GPM (Global Precipitation Measurement) Products. Includes information on these two programs that integrate GPM data: Multi-Radar/Multi-Sensor (MRMS)  and  Integrated Multi-satellitE Retrievals for GPM (IMERG)

Huffman, George J.

NASA Technical Reports Server

GPM Products
George J. Huffman
NASA/GSFC Earth Sciences Division – Atmospheres
george.j.huffman@nasa.gov
•  Introduction
•  IMERG
•  Early Results
•  Schedule and 
Final Remarks
https://ntrs.nasa.gov/search.jsp?R=20190028284 2019-09-26T20:10:11+00:00Z
We want 3-hourly observations, globally
• sampling the diurnal cycle
• morphed microwave loses skill outside ±90 
min
The current GPM constellation includes:
• 5 polar-orbit passive microwave imagers
•   3 SSMIS, AMSR-2, GMI
• 6 polar-orbit passive microwave sounders
•   3 MHS, 2 ATMS, SAPHIR*
The constellation is evolving
• drifting legacy satellites are key to providing as 
much data as possible
• launch manifests are assured for sounders, 
sparse for imagers
1. Introduction – The Constellation
“Observatory” instruments
• Radiometer – TMI and GMI, led by NASA
• Radar – PR and DPR, led by JAXA
“Partner constellation” PMW instruments
• CNES/ISRO: MeghaTropiques SAPHIR
• EUMETSAT: MetOp-A/B/C MHS
• JAXA: Aqua (NASA) AMSR-E; GCOM-W AMSR2
• NOAA: NOAA-15/16/17 AMSU; NOAA-18/19 MHS; SNPP, NOAA-20 ATMS
• U.S. DoD: DMSP F13/14/15 SSMI; DMSP F16/17/18/19 SSMIS
1. Introduction – The Input Data Comes From
Data are provided at different processing levels, defined as:
• Level 1A: Reconstructed, unprocessed instrument data at full resolution, time referenced, and annotated 
with ancillary information, including radiometric and geometric calibration coefficients and georeferencing 
parameters (i.e., platform ephemeris), computed and appended, but not applied, to Level 0 data.
• Level 1B: Radiometrically corrected and geolocated Level 1A data processed to sensor units.
• Level 1C: Common intercalibrated brightness temperature (Tc) products using GMI Level 1B as the 
reference standard.
• Level 2: Derived geophysical parameters at the same resolution/location as those of the Level 1 data.
• Level 3: Geophysical parameters spatially and/or temporally resampled from Level 1 or Level 2 data.
The public postings at Goddard cover:
• Level 1A: TMI, GMI
• Level 1B: TMI, GMI
• Level 1C: TMI, GMI, partner PMW instruments
• Level 2: GPROF-GMI, GPROF-partner, PRPS-SAPHIR, Ku, Ka (GPM only), DPR (GPM only), TMI/GMI-Ku 
Combined
• Level 3: GPROF-GMI, GPROF-partner, PRPS-SAPHIR, PR/DPR, TMI/GMI-Ku Combined, IMERG
• Related datasets: TRMM LIS, land/sea mask, …
1. Introduction – GPM Product Levels and Postings
project-specific terminology, 
not an official Level definition
Integrated Multi-satellitE Retrievals for GPM is a unified 
U.S. algorithm based on
• Kalman Filter CMORPH – NOAA/CPC
• PERSIANN CCS – U.C. Irvine
• TMPA – GSFC
• PPS (GSFC) processing environment
IMERG is a single integrated code system for near-real 
and post-real time
• multiple runs for different user requirements for latency 
and accuracy
• “Early” – 4 hr (flash flooding)
• “Late” – 14 hr (crop forecasting)
• “Final” – 3 months (research)
• time intervals are half-hourly and monthly (Final only)
• 0.1º global CED grid
• morphed precip 90º N-S except polar snow/ice area
• IR covers 60º N-S
2. IMERG – Quick Description (1/2)
Half-hourly data file (Early, Late, Final)
1 [multi-sat.] precipitationCal
2 [multi-sat.] precipitationUncal
3 [multi-sat. precip] randomError
4 [PMW] HQprecipitation
5 [PMW] HQprecipSource [identifier]
6 [PMW] HQobservationTime
7 IRprecipitation
8 IRkalmanFilterWeight
