GPM Available Products[As of May 9, 2022.]

Standard Products

Product
Processing Satellite / Instrument[Product Identifier/Level/ Algorithm
Algorithm Key*1]

1

Key ParametersFilecoverage

Available Latest Product Version (Caveats)

GPM/DPR/Ku

KuPR L1B[DUB]

Received PowerGPM orbit(Gorbit*)Ver. 07 (See: page 11~)

GPM/DPR/Ka

KaPR L1B[DAB]

Received Power

GorbitVer. 07 (See: page 11~)

GPM/GMI

GMI L1B[G1B]

Brightness Temperature (Tb)

GorbitVer. 07 (See: page 4)

GPM/GMI

GMI L1C[G1C]

Brightness Temperature (Tb)

1 orbitVer. 07 (See: page 5)

Constellation/
MWS

Constellation L1C[ *2 ]

Inter-calibrated Brightness
Temperature (Tb)

GorbitVer. 07 (See: page 5)

KuPR L2[DU2]

Reflectivities, 3D Precipitation

GorbitVer. 07 (See: page 24~)

KaPR L2[DA2]

Reflectivities, 3D Precipitation

GorbitVer. 07 (See: page 24~)

DPR L2[DD2]Dual Frequency Retrievals, 3D
precipitation

GorbitVer. 07 (See: page 24~)Spectral latent heating

GorbitVer. 07 (See: page 31~)
Revised in May 2022

Precipitation, Total Precipitable
Water

GorbitVer. 07 (See: page 7~)

GorbitVer. 07 (See: page 42~)

GPM/DPR

2

SLH-DPR L2[SLP]
GPM/GMI/GPROF
GPM/DPR-GMI/COMB

GPM/DPR

GPM/GMI/GPROF
3

GPM/DPR-GMI/COMBMulti/Multi/GSMaPGMI L2[GL2]

DPR-GMI Comb L2 DPR-GMI retrieval. 3D[CL2]
Precipitation
DPR L3 Daily(TEXT)[D3D]

Precipitation

0.1°x 0.1°
Daily

Ver. 07 (See: page 24~)

DPR L3
Daily(HDF5)[D3Q]

Precipitation

0.25° x 0.25°
Daily

Ver. 07 (See: page 24~)

DPR L3 Monthly[D3M]

Precipitation

0.25° x 0.25°
MonthlyVer. 07 (See: page 24~)

SLH-DPR L3
Gridded orbit[SLG]

Spectral latent heating

0.5°x 0.5°
GorbitVer. 07 (See: page 31~)
Revised in May 2022

SLH-DPR L3
Monthly[SLM]

Spectral latent heating

0.5°x 0.5°
MonthlyVer. 07 (See: page 31~)
Revised in May 2022

GMI L3 Monthly[GL3]

Precipitation

0.25° x 0.25°
MonthlyVer. 07 (See: page 10~)

DPR-GMI Comb L3
Precipitation[CL3]

0.25° x 0.25°
MonthlyVer. 07 (See: page 42~)

DPR-GMI CSH L3 Gridded Orbital Convective[CSG]
Stratiform Heating

0.25° x 0.25°
GorbitVer. 07 (See: page 49～)
To be updated soon

DPR-GMI CSH L3 Monthly Convective Stratiform 0.25° x 0.25°[CSM]
HeatingMonthlyVer. 07 (See: page 49～)
To be updated soon

GSMaP Hourly(TEXT)[MCT]

Precipitation *3

0.1°x 0.1°
HourlyVer. 05 (See: page 36~)
Ver. 04 (See: page 39~)

GSMaP Hourly(HDF5)[MCH]

Precipitation *3

0.1°x 0.1°
HourlyVer. 05 (See: page 36~)
Ver. 04 (See: page 39~)

GSMaP Monthly[MCM]

Precipitation *3

0.1°x 0.1°
MonthlyVer. 05 (See: page 36~)
Ver. 04 (See: page 39~)

* Gorbit is the GPM orbit calculated from the southern most point back to the southern most point

1

GPM Available Products[As of May 9, 2022.](Near real-time data can be downloaded using SFTP after G-Portal user registration and public key authentication.

Near Real-Time Products SFTP directory tree is shown in page 3. *4)
Processing Satellite / Instrument
Level/ Algorithm

Product[Product Identifier/Algorithm Key*1]

Key ParametersFilecoverage

Available Product Version

GPM/DPR/Ku

KuPR L1B[DUB]

Received Power

30 minVer. 07 (See: page 11~)

GPM/DPR/Ka

KaPR L1B[DAB]

Received Power

30 minVer. 07 (See: page 11~)

GPM/GMI

GMI L1B[G1B]

Brightness Temperature (Tb)

5 minVer. 07 (See: page 4)

GPM/GMI

GMI L1C[G1C]

Brightness Temperature (Tb)

5 minVer. 07 (See: page 5)

KuPR L2[DU2]

Reflectivities, 3D Precipitation

30 minVer. 07 (See: page 24~)

KaPR L2[DA2]

Reflectivities, 3D Precipitation

30 minVer. 07 (See: page 24~)

DPR L2[DD2]Dual Frequency Retrievals, 3D
precipitation

30 minVer. 07 (See: page 24~)

GMI L2[GL2]

Precipitation, Total Precipitable
Water

5 min

Ver. 07 (See: page 7~)

30 minVer. 07 (See: page 42~)

1R

GPM/DPR

2R

GPM/GMI/GPROF

GPM/DPR-GMI
/COMB

3R

Multi/Multi/GSMaPDPR-GMI Comb L2 DPR-GMI retrieval. 3D[CL2]
Precipitation
GSMaP Hourly(HDF5)[MFW]

Precipitation *3

0.1°x 0.1°
HourlyVer. 05 (See: page 36~)
Ver. 04 (See: page 39~)

GSMaP Hourly(TEXT)[MFT]

Precipitation *3

0.1°x 0.1°
HourlyVer. 05 (See: page 36~)
Ver. 04 (See: page 39~)Filecoverage

Available Latest Product Version

Environmental data
Temperature, Air Pressure,extracted KuPR
Cloud Water Vapor, Liquid
swath
Water
[DU2/ENV]

GorbitVer. 07

Environmental data
Temperature, Air Pressure,extracted KaPR
Cloud Water Vapor, Liquid
swath
Water
[DA2/ENV]

GorbitVer. 07

Environmental data
Temperature, Air Pressure,extracted DPR
Cloud Water Vapor, Liquid
swath
Water
[DD2/ENV]

GorbitVer. 07

Auxiliary Products
Processing Satellite / Instrument
Level/ Algorithm

AUX.

