The Evolution of the Goddard Profiling Algorithm (GPROF) for Rainfall Estimation from Passive Microwave Sensors

By Christian D. Kummerow, Yang Hong, W. Olson, S. Yang, Robert F. Adler, J. McCollum, Ralph R. Ferraro, Grant W. Petty, D.-B. Shin, T. Wilheit. 2001

883 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/fe7021bdb4ef0d563ff3e1e663f512aca71b9bbb

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Citations in the corpus

Dreary state of precipitation in global models (2010, background): “…Measurement Mission (TRMM) Precipitation Radar (PR) product 2A25 [Iguchi et al., 2000] and the Version 6 TRMM Microwave Imager (TMI) product 2A12 [Kummerow et al., 1998, 2001], and (3) Global Precipitation Climatology Project (GPCP) Version 2.1 precipitation data that combines satellite IR data…”
Global Precipitation Map Using Satellite-Borne Microwave Radiometers by the GSMaP Project: Production and Validation (2006, methodology): “Using the 1998–2005 Rainpr as a reference, the GSMaP_TMI are compared to surface rainfall rates of 2A12 by the GPROF Fig.”
Evaluation of cloud‐resolving and limited area model intercomparison simulations using TWP‐ICE observations: 1. Deep convective updraft properties (2014, methodology): “Despite uncertainties in these parameterizations, cloud-resolving models (CRMs) are increasingly used in satellite algorithms [e.g., Kummerow et al., 2001; Kingsmill et al., 2004; Shige et al., 2009] to derive quantities such as rainfall and latent heating.”
Operational Implementation of the 1D+3D-Var Assimilation Method of Radar Reflectivity Data in the AROME Model (2014, methodology): “This 1D approach has been largely inspired from the methodology used to retrieve precipitation rates and latent heating profiles from satellite observations and from a database of cloud ensemble simulations (Olson et al. 1999; Kummerow et al. 2001).”
Afternoon rain more likely over drier soils (2012, neutral): “In the case of the AMSR-E data, we first excluded cases where rainfall was present at overpass time from the Goddard Profiling Algorithm [GPROF; Kummerow et al. , 2001].”
Status and Outlook of Operational Satellite Precipitation Algorithms for Extreme-Precipitation Events (2003, methodology): “Also noteworthy are rain-rate estimates produced routinely from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI; Kummerow et al. 1998, 2001).”
Kalman Filter–Based CMORPH (2011, background): “…precipitation estimates Previously the only source of the information used by CMORPH to define global high-resolution precipitation analyses was the level 2 instantaneous rain-rate products retrieved from PMW observations of LEO satellites (Ferraro 1997; Ferraro et al. 2000; Kummerow et al. 2001).”
Intercalibrated Passive Microwave Rain Products from the Unified Microwave Ocean Retrieval Algorithm (UMORA) (2008, methodology): “2003) and both the swath level (2A12) and gridded (3A12) version 6 Goddard Profiling Algorithm (GPROF) surface rain rates (Kummerow et al. 2001). The comparison of our TMI swath data with the GPROF swath data is unique because it is a pixel-topixel matchup between retrievals from the same sensor on ”
Satellite cloud and precipitation assimilation at operational NWP centres (2011, methodology): “…the above-listed drawbacks, the choice was made to use an alternative method to the 1D-Var, based on the Goddard profiling algorithm (GPROF) used to retrieve precipitation rates (Kummerow et al., 2001) from satellite observations, together with a database of Goddard cumulus ensemble simulations.”
Evaluation of Satellite Rainfall Estimates for Drought and Flood Monitoring in Mozambique (2015, neutral): “, [8]) from the Special Sensor Microwave Imager (SSM/I), Tropical Rainfall Measuring Mission (TRMM), or the Advanced Microwave Sounding Unit (AMSU).”