Algorithm Information

The L3GPROF algorithm provides monthly and daily mean precipitation and related retrieved parameters from the Level 2 GPROF precipitation profiling algorithm for the GPM core and constellation satellites.

Each L3GPROF product contains global 0.25o x 0.25o gridded monthly/daily unconditional means and pixel counts. Monthly product filenames start with 3A-MO or 3A-CLIM-MO, and daily product filenames start with 3A-DAY or 3A-CLIM-DAY.

This Level 3 GPROF Version 07 release involves the following changes from the previous
(Version 05) release:

1. Product format change:
Added dimension variables such as lat, lon, layer, lat_bnds, lon_bnds, layer_bnds. Removed
cloudIce variable but added graupel and latentHeating variables.
2. Total number of precipitation pixels (npixPrecipitation):
In Version 05, pixels with pixelStatus=0 and surfacePrecipitation > 0 are counted as
precipitation pixels. In Version 07, pixels with pixelStatus=0, surfacePrecipitation > 0 and

GPROF V05 is the rainfall algorithm used in TRMM V8. It matches in all ways the algorithm from the retrieval for GPM GMI and other satellites in the constellation. The Goddard Profiling Algorithm is a Bayesian approach that nominally uses the GPM Combined algorithm to create it's a-priori databases. Given the importance of these databases to the final product, they are worth reviewing before discussing particular changes to the algorithm. GPROF V03 was implemented at the launch of the GPM mission and thus had no databases from the GPM satellite itself.

This document describes the data file formats for all GPM products. Metadata is described in Metadata for GPM Products. The purpose of this file specification document is to define the file content and format for the GPM data products. The file specifications have been reviewed by the algorithm developers. Formats are expected to change for each processing cycle.

Version 7.04 Test TKIO 3.97.18

This document contains a comprehensive list of all data products produced by the NASA Precipitation Processing System, including their Document Object Identifiers (DOIs)

This document describes the file naming conventions that will be used to name data products produced by the Precipitation Processing System (PPS) for the Global Precipitation Measurement (GPM) Mission. These file naming conventions are also intended to apply to files produced or reprocessed from the Tropical Rainfall Measuring Mission (TRMM) satellite during the period of GPM operations.

The purpose of this file specification document is to define the file content and format for the TRMM data products.

The algorithm for the Integrated Multi-satellitE Retrievals for GPM (IMERG) has now been upgraded to Version 06. The transition to V05 for the IMERG Final Run began 13 March 2019 at PPS and the new data started flowing down to the GES DISC as well. However, on 15 March 2019 an error was discovered in processing the initial batches of V06 IMERG Final Run months. A design choice in the code ended up retaining microwave precipitation estimates in the latitude band 60°N-S when there is snow/ice on the surface, rather than masking out the estimates due to low performance in such cases.

Users have requested a “simple” quality index (QI) to give some guidance on when they should most trust the Integrated Multi-satellitE Retrievals for GPM (IMERG). While the goal is reasonable, there is no agreement about how this quantity should be defined. After some discussion within the team, two distinctly different quality indices were chosen for the half-hourly and monthly data fields (QIh and QIm, respectively) for implementation in Version 05 and continued in V06.

NASA produces a GIS translation of IMERG for various accumulation periods that maintains the 0.1-degree resolution and global coverage of the original HDF5 data product. In April 2019, Version 6 of IMERG was released in both the HDF5 and the GIS formats. Version 6 is the first version to cover the approximately 20-year period from June 2000 to the present. This long-duration archive will be a boon to scientific research and will provide an expanded training set for realtime applications such as disaster monitoring.