Auxiliary Data
(JMA/GANAL)

Product[Product Identifier/Algorithm Key*1]

Key Parameters

2GPM Available Products[As of May 9, 2022.]

Notes*1 File Naming Convention
GPM product file naming conventions is as below, and algorithm key is corresponding to (7).

GPMxxx _ sss _ YYMMDDhhmm _ hhmm _ nnnnnn _ LLS _ aaa _ VVv . h5(1)Mission ID(3) Scene Start(2) Sensor ID(4) Scene End(6) Process Level(8)Product Version(5) Orbit Number(7)Algorithm Key
skip for NRT data
indicated in product list
start and end time for L3 productand below note (*2) for L1C

hourly file: YYMMDDhhmm_H
daily file:
YYMMDD_D
monthly file: YYMM_M*2 Product Identifier for Constellation L1C
Satellite
Megha
Tropiques
GCOM-WDMSP F16
DMSP F17DMSP F18
DMSP F19
NOAA-18
NOAA-19NPP
METOP-A
METOP-B
METOP-C
TRMM

Instrument

Product Identifier /Algorithm Key

SAPHIR

SPH

AMSR2
SSMIS
SSMIS
SSMIS
SSMIS
MHS
MHS
ATMS
MHS
MHS
MHS
TMI

AM2
MIS
MIS
MIS
MIS
MHS
MHS
ATS
MHS
MHS
MHS
TMI

*3 Introduced satellite/instrument data in GSMaPSatellite / Instrument

Term

TRMM/TMIDMSP-F16/SSMISDMSP-F17/SSMISDMSP-F18/SSMIS
2014.3.1~2014.3.4 GCOM-W/AMSR2METOP-A/AMSU-A, MHS
METOP-B/AMSU-A, MHSNOAA-18/AMSU-A, MHSNOAA-19/AMSU-A, MHS

2014.3.4~

GPM/GMI (No data during Oct.22-24,2014)
TRMM/TMI (No data from Apr.8,2015 onward)
DMSP-F16/SSMISDMSP-F17/SSMIS (No data from May 1,2018 onward)
DMSP-F18/SSMISDMSP-F19/SSMIS (No data from Feb.11,2016 onward)
GCOM-W/AMSR2METOP-A/AMSU-A, MHS *(No MHS data from Mar.27 to May 20, 2014)
METOP-B/AMSU-A, MHSNOAA-18/AMSU-A, MHS (No data from Dec.24,2018
onward)NOAA-19/AMSU-A, MHS

2021.12.1~

See:https://sharaku.eorc.jaxa.jp/GSMaP/guide.html#05*4 G-Portal SFTP directory tree

3

RELEASE NOTES OF GPM VERSION 07 GMI/TMI 1B/BASE
The PPS V07 GMI 1B/BASE have no calibration changes from V05 GMI 1B/BASE. However, amodule is implemented to perform calibrations under conditions of missing all cold counts or all
hot counts by utilizing the noise diode counts.
The PPS V07 TMI 1B/BASE applied a new Tb correction developed by UCF based on mainreflector physical temperatures. The maximum Tb change is around 0.5K.

Reflector Physical Temperatures, Tphy, is functions of solar-beta-ang & orbit-phase.
Final Tphy Tables:•••

Rows = Solar beta angle in deg. [ -4 to 65 deg.(yaw0) and 4 to -65 deg.(yaw180) in 0.25
deg. step] > 277 rows
Columns = Phase from orbit midnight in deg. [ -180 to 180 deg. in 1 deg. step] > 361
columns
For yaw=180, beta was reversed (i.e., beta=-1*beta;)4

Level 1C Version 07 (V07) Release Notes
This Level 1C Version 07 (V07) release involves the following changes from the previous
release in the GPM radiometer constellation.
1. A new parameter, “sunLocalTime” (local solar time), was added to all GPM V07 Level 1C
products.
2. Level 1C V07 GMI brightness temperature (Tc) remains unchanged from V05. There is no
GMI calibration update. A minor update was implemented to handle long-term failure ofcold load and/or hot load.
3. Level 1C V07 TMI intercalibrated brightness temperature (Tc) differs from V05 by asmuch as 0.7 K due to the following updates:
a. Updated emissive reflector correction in L1B/1Base. V07 corrects a minor error
discovered in the V05 emissive reflector correction and improves the simulated
brightness temperatures used in the correction algorithm with ERA5 inputs instead of
GDAS.
b. Adjusted first pixel time in L1B/1Base. This changes the geolocation by about 0.95
kilometers forward along-track for the 10 GHz channels and 0.15 km back along-track
for the 19-85 GHz channels.
c. Updated intercalibration adjustments (XCAL2021-V) to account for the brightness
temperature changes. Table adjustments range from 0.1 to 0.2 K depending onchannel.

5Acronym List
Below are the acronyms and abbreviations used in this document.
AMSR2
AMSRE
AMSUB
ATMS
ECMWF
GCOM-W1
ERA5
GDAS
GMI
GPM
L1B
METOP-A
MHS
MSPPS
NOAA
NPP
ORB
SAPHIR
SDR
SSMI
SSMIS
Tb
Tc
TIROS
TMI
TOVS
TRMM
V05, V07
V1, V2X-CAL

Advanced Microwave Scanning Radiometer 2
Advanced Microwave Scanning Radiometer Earth Observing System
Advanced Microwave Sounding Unit – B
Advanced Technology Microwave SounderEuropean Centre for Medium-Range Weather Forecasts
Global Change Observation Mission – Water
ECMWF Reanalysis V5
Global Data Assimilation System
GPM Microwave Imager
Global Precipitation Measurement
Level 1B
Meteorological Operational Satellite – A
Microwave Humidity Sounder
Microwave Surface and Precipitation Products System
National Oceanic and Atmospheric Administration
National Polar-Orbiting Partnership
OrbitalSondeur Atmospherique du Profil d’Humidite Intertropicale par Radiometrie
Sensor Data Record
Special Sensor Microwave Imager
Special Sensor Microwave Imager/Sounder
Brightness Temperature
Intercalibrated Brightness Temperature
Television Infrared Observation Satellites
TRMM Microwave Imager
TIROS Operational Vertical Sounder
Tropical Rainfall Measuring Mission
Version 5, Version 07
Version 1, Version 2
GPM Intercalibration Working Group

