The evolution of the Goddard profiling algorithm (GPROF) for rainfall estimation from passive microwave sensors

This paper describes the latest improvements applied to the Goddard profili ng algorithm (GPROF), particularly as they apply to the Tropical Rainfall M easuring Mission (TRMM). Most of these improvements, however, are conceptua l in nature and apply equally to other passive microwave sensors. The impro vements were motivated by a notable overestimation of precipitation in the intertropical convergence zone. This problem was traced back to the algorit hm's poor separation between convective and stratiform precipitation couple d with a poor separation between stratiform and transition regions in the a priori cloud model database. In addition to now using an improved convecti ve-stratiform classification scheme, the new algorithm also makes use of em ission and scattering indices instead of individual brightness temperatures . Brightness temperature indices have the advantage of being monotonic func tions of rainfall. This, in turn, has allowed the algorithm to better defin e the uncertainties needed by the scheme's Bayesian inversion approach. Las t, the algorithm over land has been modified primarily to better account fo r ambiguous classification where the scattering signature of precipitation could be confused with surface signals. All these changes have been impleme nted for both the TRMM Microwave Imager (TMI) and the Special Sensor Microw ave Imager (SSM/I). Results from both sensors are very similar at the storm scale and for global averages. Surface rainfall products from the algorith m's operational version have been compared with conventional rainfall data over both land and oceans. Over oceans, GPROF results compare well with ato ll gauge data. GPROF is biased negatively by 9% with a correlation of 0.86 for monthly 2.5 degrees averages over the atolls. If only grid boxes with t wo or more atolls are used, the correlation increases to 0.91 but GPROF bec omes positively biased by 6%. Comparisons with TRMM ground validation produ cts from Kwajalein reveal that GPROF is negatively biased by 32%, with a co rrelation of 0.95 when coincident images of the TMI and Kwajalein radar are used. The absolute magnitude of rainfall measured from the Kwajalein radar , however, remains uncertain, and GPROF overestimates the rainfall by appro ximately 18% when compared with estimates done by a different research grou p. Over land, GPROF shows a positive bias of 17% and a correlation of 0.80 over monthly 5 degrees grids when compared with the Global Precipitation Cl imatology Center (GPCC) gauge network. When compared with the precipitation radar (PR) over land, GPROF also retrieves higher rainfall amounts (20%). No vertical hydrometeor profile information is available over land. The cor relation with the TRMM precipitation radar is 0.92 over monthly 5 degrees g rids, but GPROF is positively biased by 24% relative to the radar over ocea ns. Differences between TMI- and PR- derived vertical hydrometeor profiles below 2 km are consistent with this bias but become more significant with a ltitude. Above 8 km, the sensors disagree significantly, but the informatio n content is low from both TMI and PR. The consistent bias between these tw o sensors without clear guidance from the ground-based data reinforces the need for better understanding of the physical assumptions going into these retrievals.