10-km retrievals of the integrated liquid water content based upon SSM/I channels. This correlation increases to ~0.90 for the same retrievals using the TMI channels and resolution. Due to the lack of quantitative validation data, hydrometeor profiles cannot be compared directly but are instead converted to an equivalent refiectivity structure and compared to existing radar observations where possible.