6

May 3, 2022
ATBD GPM V7: http://rain.atmos.colostate.edu/ATBD/ATBD_GPM_V7_May15_2021.pdf

Release Notes for GPROF V7 Public Release

The Goddard Profiling Algorithm is a Bayesian approach that nominally uses the GPM
Combined algorithm to create its a-priori databases. Given the importance of these
databases to the final product, they are worth reviewing before discussing particularchanges to the algorithm. GPROF V03 was implemented at the launch of the GPM mission
and thus had no databases from the GPM satellite itself. Instead, databases were made froma combination of TRMM, Cloudsat, ground based radars and models. V04 used the GPM
generated databases but had a very short lead time as the radar and combined algorithm
were in flux until nearly the date of the public release. Because the V04 of the Combinedalgorithm appeared to significantly overestimate precipitation over land, the a-priori
databases were constructed from the Combined Algorithm (V04) over ocean, but the DPR
Ku (V04) over land and coastal regions. The very short lead time to produce the a-priori
databases led to insufficient testing of GPROF V04 that resulted in some less-than ideal
retrievals.

GPROF V05 retained the previous version (i.e. V04) of the Combined and DPR-Ku products
for its databases, but improved some of the ice hydrometeor simulations in order to getbetter agreement between computed and simulated brightness temperatures. Additionalchanges were made to retrievals of high latitude oceanic drizzle and snowfall over land as
the DPR sensitivity of 12 dBZ (and therefore the Combined and Radar products) was
shown to miss substantial amounts of drizzle and light to moderate snowfall events. Drizzle
in the a-priori database was matched to the CloudSat based probability of rainfall and thiswas partitioned between cloud- and rain water. This increased rain water at high altitudes
to agree better with CloudSat and ERA and MERRA re-analyses, but continued to be low
relative to these estimates.
There is not GPROF V06. Instead, GPROF jumped to GPROF V07 to mirror the other GPM
products that were produced starting in May 2022. In GPROF V07, the a-priori databases
were constructed singularly from the Combined Radar/Radiometer Algorithm (V07) where
it sees precipitation, while non-precipitating and light drizzle (up to 0.2 mm/hr) arecreated from the operations Microwave Integrated Retrieval System (MiRS) Optimal
Estimation retrieval. The GPROF V07 database, as done previously, uses only the middle 21
pixels of the radar scan. This subset of the CMB product produces more rain than theentire swath, and V7 produces substantially more rain than the CMB product used in
GPROF V05. Rain rates have thus increased significantly, in the 12% range globally.
Some changes were made to the coast and land surface types for GPROF V07. Previous
versions of GPROF showed spurious precipitation in coastal areas. To improve estimates,
the V05 coast surface class was split into three classes of decreasing ocean/water
percentages: water coast, mixed coast and land coast.

7

Similarly, areas of complex terrain have been identified as lacking orographically enhanced
precipitation, compared to ERA5 and High Resolution Rapid Refresh (HRRR) model data. A
mountain rain and mountain snow class are included in GPROF V07. For the mountain rain
class, the surface type was further subclassified with an airmass lifting index (ALI), basedon whether atmospheric conditions were stratiform and orographically enhanced. The
Combined product precipitation used int eh a-priori database for the two new mountainclasses was also scaled to match ERA5, which was considered to be a better mountainprecipitation climatology.
Over land, the US based MRMS data was used to build a-priori databases for snow covered
surfaces of each of the constellation’s radiometers. Five years of MRMS data were matched
up with individual satellite overpasses, compared to two years in GPROF V05. While MRMS
data removed much of the low bias that GPROF V04 had over snow covered surfaces,
further adjustments were made to make the MRMS data spatially consistent with SNOw
Data Assimilation System (SNODAS) precipitation estimates.

Sea ice a-priori databases in GPROF V07 are created with ERA5 precipitation, since the DPR
is insufficient over these surface types. As with the MRMS data, the sea ice database wasonly 2D and did not contain the vertical hydrometeor profiles, so no profile information isavailable from the GPROF retrieval over sea ice covered surfaces.
A change has been made to the GPROF V05 profile species, which were: rain water content,
cloud water content, ice water content, snow water content and graupel/hail content. In
GPROF V07, the profile species are: rain water content, cloud water content, snow watercontent, graupel/hail content and latent heating. However, the graupel/hail content andlatent heating profiles are currently set to missing, with the latter to be implemented in thenext version.

A final modification made to GPROF is the determination of a precipitation threshold.
GPROF V04 reported an unconditional rain rate and a probability of precipitation, it was up
to the user to set a threshold (either in probability or rain rate) if rain/no rain information
was needed. While GPROF V05 reported the same information, the algorithm internallydecided if the pixel was precipitating or not, and non-precipitating pixels were set to zerorainfall. This required the algorithm to replace the missing rain by slightly increasing the
precipitation in the pixels that were deemed raining. GPROF V07 reverts back to theunconditional rain rate and probability of precipitation being reported, as it is really notpossible for passive microwaves to distinguish clouds from very light rain in an
unambiguous manner. To accommodate users who need a binary yes/no answer, a new
precipitation flag has been added to the output. If the algorithm determined the pixel wasmost likely precipitating, the ‘precipitationYesNoFlag’ is set to raining.

Limited validation of GPROF V07 for GMI shows better correlations and smaller biases than
GPROF V05. Statistics were run over the Continental United States with MRMS data andglobally with Combined data. Even more limited validation has been done on snow due tothe difficulty in getting reliable ground-based measurements.
8

GPROF V07 chose to wait for the latest interation of the GPM CMB product in order betterto synchronize the products. The short lead time to produce the a-priori databases,
unfortunately, also led to less than full testing of GPROF V07. Little validation has been
done on the constellation radiometers beyond limited comparisons with GMI (or TMI for
earlier sensors) to ensure that the retrieval is performing as expected.
The GPROF V07 output file ‘airmassLiftIndex’ has replaced the V05 parameter labeled‘CAPE’ (set to missing in GPROF V05), as it is not used.