9 [phase] probabilityLiquidPrecipitation
10 precipitationQualityIndex
Monthly data file (Final)
1 [sat.-gauge] precipitation
2 [sat.-gauge precip] randomError
3 GaugeRelativeWeighting
4 probabilityLiquidPrecipitation [phase]
5 precipitationQualityIndex
IMERG is adjusted to GPCP V2.3 seasonal climatology
zonally to achieve a bias profile that we consider 
reasonable
• GPM Version 04, 05, 06 core products have similar 
zonal profiles (by design)
• these profiles are systematically low in the 
extratropical oceans compared to
• GPCP V2.3 monthly Satellite-Gauge product
• Behrangi Multi-satellite CloudSat, TRMM, 
Aqua (MCTA) product
• over land GPCP adjustment provides a first cut at 
the adjustment to gauges that the final calibration in 
IMERG enforces
• similar bias concerns apply during TRMM
2. IMERG – Quick Description (2/2)
Half-hourly data file (Early, Late, Final)
1 [multi-sat.] precipitationCal
2 [multi-sat.] precipitationUncal
3 [multi-sat. precip] randomError
4 [PMW] HQprecipitation
5 [PMW] HQprecipSource [identifier]
6 [PMW] HQobservationTime
7 IRprecipitation
8 IRkalmanFilterWeight
9 [phase] probabilityLiquidPrecipitation
10 precipitationQualityIndex
Monthly data file (Final)
1 [sat.-gauge] precipitation
2 [sat.-gauge precip] randomError
3 GaugeRelativeWeighting
4 probabilityLiquidPrecipitation [phase]
5 precipitationQualityIndex
PMW
sensor
2. IMERG – Examples of Data Fields
PMW
time into 
half hour
PMW
precip
IR precip
IR weight
cal precip
(uncal precip)
probability of 
liquid phase
Quality 
Index
2 July 2015 
0030 UTC
Morphing vector source switched to MERRA-2/GEOS FP
Morphed precip extended from 60º N-S (V05 and earlier) to 90º N-S, but 
• masked out for icy/snowy surfaces
Half-hourly Quality Index modified
• t=0 values estimated (set to 1 in V05)
• shifted to 0.1º grid (0.25º in V05)
Full intercalibration to Combined product
• V05 took shortcuts
Modifications for TRMM era
• compute calibrations for older satellites against TRMM
• compute TRMM-era microwave calibrations in the band 33ºN-S and 
• blend with adjusted monthly climatological GPM-era microwave calibrations over 25º-90º N and S
Revisions to internals raises the maximum precip rate from 50 to 200 mm/hr and no longer discrete
• files bigger due to less compressibility
• allows really tiny numbers
2. IMERG – V06 Upgrades
Calibration sequence is
• CORRA calibrated to GPCP over ocean 
outside 30ºN-S
• GPM constellation calibrated to CORRA
Adjustments working roughly as intended
• CORRA is low at higher latitudes
• adjustments in Southern Ocean are 
large and need analysis
• IMERG subsetted to coincidence 
with CORRA is much closer to 
CORRA
3. Early Results – Calibration 
D. Bolvin (SSAI; GSFC)
Latitude
CORRA
CORRA
Average September-November 
for 2001 to 2018
• data re-sorted to give the 
same LST over the globe
• surface cycles between Blue 
Marble and Night Lights
Reminiscent of TMPA, but
• more detailed, broader 
spatial coverage
• no interpolations between 
the 3-hourly times
• less IR-based precip used 
(which tends to have a 
phase lag)
3. Early Results – SON Diurnal Cycle, Maritime Continent 
Reminiscent of IMERG V05, but 
• less “flashing” due to inter-satellite differences and morphing
• better data coverage at higher latitudes
• and still have artifacts along ice edges
J. Tan (USRA; GSFC)
Average June-July-August for 2014 to 2018 (5 
summers) for 6 states
Compared to Multi-Radar Multi-Sensor 
(MRMS), IMERG Final shows:
• lower averages
• lower amplitude cycle in Colorado
• higher amplitude cycle in Iowa
• very similar curve shapes, peak times
This version of MRMS only starts in 2014, so 
