Overview

This document provides an overview of the precipitation data from the Integrated Multi-Satellite Retrievals for GPM (IMERG) version 5.2. The dataset is a combination of many satellite microwave precipitation estimates, microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and some other precipitation estimators. The precipitation product is the result of several optimization iterations. The GPM is an international (NASA and Japan Aerospace and Exploration Agency (JAXA)) mission. Updates to the morphing algorithm have been introduced to version 6 (V6) of IMERG.

Provider’s contact information

The work on IMERG is being carried out as part of the Global Precipitation Measurement (GPM) mission, an international project of NASA and JAXA.

Dataset Point of Contact:

https://gpm.nasa.gov/contact

Licensing and citation

Cite data as:

Huffman, G.J., E.F. Stocker, D.T. Bolvin, E.J. Nelkin, Jackson Tan, 2021, last updated 2021: Data Sets. NASA/GSFC, Greenbelt, MD, USA, , accessed , [doi: or at <data landing page URL>].

Variable name and units

IMERG output is available in two near-real time formats:

• “Early” multi-satellite product is processed about 4 hours after observation time
• “Late” multi-satellite product is processed about 14 hours after observation time

After the monthly gauge analysis is received

• “Final” satellite-gauge product is processed about 3.5 months after observation month

We cover only the final satellite-gauge product variables here. For more information, see the IMERG Technical Document.

Spatial coverage and resolution

The spatial resolution is 0.1°x0.1° and has global coverage (though not as much data goes into the estimate for different locations)

Temporal coverage and resolution

The temporal resolution of the IMERG product is 30 minutes. The V5.2 record spans from 1998 to present. The V6 record will span from 2000/06/01 to present and supersedes the V5.2 record.

Information about observations (number, homogeneity)

Most satellite-based precipitation estimates are provided by low-Earth-orbit passive microwave sensors. These provide somewhat limited sampling, so a constellation of similar satellites is combined. Additional information can come from geosynchronous-Earth-orbit infrared (IR) estimates, though these are poor over ice and snow. Gauge analyses are used to provide regionalization and bias correction to satellite estimates.

Satellite overview:

• Core satellites (merged radar-passive microwave imager):
  • TRMM PR-TMI [2014/04-est. 2024/02]
  • GPM DPR-GMI [1998/01-2014/09]
• Microwave constellation:
  • Conically-scanning passive microwave imagers and imager/sounders
    • Aqua AMSR-E [2002/06-2011/10]
    • DMSP F13 SSMI [1998/01-2009/11]
    • DMSP F14 SSMI [1998/01-2008/08]
    • DMSP F15 SSMI [2000/02-2006/08]
    • DMSP F17 SSMIS [2008/03-est. 2019/12]
    • DMSP F17 SSMIS [2010/03-est. 2020/03]
    • DMSP F19 SSMIS [2014/12-2016/02]
    • GCOMW1 AMSR2 [2012/07-est. 2022/05]
    • GOSAT-3 AMSR3 [est. 2022/02-est. 2032/01]
    • GPM GMI [2014/03-est. 2024/02]
    • METOP-SG B1 MWI
    • METOP-SG B2 MWI
    • METOP-SG B3 MWI
    • TRMM TMI
    • WSF-M 1 MIS
    • WSF-M 2 MIS
  • Cross-track-scanning passive microwave sounders
    • JPSS-2 ATMS
    • JPSS-3 ATMS
    • METOP-2/A MHS
    • METOP-1/B MHS
    • METOP-3/C MHS
    • METOP-SG A1 MWS
    • METOP-SG A2 MWS
    • METOP-SG A3 MWS
    • M-T SAPHIR
    • NOAA-15 AMSU
    • NOAA-16 AMSU
    • NOAA-17 AMSU
    • NOAA-18 MHS
    • NOAA-19 MHS
    • NOAA-20 ATMS
    • SNPP ATMS
  • IR/passive microwave sounders
    • Aqua AIRS [2002/02-est.2020/09]
    • NOAA-14 TOVS [1998/01-2005/04]
    • NOAA-20 CrIS [2017/11-est.2022/06]
    • SNPP CrIS [2011/11-est.2021/11]
• Geosynchronous infrared images
  • GMS, MTSat, Himawari Series [Sub-sat. Lon. 140E]
  • GOES-E Series [Sub-sat. Lon. 75W]
  • GOES-W Series [Sub-sat. Lon. 135W]
  • Meteosat prime series [Sub-sat. Lon. 0E]
  • Meteosat repositioned series [Sub-sat. Lon. 63E from Jul 1998, 41E from Oct 2016]