Sims, E.M. and G. Liu, 2015: A Parameterization of the Probability of Snow–Rain Transition.
J. Hydrometeor., 16, 1466–1477, doi: 10.1175/JHM-D-14-0211.1.

National Operational Hydrologic Remote Sensing Center. 2004. Snow Data Assimilation
System (SNODAS) Data Products at NSIDC, Version 1. Boulder, Colorado USA. NSIDC:
National Snow and Ice Data Center. doi: https://doi.org/10.7265/N5TB14TC.

9GPM Level 3 GPROF Version 07 Information
Version 07 Release Notes:
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
precipitationYesNoFlag=1 are counted as precipitation pixels.
Description:
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 andconstellation satellites.
Each L3GPROF product contains global 0.25o x 0.25o gridded monthly/daily unconditional means andpixel counts. Monthly product filenames start with 3A-MO or 3A-CLIM-MO, and daily productfilenames start with 3A-DAY or 3A-CLIM-DAY.
For example:
3A-MO.GPM.GMI.GRID2021R1.20161201-S000000-E235959.12.V07A.HDF5
3A-DAY.GPM.GMI.GRID2021R1.20161201-S000000-E235959.336.V07A.HDF5
3A-CLIM-MO.GPM.GMI.GRID2021R1.20161201-S000000-E235959.12.V07A.HDF5
3A-CLIM-DAY.GPM.GMI.GRID2021R1.20161201-S000000-E235959.336.V07A.HDF5
Current available L3GPROF V07 products are listed in the following table:
Product ID
3GPROF
3GPROF

Radiometer
AMSR2
ATMS

3GPROF
3GPROF

GMI
MHS

3GPROF

SSMI/S

1Satellite
GCOM-W1NPP
NOAA20GPM
NOAA-18
NOAA-19
METOP-A
METOP-B
METOP-C
F16
F17
F18
F19

10

Release Notes for the DPR Level 1 products

All users of DPR Level 1 data should keep in mind the following changes in Version
07A products.
＜Major changes in the DPR Level 1 products from Version 05C to Version 07A＞
1.

Refinement of the scan angle table (KuPR)
JAXA implements a small adjustment (about 0.024 deg) of the scan angle tableused in the geolocation calculations based on a statistical analysis of KuPR’s
geolocation. Note that KuPR’s hardware setting related to beam pointing does
not change, and a parameter in the scan angle table for calculating thegeolocation of beam is updated.

2.

A new quality flag for mirror image (KuPR and KaPR)
binMirrorImage which notifies an unusual precipitation at high altitude by

the mirror image of DPR is introduced.

3.

Swath name change (KuPR)
A swath name in KuPR Level 1 product is changed from ‘NS’ to ‘FS’.

4.

Refinement of a table for gain of phase shifter (KaPR)
JAXA implements a small adjustment to a table for the gain of phase shifter
which is one of the components in the receiver system and is responsible for the

DPR’s beam direction. This is expected to mitigate an angle bin dependency of
the gain depending on the phase shifter setting after the change of KaPR’s scanpattern on 21 May 2018. This adjustment results in a maximum change ofabout 0.5 dB in the received power and noise power of KaPR.
5.

New variables related to geolocation (KuPR and KaPR)
New variables related to geolocation are newly added. They are sunLocalTime,

scHeadingGround, scHeadingOrbital, sunData group. See full descriptions

by the following link.

https://www.eorc.jaxa.jp/GPM/en/archives.html＜Minor changes in the DPR Level 1 products from Version 05B to Version 05C＞
JAXA and NASA implemented a small adjustment of the instrument alignments
used in the geolocation calculations based on the beam matching adjustments in

11Version 05B. These shifts in pointing will occur in the V05A/B data once that data isreprocessed as well.
1.

The KaPR roll alignment was adjusted so that the calculated beam locations on
the surface better match those for KuPR in the cross-track direction. The
adjustment changes the KaPR locations by about 100 meters cross track.

2.

The KuPR pitch alignment was adjusted so that the calculated beam locations
on the surface better match those for KaPR in the along-track direction. The
adjustment changes the KuPR locations by about 40 meters along track.

＜Minor changes in the DPR Level 1 products from Version 05A to Version 05B＞
JAXA and NASA implemented a change of the scan pattern of the KaPR on 21 May
2018.

Figure 1. DPR’s scan pattern before May 21 2018 (left) and after May 21 2018 (right). KaHS
beams scan in the inner swath before May 21 2018, but now they scan in the outer swath andmatch with KuPR’s beams. Numbers in color indicate angle bin numbers for KuPR (blue),
KaMS (yellow), and KaHS (red).

According to this event, the DPR Level 1 product version has changed from 05A to
05B. Major effects were summarized as follows.
1.

Change of the scan pattern of the KaPR.
JAXA changed the scan pattern of the KaPR on 21 May 2018. As shown in Fig.1,
the high sensitivity beams of KaPR, which is called KaHS, scanned in the inner

12

swath and interlaced between the matched beams of KaPR, which is called
KaMS, from the beginning of the mission until May 21. As of May 21, KaHS
beams are moved to the outer swath and match with the KuPR beams there sothat the dual-frequency algorithm can be applied over the full swath. The
format of the product does not change in V05B. The first 12 elements in each of
KaHS’s scan data that consist of 24 angle bins correspond to the last 12 angle binsof the KuPR angle bins, and the remaining 12 elements correspond to the first 12
angle bins of the following KuPR scan. A warning flag is set in the variable called“dataQuality”.

2.

Improvement of beam matching between the KuPR and the KaPR.
JAXA has adjusted the beam directions of the KuPR and the KaPR in order toimprove the beam matching between the KuPR scans and KaPR scans. The
offset between the KuPR and KaMS footprint centers at nadir has reduced
approximately from 300m to 30m since the adjustment on 21 May 2018,.

＜Major changes in the DPR Level 1 products from Version 4 to Version 5＞
1.

