• Bayesian retrieval schemes;
  • cloud/rain ratio;
  • 37 and 85 GHz


Both the Goddard Profiling Algorithm (GPROF) and European Centre for Medium-Range Weather Forecasts (ECMWF) one-dimensional + four-dimensional variational analysis (1D+4D-Var) rainfall retrievals are inversion algorithms based on Bayes' theorem. Differences stem primarily from the a priori information. The GPROF uses an observationally generated a priori database whereas ECMWF 1D-Var uses the model forecast first guess (FG) fields. The relative similarity in the two approaches means that comparisons can shed light on the differences that are produced by the a priori information. Case studies have found that differences can be classified into four categories based upon the agreement in the brightness temperatures (Tbs) and in the microphysical properties of cloud water path (CWP) and rainwater path (RWP) space. A category of special interest is when both retrievals converge to similar Tb through minimization procedures but produce different CWP and RWP. The similarity in Tb can be attributed to comparable total water path (TWP) between the two retrievals while the disagreement in the microphysics is caused by their different degrees of constraint of the cloud/rain ratio by the observations. This situation occurs frequently and takes up 46.9% in the 1 month 1D-Var retrievals examined. The two retrievals produce similar spatial patterns but with different magnitude. The allocation of a large amount of CWP in the 1D-Var retrieval seems to be related to the stratiform portion of rain, which is produced by the large-scale condensation scheme. To attain better-constrained cloud/rain ratios and improved retrieval quality, this study suggests the implementation of higher microwave frequency channels in the 1D-Var algorithm. Copyright © 2012 Royal Meteorological Society