an extended comparison would have to use 
different data 
3. Early Results – JJA Diurnal Cycle in Central U.S. (GPM Era) 
J. Tan (USRA; GSFC)
IMERG-Final – solid
MRMS – dashed
V06 Final Run starts June 2000
V06 is higher than 3B43 (TMPA) and GPCP 
over ocean
TRMM-era IMERG has a strong semi-annual 
signal
• GPM-era IMERG and 3B43 dominated by 
the annual cycle
Interannual variation
• has similar peaks/troughs for all datasets
• GPCP (passive microwave calibration) 
lags phase of 3B43 (through 2013), 
IMERG (both PMW/radar calibration)
• after September 2014, 3B43 (PMW 
calibration) matches GPCP phase
Additional multi-year variations
• IMERG (and 3B43) are High Resolution 
Precipitation Products, not CDRs
3. Early Results – Ocean (50ºN-S) Timeseries
E. Nelkin (SSAI; GSFC)
Early March 2019: began Version 06 IMERG Retrospective Processing
• the GPM era was launched first, Final Run first, done
• the TRMM era Final Run retrospective processing is done
• 4 km merged global IR data files continue to be delayed for January 1998-January 2000
• the run builds up the requisite 3 months of calibration data starting from February 2000
• the first month of data is for June 2000
• the initial 29 months of data will be incorporated when feasible
• Early and Late Run Initial Processing started ~1 May
• a damaged land/ocean map forced a shift to V06B ~22 May, including a restart on Final retrospective 
processing
• Early and Late Run Retrospective Processing uses Final intermediate files, so they come after Final
• The GPM era is essentially done; the TRMM era is forecast to finish in mid-to-late August
• Final is always ~3.5 months behind, so the Early and Late retrospective processing have to wait on 
Initial Processing for the Final Run to fill in April 2019
4. Schedule and Final Remarks (1/3)
done
done
completing coming
Development Work for V07
• multi-satellite issues
• improve error estimation
• develop additional data sets based on observation-model combinations
• work toward a cloud development component in the morphing system
• general precipitation algorithmic issues
• introduce alternative/additional satellites at high latitudes (TOVS, AIRS, AVHRR, etc.)
• evaluate ancillary data sources and algorithm for Prob. of Liq. Precip. Phase
• work toward using PMW retrievals over snow/ice
• work toward improved wind-loss correction to gauge data
Version 07 release should be in “about 2 years” (2022?)
4. Schedule and Final Remarks (2/3)
IMERG is being 
upgraded to V06 now
• the product structure 
remains the same
• Early, Late, Final
• 0.1ºx0.1º half-
hourly (and 
monthly in Final)
• new source for 
morphing vectors
• higher-latitude 
coverage
• extension back to 
2000 (and eventually 
1998)
• improved Quality 
Index
4. Schedule and Final Remarks (3/3)
J. Tan (USRA; GSFC)
See https://svs.gsfc.nasa.gov/cgi-bin/details.cgi?aid=4285
Half-hourly QI (revised)
• approx. Kalman Filter correlation
• based on 
• times to 2 nearest PMWs (only 1 for 
Early) for morphed data
• IR at/near time (when used)
• where r is correlation, and  the i’s are for 
forward propagation, backward 
propagation, and IR
• or, an approximate correlation when a 
PMW is used for that half hour
• revised to 0.1º grid (0.25º in V05)
• thin strips due to inter-swath gaps
• blocks due to regional variations
• snow/ice masking will drop out microwave 
values
2. IMERG – Quality Index (1/2)
D.Bolvin (SSAI; GSFC)
The goal is a simple “stoplight” index
• ranges of QI will be assigned
• good 0.6-1
• use with caution 0.4-0.6
• questionable 0-0.4
• is this a useful parameter?