Changes of the DPR’s calibration parameters.
JAXA improved the DPR’s calibration parameters in the Version 5 products
based on the new calibration results on orbit. With the new parameters, themeasured radar reflectivity factors increase by about +1.3 dB for the KuPR and
by about +1.2 dB for the KaPR from the corresponding Version 4 data.

2.

Changes of FCIF-LUT.
The temperature dependence of the FCIF input/output characteristic was
improved based on the re-examination of calibration data. The re-examination
revealed that the version 4 algorithm compensate the temperature changes toomuch. In V5, the gain adjustment due to the temperature change is nullified
because the actual temperature of FCIF is very stable on orbit.
The FCIF-LUT for the KuPR near the saturation was improved so that theeffect of saturation near the saturation level was mitigated. As for the KaPR,
the modification was not made because saturations do not occur in the KaPR ina normal condition.

3.

Data format change
The following two new variables were added and one variable was modified.

13

‘receivedPulseWidth’ that indicates the received pulse width after passing
through the band-pass-filter was added.

‘totalCoefVersion’ that indicates the total version, which consists of theversion number of the calibration coefficients and the version number of the
FCIF-LUT, was added.

‘transReceiverCoefVersion’ that indicates the version number of thecalibration coefficients was modified.

4.

Improvement in noise power calculation
The definition of the DPR’s noise power was changed. Since the effect of the
band-pass filter to noise is different from that of the radar echoes (the former
has a continuous flat spectrum while the latter has a non-flat spectrum definedby the transmitting pulse shape), the version 4 algorithm used a formula tocalculate the noise power that differs from the formula to calculate the echo
power. In version 5, the noise power is also calculated with the formula that isused to calculate the echo powers so that the noise power in V5 is the effective
noise power that can be compared with echo powers directly.

14

＜Caveats for the DPR Level 1 products＞
1.

Status of the DPR A-side operation
JAXA changed that the status of the DPR data obtained during the A-side
operation in products Version5 (B-side is used in the current standard
operation). The reasons for such handling are that the calibration parameters of
the DPR A-side are not reliable, because no external calibration of the DPR
A-side was carried out on orbit, and the ground test values of the DPR A-side
before launch are not reliable either. Therefore, the corresponding scans wereattached with identification flags in the DPR Level 1 products, and these scans
were treated as missing scans in the DPR Level 2 products. The following tableshows the periods of the DPR A-side nominal operation.

Operational mode

PeriodOrbit number

SCDP-B/FCIF-A

2014/3/10~3/12, 5/25

171 - 205, 1351-1355

SCDP-A/FCIF-A

2014/3/14~16

232-262

SCDP-A/FCIF-B

2014/3/16~18

263-295

B-side (SCDP-B/FCIF-B) is used in the rest of the period.
2.

Calculation of the DPR’s noise power
The DPR has a special mode to measure background noise in which the averagenoise power is measured while the transmitter is turned off at each angle bin in
the ordinary observation mode (‘Noise-A’). This noise power is subtracted fromthe received power measured at each range bin to extract radar echo power
profile. To calculate the echo profile, the effect of the band-pass filter assumes
that the echo has the same power spectrum defined by the transmitted pulse
shape. Since the received power is the sum of echo power and noise power, thenoise power recorded in each profile is the effective noise (‘Noise-B’) that affectthe echoes. In version 4, Noise-A was calculated by assuming that the noise has
a flat spectrum. The difference in the conversion formulas to calculate Noise-A
and Noise-B created some confusions to the users although the noise wasproperly subtracted in the products. In order to avoid the confusion, Noise-A is
calculated with the same formula that is used to calculate the echo profile (andhence Noise-B) in version 5. (As a result, the noise power calculated by using
the hardware design document is the lower than the power stored in the DPR

15

L1 product. The magnitudes of difference are -2.11dB, -2.41dB and -2.13dB for
the KuPR, the KaPR(MS) and the KaPR(HS), respectively. )3.

Beam directions of the KaPR
JAXA uploaded a proper set of phase code to the KaPR on August 6th, 2014.
Until that time from April 8th, 2014, the beam pointing directions of the KaPR
had small biases. The code error caused a beam match between the KuPR and
the KaPR(MS) and affected the KaPR’s total antenna gain slightly. After theproper code was uploaded, the beam scans in the proper directions and the bias
error were resolved. The DPR Level 1 algorithm was modified to compensate
this bias for the antenna gain in this period so that the antenna gain in the
products is correct.

4.

Reversal of DPR’s scan direction
The beam scan direction of DPR had been reversed from the proper direction
until JAXA uploaded a proper set of phase code to the DPR at 13:20 UTC on
March 18th, 2014. After the proper code was uploaded, the beam has beenscanned in the proper direction, i.e., from left to right with respect to the +X
forward direction of the satellite.
The DPR Level 1 algorithm was modified to accommodate this change so thatthe geolocations in the products are correct from the beginning of the mission.

5.

Special operations of DPR
The following caveats describe special operations of DPR. You can use thesedata with your discretion. You can also refer to the DPR invalid data lists at thefollowing web site. https://gportal.jaxa.jp/gpr/information/product5.1. Operation with the DPR transmitters off
JAXA carried out the receiving only mode to check the DPR receiver system.
The orbits in which this operation was performed are shown Appendix-A andoperation status in the following site.

https://gportal.jaxa.jp/gpr/information/product5.2. Change of the DPR receiver attenuator (RX ATT) setting
JAXA has checked the dynamic range of the radar system by changing theattenuator setting in the DPR receivers. The received power in the DPR Level 1

16

products is not affected, because the offset caused by the receiver attenuator isaccounted for in the DPR Level 1 algorithm. The orbits in which this operation
was performed are shown Appendix-A and operation status in the following site.
https://gportal.jaxa.jp/gpr/information/product5.3. Operation of GPM satellite maneuverNASA has carried out several maneuver operations such as a delta-V maneuver
and a Yaw maneuver. In addition, pitch offset maneuvers have also been
conducted to check the GPM satellite status. The orbits in which these
operations were performed are shown Appendix-A and operation status in thefollowing site. https://gportal.jaxa.jp/gpr/information/product5.4. Test operation for adjusting the phase code in the KuPR instrument
The JAXA DPR project team has conducted several test operations using
different phase codes in the phase shifters in order to mitigate the effects ofsidelobe clutter in KuPR. Please be cautious of the periods in these test
operations. The orbits in which these operations were performed are shown
Appendix-A and operation status in the following site.
https://gportal.jaxa.jp/gpr/information/product17

＜Appendix A: Major DPR events＞
Major DPR events until September 2, 2014 are as follows. After September 2, you
can visit the following web site to check the DPR status.
https://gportal.jaxa.jp/gpr/information/product
Orbit No.

