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Gprof 2010v2 Algorithm Description

The Goddard profiling algorithm (Gprof) is the current operational rainfall algorithm for both TRMM TMI and AMSR-E. Gprof retrieves both the instantaneous rainfall and the rainfall vertical structure by using a Bayesian approach to match the observed brightness temperatures to hydrometeor profiles derived from cloud resolving models (CRMs). The gridded data files and associated plots provided on this website are from version 2010v2 of the Gprof algorithm, which is a major revision providing substantial improvements from the previous 2004 algorithm. Over ocean the 2010 algorithm is more physically-based retrieval, thus providing more consistent estimates between different radiometers. The land algorithm has also been updated to provide more consistent estimates between sensors.

Gprof 2004 relied on CRMs for its a-priori database. The biggest shortcoming of this approach was due to representativeness errors (Kummerow et al., 2006). Specifically there were always issues with correctly representing the frequently observed light, relatively small rain systems relative to well organized deep convective systems that are more interesting from a cloud resolving model perspective. Incorrect ratios of convective, stratiform, and shallow rainfall in the simulations often lead to errors in the retrieved fraction and rainfall rates associated with these rain types.

Gprof 2010 addresses this issue by constructing an a-priori database from observed TRMM radar and radiometer measurements. The method uses a very conservative approach that begins with the operational TRMM precipitation radar algorithm and adjusts its solution as little as possible while striving to simultaneously match the radiometer observations. Where the TRMM Precipitation Radar (PR) indicates no rain, an optimal estimation procedure using TRMM Microwave Imager (TMI) radiances is used to retrieve non-raining parameters. The optimal estimation methodology ensures that the geophysical parameters are fully consistent with the observed radiances. Within raining fields of view, cloud-resolving model outputs are matched to the liquid and frozen hydrometeor profiles retrieved by the TRMM PR. The Cloud resolving models act primarily to constrain parameters such as cloud water and ice that are not detected directly by the PR. The profiles constructed in this manner are used to compute brightness temperatures that are then compared directly to coincident observations. Adjustments are made to the rain water derived by PR in order to achieve agreement at 19 GHz, vertically polarized brightness temperatures at monthly time scales. The database is generated only in the central 11 pixels of the PR radar scan, and the rain adjustment is performed independently for distinct Sea Surface Temperature (SST) and Total Precipitable Water (TPW) values. Overall, the procedure increases PR rainfall by 4.2% but the adjustment is not uniform across all SST and TPW regimes. Rainfall differences range from a minimum of -57% for SST of 293K and TPW of 13 mm to a maximum of +53% for SST of 293K and TPW of 45mm. These biases are generally reproduced by a TMI retrieval algorithm that uses this adjusted database. It yields an overall increase of 5.0% with a minimum of -99% for SST of 293K and TPW of 14 mm to a maximum of +11.8% for an SST of 294K and TPW of 50 mm. Some differences are expected because the retrieval searches more than a single SST and TPW bin.

Instrument dependent details:

SSM/I
TMI
AMSR-E
SSMIS
AMSR2

References:

–   Kummerow, C. and L. Giglio, 1994: A passive microwave technique for estimating rainfall and vertical structure information from space, part I: algorithm descriptiion, J. Appl. Meteor., 33, 3-18.
–   Kummerow, C. and L. Giglio, 1994: A passive microwave technique for estimating rainfall and vertical structure information from space, part II: applications to SSM/I data, J. Appl. Meteor., 33, 19-34.
–   Kummerow, C., W. S. Olson, and L. Giglio, 1996: A simplified scheme for obtaining precipitation and vertical hydrometeor profiles from passive microwave sensors, IEEE Trans. Geosci. Remote Sens., 34, 1213-1232.
–   Huffman, G. J., and Coauthors, 1997: The Global Precipitation Climatology Project (GPCP) combined precipitation dataset, Bull. Amer. Meteor. Soc., 78, 5-20.
–   Kummerow, C., and Coauthors, 2001: The evolution of the Goddard Profiling Algorithm (GPROF) for rainfall estimation from passive microwave sensors, J. Appl. Meteor., 40, 1801-1820.
–   Wilheit, T., C. Kummerow and R. Ferraro, 2003: Rainfall algorithms for AMSR-E, IEEE Trans. Geoscience and Rem. Sensing, 41, 204-214.
–   Shin, D. B. and C. Kummerow, 2003: Parametric rainfall retrievals for passive microwave radiometers, J. Appl. Meteor., 42, 1480-1496.
–   Masunaga, H. and C. D. Kummerow, 2005: Combined radar and radiometer analysis of precipitation profiles for a parametric retrieval algorithm, J. Atmos. Oceanic Technol., 22, 909-929.
–   Kummerow, C., W. Berg, J. Thomas-Stahle, and H. Masunaga, 2006: Quantifying global uncertainties in a simple microwave rainfall algorithm, J. Atmos. Oceanic Tech., 23, 23-37.
–   Kummerow, C., 1993: On the accuracy of the Eddington approximation for radiative transfer in the microwave frequencies, J. Geophys. Res., 98, 2757-2765.
–   Kummerow, C., S. Finn, J. Crook, D. Randel, and W. Berg, 2009: An observatiionally based a-priori database for Bayesian rainfall retrievals, submitted to the J. Clim. and Appl. Meteor.