Monthly QI (unchanged)
• Equivalent Gauge (Huffman et al. 1997) in gauges / 2.5ºx2.5º
• where r is precip rate, e is random error, and H and S are source-specific error constants
• invert random error equation
• largely tames the non-linearity in random error due to rain amount
• some residual issues at high values
• doesn’t account for bias
• the  stoplight ranges are
• good > 4
• use with caution 2-4
• questionable < 2
• note that this ranking points out uncertainty
in the values in light-precip areas that
nearly or totally lack gauges (some
deserts, oceanic subtropical highs)
2. IMERG – Quality Index (2/2)
Month Qual. Index  Dec 2016 0 4 8 12  16 20+
D.Bolvin (SSAI; GSFC)
Following the CMORPH approach
• for a given time offset from a microwave overpass
• compute the (smoothed) average correlation 
between
• morphed microwave overpasses and microwave 
overpasses at that time offset, and
• IR precip estimates and microwave overpasses 
at that time offset and IR at 1 and 2 half hours 
after that time offset
• for conical-scan (imager) and cross-track-scan 
(sounder) instruments separately
• the microwave correlations drop off from t=0, 
dropping below the IR correlation within a few hours 
(2 hours in the Western Equatorial Pacific)
3. Some Details – Key Points in Morphing (1/3)
Western 
Equatorial 
Pacific Ocean
Aug.-Oct. 2017
J. Tan (USRA; GSFC)
Following the CMORPH approach
• for a given time offset from a microwave overpass
• compute the (smoothed) average correlation between
• morphed microwave overpasses and microwave 
overpasses at that time offset, and
• IR precip estimates and microwave overpasses at 
that time offset and IR at 1 and 2 half hours after 
that time offset
• for conical-scan (imager) and cross-track-scan 
(sounder) instruments separately
• the microwave correlations drop off from there, 
dropping below the IR correlation within a few hours (2 
hours in the Western Equatorial Pacific)
• at t=0 (no offset), imagers are better over oceans, 
sounders are better or competitive over land
3. Some Details – Key Points in Morphing (2/3)
L2 correlation at t=0  Aug.-Oct. 2017
Imager
Sounder
J. Tan (USRA; GSFC)
Tested vectors computed on a 5ºx5º template 
every 2.5º, interpolated to 0.1ºx0.1º based on
• MERRA2 TQV (vertically integrated vapor)
• MERRA2 PRECTOT (precip)
• CPC 4-km merged IR Tb (as in V05 IMERG)
• NULL (no motion)
On a zonal-average basis, compute the Heidke
Skill Score for
• merged GPROF precip (HQ) propagated for 
30 min. 
• compared to HQ precip observed in the 
following 30 min.
• TQV is consistently at/near the top
• further research is expected for V07
3. Some Details – Key Points in Morphing (3/3)
J. Tan (USRA; GSFC)
−90 −60 −30 0 30 60 90
latitude
0.500
0.525
0.550
0.575
0.600
0.625
0.650
0.675
0.700
H
S
S
None IR (V05) PRECTOT TQV
Harvey loitered over southeast Texas for a 
week
• Multi-Radar Multi-Sensor (MRMS) 
considered the best estimate
• some questions about the details of the 
gauge calibration of the radar estimate
• over land
• Uncal (just the intercalibrated satellite 
estimates) under(over)-estimated in Area 
1(2)
• should be similar in NRT Late Run
• Cal (with gauge adjustment) pulls both 
areas down
• microwave-adjusted PERSIANN-CCS IR 
has the focus too far southwest
4. Early Results – Hurricane Harvey, 25-31 August 2017, IMERG and MRMS (1/2)
J. Tan (USRA; GSFC)
IMERG largely driven by microwave 
overpasses (dots)
• except duplicate times
• not just time interpolation
• systems move into / out of the box 
between overpasses
• satellites show coherent differences from 
MRMS
• microwave only “sees” the solid 
hydrometeors (scattering channels), 
since over land
• IR looks at Tb within “clustered” data
• both are calibrated to statistics of 
time/space cubes of data
• Cal is basically ( Uncal x factor )
• short-interval differences show some 
cancellation over the whole event
• but several-hour differences can be 
dramatic
4. Early Results – Hurricane Harvey, 25-31 August 2017, IMERG and MRMS (2/2)
J. Tan (USRA; GSFC)


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