UTC

DPR Event#144

2014/3/8 21:54

DPR observation start#171

2014/3/10 16:29

Change DPR FCIF-B to A

#201

2014/3/12 14:24

GPM Delta-V Maneuver#206

2014/3/12 22:43

DPR power OFF#207-231

2014/3/13-14

GPM EEPROM change#232

2014/3/14 14:14

DPR SCDP-A ON#232

2014/3/14 14:41

DPR check out restart#236

2014/3/14 20:02

DPR observation restart#263

2014/3/16 14:08

Change DPR FCIF-A to B

2014/3/16 14:59

DPR transmitters off (f1/f2 off) test

#264

2014/3/16 15:49#279

2014/3/17 15:10

GPM 180deg Yaw Maneuver (+X to -X)#294

2014/3/18 13:20

Proper phase code upload#296

2014/3/18 17:18

DPR SCDP-B ON Observation mode

#310

2014/3/19 14:21

GPM Delta-V Maneuver#325

2014/3/20 13:41

DPR patch adaption#328

2014/3/20 17:56

DPR observation restart#374

2014/3/23 17:26

DPR transmitters off observation#375

2014/3/23 19:05
2014/3/23 19:06

SSPA LNA analysis mode#377

2014/3/23 22:35

DPR observation restart#380

2014/3/24 2:11

DPR External calibration#404

2014/3/25 15:07

DPR transmitters off observation#418

2014/3/26 12:32#419

2014/3/26 14:20

GPM Delta-V Maneuver#478

2014/3/30 9:53

DPR External calibration#503

2014/4/1 0:00

DPR External calibration (Yaw + pitch)#531

2014/4/2 19:47

GPM Delta-V Maneuver#601

2014/4/7 7:37

DPR External calibration18

Orbit No.

UTC

DPR Event#621

2014/4/8 14:10

Upload new test phase code of KuPR (#1)#626

2014/4/8 21:46

DPR External calibration (Yaw + pitch)#647

2014/4/10 6:36

DPR External calibration#672

2014/4/11 20:43

DPR External calibration (Yaw + pitch)#675

2014/4/12 1:45

GPM Delta-V Maneuver#715

2014/4/14 15:28

Upload new test phase code of KuPR (#2)#731

2014/4/15 15:44

Return to phase code (#1)#675

2014/4/12 1:45

GPM Delta-V Maneuver#747

2014/4/16 17:04

GPM Delta-V Maneuver#748

2014/4/16 17:39

DPR transmitters off observation#763

2014/4/17 17:07#770

2014/4/18 4:22

DPR External calibration (Yaw + pitch)#795

2014/4/19 18:31

DPR External calibration (Yaw + pitch)#795

2014/4/19 18:55

Ku/Ka RX ATT change 6dB to 9dB#810

2014/4/20 17:59

Ku/Ka RX ATT change 9dB to 12dB

#824

2014/4/21 15:36

Ku/Ka RX ATT change 12dB to 6dB#827

2014/4/21 20:34

GPM Delta-V Maneuver#885

2014/4/25 13:05

GPM ST alignment and IRUCAL table updates#886

2014/4/25 14:30

GPM +10 deg. roll slew

2014/4/25 15:20

GPM +10 deg. pitch slew

#887

2014/4/25 16:10

GPM +10 deg. yaw slew

#901

2014/4/26 13:30

GPM 180deg Yaw Maneuver (-X to +X)#907

2014/4/27 0:00

GPM -1 deg. pitch slew

#913

2014/4/27 8:20

GPM -1 deg. pitch slew (-2 deg. total)#918

2014/4/27 16:20

GPM -2 deg. pitch slew (-4 deg. total)#923

2014/4/28 0:25

#924

2014/4/28 1:10

Ku/Ka RX ATT change 6dB to 9dB#933

2014/4/28 15:04

Upload new test phase code of KuPR(#3)#935

2014/4/28 18:13

Return to phase code(#1)#964

2014/4/30 15:50

GPM Delta-V Maneuver#994

2014/5/2 13:20

Upload new test phase code of KuPR (#4)

2014/5/2 13:21

Ku/Ka RX ATT change 9dB to 6dB#996

2014/5/2 16:36

Upload new test phase code of KuPR(#5)#998

2014/5/2 19:44

Ku/Ka RX ATT change 6dB to 9dB

19Orbit No.

UTC

DPR Event

2014/5/2 19:45

Return to phase code (#1)#1059

2014/5/6 17:35

GPS both A and B ON

#1103

2014/5/14 13:44#1073

2014/5/7 15:57

GPM Delta-V Maneuver#1088

2014/5/8 14:15

Ku SSPA analysis mode (5min)2014/5/8 15:08

Ka SSPA analysis mode (5min)2014/5/8 15:48

Ku LNA analysis mode (5min)2014/5/8 16:44

Ka LNA analysis mode (5min)#1090

2014/5/8 17:23

Upload new test phase code of KuPR (#6)#1092

2014/5/8 20:21

Ka SSPA analysis mode (5min)2014/5/8 21:12

Upload new test phase code of KuPR (#7)#1094

2014/5/9 0:16

Return to phase code(#1)#1150

2014/5/12 14:58

Ku/Ka RX ATT change 9dB to 12dB

#1182

2014/5/14 16:07

GPM Delta-V Maneuver#1274

2014/5/20 13:30

GMI Deep Space Calibration#1277

2014/5/20 18:44#1288

2014/5/21 11:30

Upload new test phase code of KuPR (#8)#1290

2014/5/21 14:43

Upload new test phase code of KuPR (#9)#1292

2014/5/21 17:59

Upload new test phase code of KuPR (#10)#1294

2014/5/21 21:07

Upload new test phase code of KuPR (#11)#1296

2014/5/22 0:16

Return to phase code(#1)#1319

2014/5/23 11:38

Upload new test phase code of KuPR (#12)#1322

2014/5/23 15:03

Upload new test phase code of KuPR (#13)#1324

2014/5/23 15:03

Upload new test phase code of KuPR (#14)#1326

2014/5/23 21:37

Upload new test phase code of KuPR (#15)#1328

2014/5/24 0:57

Return to phase code(#1)#1351

2014/5/25 11:44

Change DPR FCIF-B to A (For External Cal.)#1089

Ku/Ka RX ATT change 12dB to 6dB#1354

2014/5/25 17:18

DPR External calibration (Yaw + pitch)#1355

2014/5/25 17:54

Change DPR FCIF-A to B
Ku/Ka RX ATT change 6dB to 12dB

#1414

2014/5/29 13:59

GPM Delta-V Maneuver#1430

2014/5/30 13:50

Upload new test phase code of KuPR (#16)#1431

2014/5/30 15:26

Upload new test phase code of KuPR (#17)

20

Orbit No.

