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Description
Version 08 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version.
The 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:
TMI (TRMM)
GMI, (GPM)
SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)
AMSR2 (GCOM-W1)
MHS (NOAA 18,19)
MHS (METOP A,B)
ATMS (NPP)
SAPHIR (MT1)
This provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.
The GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.
GPM Project generated these data at spatial sampling of 6 x 13 km (cross-track x along-track at forward bore sight).
Goddard Earth Sciences Data and Information Services Center (formerly Goddard DAAC), Terrestrial Information Systems Laboratory, Earth Sciences Division, Science and Exploration Directorate, Goddard Space Flight Center, NASA (NASA/GSFC/SED/ESD/TISL/GESDI
Citation is critically important for dataset documentation and discovery. This dataset is openly shared, without restriction, in accordance with the EOSDIS Data Use and Citation Guidance.
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Kummerow, C. (2026). GPM GMI (GPROF) Radiometer Precipitation Profiling L2A 1.5 hours 13 km V08 [Data set]. NASA Goddard Earth Sciences Data and Information Services Center. https://doi.org/10.5067/GPM/GMI/GPM/GPROF/2A/08 Date Accessed: 2026-05-02
Kummerow, Christian. “GPM GMI (GPROF) Radiometer Precipitation Profiling L2A 1.5 Hours 13 Km V08.” NASA Goddard Earth Sciences Data and Information Services Center, 2026. doi:10.5067/GPM/GMI/GPM/GPROF/2A/08. Date Accessed: 2026-05-02
Kummerow, Christian. GPM GMI (GPROF) Radiometer Precipitation Profiling L2A 1.5 Hours 13 Km V08. NASA Goddard Earth Sciences Data and Information Services Center, 2026, doi:10.5067/GPM/GMI/GPM/GPROF/2A/08. Date Accessed: 2026-05-02
Watters, Daniel C., Huffman, George J., Gatlin, Patrick N., Kirstetter, Pierre-Emmanuel, Bolvin, David T., Joyce, Robert, Nelkin, Eric J., Tan, Jackson, Wolff, David B.
da Silva, Helder Jose Farias, Goncalves, Weber Andrade, Bezerra, Bergson Guedes, Santos e Silva, Claudio Moises, Oliveira, Cristiano Prestrelo de, Mutti, Pedro Rodrigues
The table below lists the variables contained within a single granule for this
dataset. Variables often contain observed or derived geophysical measurements
collected from a variety of sources, including remote sensing instruments on
satellite and airborne platforms, field campaigns, in situ measurements, and
model outputs. The terms variable, parameter, scientific data set, layer, and band
have been used across NASA’s Earth science disciplines; however, variable is the
designated nomenclature in NASA’s Common Metadata Repository (CMR).
Variable metadata attributes such as Name, Description, Units, Data Type, Fill
Value, Valid Range, and Scale Factor allow users to efficiently process and analyze
the data. The full range of attributes may not be applicable to all variables.
Additional information on variable attributes is typically available in the data,
user guide, and/or other product documentation.
For questions on a specific variable, please use the Earthdata Forum.