Precipitation gauge analysis

• Full version 2018 from DWD/GPCC [1998/01-2016/12]
• Monitoring Version 6 from DWD/GPCC [2017/01-ongoing]

Methodology

First estimates come from the passive microwave sensors, mainly from brightness temperatures. Estimates are gridded, and then some calibration is done (using the CORRA product and the GPCP V2.3 monthly precipitation estimate). This data is combined into half-hourly fields. Quasi-Lagrangian interpolation is applied to the gridded estimates using motion vectors computed from ancillary data. CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR fields, and these supplements the morphed precipitation. Note that PMW retrievals traditionally suffer from inaccuracies over frozen surfaces, so some missing data may exist at high latitudes where IR-based estimates have gaps.

The “Final” IMERG precipitation estimate results after calibration with gauge data. The ratio between the monthly accumulation of half-hourly multi-satellite-only fields and the monthly satellite-gauge field is found. Then, this ratio is applied to each half-hourly field of multi-satellite only precipitation estimates.

More details for the newest version can be found in the Algorithm Theoretical Basis Document for V6 of IMERG.

Information about the technical and scientific quality

About 90% of ECCC automatic rain gauge stations do not participate in GPCC and are thus independent validation sources for the IMERG data, which uses GPCC for calibration.

This is not covered here in detail, but there are actually four products provided through IMERG: precipitationCal, precipitationUncal, IRprecipitation, and HQprecipitation. The uncalibrated (precipitationUncal) and calibrated (precipitationCal) estimates only differ in the Final run, where gauge calibration is done as described in the methodology section for precipitationCal. Only the infrared geostationary satellite precipitation data is included in IRprecipitation, and the precipitation extracted from merging high-quality passive microwave sensors only includes microwave data but has significant gaps (HQprecipitation).

PrecipitationCal is considered as the most reliable IMERG precipitation estimate and should generally be used.

IMERG is not intended as a Climate Data Record dataset, so be wary of discontinuities/changes where sensors change, etc.

Limitations and strengths for application in North Canada

Due to small number of reporting sites in North Canada, validation specific to that region is limited.

It is suggested by studies that IMERG tends to overestimate light to moderate precipitations, particularly in the summer.

References to documents describing the methodology and/or the dataset

Algorithm Theoretical Basis Document for IMERG V5.2 (https://gpm.nasa.gov/resources/documents/gpm-integrated-multi-satellite-retrievals-gpm-imerg-algorithm-theoretical-basis-)

Algorithm Theoretical Basis Document for IMERG v6 – may only be temporarily hosted here (https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERG_ATBD_V06.pdf)

IMERG V06 Changes to Morphing Algorithm:

Tan, J., G. J. Huffman, D. T. Bolvin, and E. J. Nelkin, 2019: IMERG V06: Changes to the Morphing Algorithm. Journal of Atmospheric and Oceanic Technology 36, 12, 2471-2482, https://doi.org/10.1175/JTECH-D-19-0114.1

Link to download the data and format of data

Direct Download from GPM data access page (http://pmm.nasa.gov/data-access/downloads/gpm) – requires a simple, free, automatic on-line registration

Helpful how-to on reading IMERG data using Python (https://disc.gsfc.nasa.gov/information/howto?title=How%20to%20Read%20IMERG%20Data%20Using%20Python)

Data are in a variety of formats: Visualization, GeoTIFF, HDF5, NetCDF, OPeNDAP.

Publications including dataset evaluation or comparison with other data in Canada

Moazami, S., and M. R. Najafi, 2021: A comprehensive evaluation of GPM-IMERG V06 and MRMS with hourly ground-based precipitation observations across Canada. Journal of Hydrology, 594, 125929. https://doi.org/10.1016/j.jhydrol.2020.125929