UTC

DPR Event#1432

2014/5/30 17:01

Upload new test phase code of KuPR (#18)#1433

2014/5/30 18:34

Upload new test phase code of KuPR (#19)#1434

2014/5/30 20:07

Return to phase code(#1)#1447

2014/5/31 16:06

Upload new test phase code of KuPR (#20)#1448

2014/5/31 17:53

Upload new test phase code of KuPR (#21)#1449

2014/5/31 19:59

Return to phase code(#1)#1477

2014/6/2 15:06

DPR External calibration#1502

2014/6/4 5:15

DPR External calibration#1508

2014/6/4 14:13

Upload new test phase code of KuPR (#22)#1508

2014/6/4 14:56

Upload new test phase code of KuPR (#23)#1509

2014/6/4 16:39

Upload new test phase code of KuPR (#22)#1511

2014/6/4 18:59

Return to phase code(#1)#1539

2014/6/6 14:09

Upload new test phase code of KuPR (#22)#1541

2014/6/6 17:26

Return to phase code(#1)#1600

2014/6/4 5:15

DPR External calibration#1603

2014/6/10 17:38

GPM 180deg Yaw Maneuver (+X to -X)#1625

2014/6/12 2:58

DPR External calibration#1646

2014/6/13 11:46

DPR External calibration#1648

2014/6/13 14:08

Upload new test phase code of KuPR (#24)#1649

2014/6/13 15:45

Upload new test phase code of KuPR (#25)#1650

2014/6/13 17:36

Upload new test phase code of KuPR (#26)#1651

2014/6/13 19:12

Upload new test phase code of KuPR (#27)#1652

2014/6/13 20:54

Upload new test phase code of KuPR (#28)#1653

2014/6/13 22:33

Upload new test phase code of KuPR (#29)#1654

2014/6/14 0:21

Upload new test phase code of KuPR (#30)#1655

2014/6/14 1:39

Return to phase code(#1)#1726

2014/6/18 15:17

GPM Delta-V Maneuver#1769

2014/6/21 9:33

DPR External calibration#1794

2014/6/22 23:42

DPR External calibration (Yaw + pitch)#1892

2014/6/29 7:18

DPR External calibration#1917

2014/6/30 21:27

DPR External calibration#1942

2014/7/2 12:42

Upload new test phase code of KuPR (#31)#1944

2014/7/2 14:38

Upload new test phase code of KuPR (#32)#1945

2014/7/2 16:30

Return to phase code(#1)

21Orbit No.

UTC

DPR Event#1975

2014/7/4 15:07

Upload new test phase code of KuPR (#33)#1976

2014/7/4 16:44

Upload new test phase code of KuPR (#34)#1977

2014/7/4 18:24

Return to phase code(#1)#2015

2014/7/7 5:01

DPR External calibration#2040

2014/7/8 19:08

DPR External calibration (Yaw + pitch)#2053

2014/7/9 16:17

GPM Delta-V Maneuver#2163

2014/7/16 16:32

GPM 180deg Yaw Maneuver (-X to +X)#2176

2014/7/17 13:22

Upload new test phase code of KuPR (#35)#2177

2014/7/17 15:03

Upload new test phase code of KuPR (#36)#2178

2014/7/17 16:37

Upload new test phase code of KuPR (#37)#2180

2014/7/17 18:47

Return to phase code(#1)#2184

2014/7/18 1:42

DPR External calibration#2209

2014/7/19 15:51

DPR External calibration#2286

2014/7/24 14:54

Change Ku timing delay#2289

2014/7/24 19:11

Upload new test phase code of KuPR (#38)#2290

2014/7/24 20:49

Return to phase code(#1)#2304

2014/7/25 18:07

Upload new test phase code of KuPR (#39)#2307

2014/7/25 23:26

DPR External calibration#2332

2014/7/27 13:34

DPR External calibration#2380

2014/7/30 16:04

GPM Delta-V Maneuver#2430

2014/8/2 21:12

DPR External calibration (Yaw + pitch)#2455

2014/8/4 11:21

DPR External calibration#2455

2014/8/6 20:48

Upload new phase code of KaPR

#2599

2014/8/13 17:55

DPR External calibration#2624

2014/8/15 8:03

DPR External calibration#2706

2014/8/20 15:09

GPM Delta-V Maneuver#2722

2014/8/21 15:40

DPR External calibration#2747

2014/8/23 5:48

DPR External calibration#2782

2014/8/25 12:15

Change DPR FCIF-B to A

#2782

2014/8/25 12:30

Upload new test phase code of KuPR
(FCIF-A#1)#2784

2014/8/25 14:34

Upload new test phase code of KuPR
(FCIF-A#2)#2785

2014/8/25 16:13

Upload new test phase code of KuPR

22

Orbit No.

UTC

DPR Event(FCIF-A#3)#2786

2014/8/25 17:51

Upload new test phase code of KuPR
(FCIF-A#4)#2787

2014/8/25 19:22

Change DPR FCIF-A to B

#2787

2014/8/25 19:24

Return to phase code(#39)

23

Release Notes for the DPR Level 2 and Level 3 products＜Major changes in the DPR Level2 from Version 06A to Version 07A＞
Version 07A is the first standard product to account for the Ka-band Precipitation Radar (KaPR)
scan pattern changes implemented on May 21, 2018. This change in scan pattern allows for a moreaccurate precipitation estimation method for dual frequency radar, Ku-band and Ka-band
Precipitation radar (KuPR and KaPR), to be applied to the entire observation swath.
On the other hand, this led to significant changes of DPR file specifications for common filestructures before and after the scan change, in addition to algorithm evolutions. See full descriptions
by the following link.
https://arthurhou.pps.eosdis.nasa.gov/atbd.html
https://www.eorc.jaxa.jp/GPM/en/archives.html
1.

Format change in the productIn the V06X (experimental product), a new format was implemented including “FS”
which is defined as the full swath dual-frequency product with 125 m range resolution. In the
V07A, this FS format is applied to data taken both

before and after the scan pattern change ofthe KaPR in May 2018 (Fig. 1).

Figure 1. Changes of File structure from V06 to V07.

24

2.

In the version 07, the improved sidelobe clutter removal routine was implemented for the
single frequency (KuPR, KaPR, and PR) L2 algorithms based on the results of Kanemaru et al.
(2020, 2021). In addition, a new 3-D precipitation judgment method is implemented to improvethe detectability of precipitation signals. This method uses signals not only in the verticaldirection but also the in cross-track and along-track directions. This method is expected to
improve the detection of weak, horizontally distributed precipitation that often occurs at highlatitudes See the ATBD for full descriptions.

3.

"flagHail" is newly implemented in the CSF. (Dual frequency only.)

4.

The following new items are added to present information about flagHeavyIcePrecip in the
CSF: binHeavyIcePrecipTop, binHeavyIcePrecipBottom, and nHeavyIcePrecip.

5.

Variables of the Trigger module (TRG) are newly implemented in V07.

6.

Changes of DSD/Solver module from V06 to V07 are listed as follows. The soil moistureeffect, listed as C) below, is expected to lead to increases of the precipitation amount over landas estimated by the single frequency algorithms by about 14-23%.
A) Revision of the relationship between precipitation rate R and volume-weighted mean drop
size Dm (the so-called, R-Dm relationship). In addition, the parameterε in the R-Dm,
which is independent of range in V06, is allowed to vary with range in V07.
B) Revision of DSD database (used for single-frequency)C) Soil moisture effect“precipRateEsurface2”, which is a surface precipitation estimate based on an a7.

priori low-level precipitation profiles, is calculated in the Experimental variable,
based upon Hirose et al. (2021, https://doi.org/10.2151/jmsj.2021-060).
8.

Other changes in DPR L2 algorithm:
“binMirrorImageL2” is newly implemented in the Preparation module (PRE) to notify an
unusual precipitation at high altitude by the mirror image of the DPR.

The VER includes new variables such as the profile of air temperature, a “flag” that indicatesan inversion layer of air temperature, and the rain-free piaNP.



Several variable names are changed.
zFactorCorrected → zFactorFinalzFactorCorrectedESurface → zFactorFinalESurface

zFactorCorrectedNearSurface → zFactorFinalNearSurface

Bugs are fixed in several modules.

25

＜Major changes in the DPR Level2 from Version 05B to Version 06A＞
1.

A new SRT code has been modified to include calculations of Hitschfeld-Bordan PIA as well asa hybrid PIA that combines HB and SRT results. As a result of applying the new PIA in SLV
module, erroneously large estimates of high precipitation over ocean (near coast) are mitigatedand both DPR(MS) and Ku rain estimates in V06A agree better with Ground Validation dataover USA.
A new classification algorithm is introduced by the University of Washington (Stacy Brodzik &2.

Robert Houze) to reclassify the stratiform rain type. The new algorithm improved the angle-bin
dependence of rain classification and SLH profiles.
Minor changes in DPR L2 algorithm:


Mitigated KaHS’s sidelobe clutter and re-calculated the data base for KuPR’s sidelobe clutter.



New variables are introduced. They are flagScanPatten in PRE module, PIAhb,

PIAhybrid, reliabFactorHY, reliabFlagHY, stddevEff, stddevHY and zeta

in SRT module. The definitions of these variables can be found in the user manual.
Modification of surface snow Index for the winter temperature inversions.



Applied the latest SRT data base.

Bug fix of flagEcho in dual frequency data processing.
＜Major changes in the DPR Level3 products from Version 05B to Version 06A＞

1.

New variables are introduced. They are zFactorMeasuredNearSurface,

DFRNearSurface, DFRmNearSurface, piaHybrid, piaHybridDPR, piaHB, zeta,
and flagHeavyIcePrecip. The definitions of these variables can be found in the user
manual.

(or: For definitions of these variables, the reader is referred to the user manual.)26

＜Major changes in the DPR Level2 products from Version 05A to Version 05B＞

Figure 2. DPR’s scan pattern before May 21 2018 (left) and after May 21 2018 (right). KaHS
beams scan in the inner swath before May 21 2018, but now they scan in the outer swath andmatch with KuPR’s beams. Numbers in color indicate angle bin numbers for KuPR (blue), KaMS
(yellow), and KaHS (red).

1. As of May 21, 2018, the scan pattern of KaHS beams was changed as shown in Fig. 2. The KaHS
beams scanned in the inner swath before May 21 2018, but now they scan in the outer swath andmatch with KuPR’s beams. Please note that the range resolution of KaHS is 500m and differsfrom that of the KuPR or KaMS. One scan of KuPR over the full swath consists of 49 beams
which are numbered from 1 to 49 in Fig. 1 below. The array for one scan of KaMS data consistsof 25 beams which correspond to KuPR’s central beams from 13 to 37, whereas one scan of KaHS
data array for 24 beams consists of two parts. The first 12 elements in the array correspond to
KuPR’s beams from 38 to 49, and the last 12 elements correspond to KuPR’s beams from 1 to 12of the following scan. All KaMS beams and the first 12 KaHS beams match well with KuPR’s
beams from 13 to 49. The magnitude of misalignment between these KuPR and KaPR beams is
estimated to be less than 50 m after May 21. However, KaHS’s beams from 13 to 24 are slightly
shifted from the corresponding KuPR beams from 1 to 12 in the along-track direction. The
magnitude of misalignment in these beams is about 400 m.
The dual-frequency algorithm can be applied to the full swath of data after the change of scanpattern. However, because it takes time to develop a new algorithm applicable to the full swath
and to adjust the necessary parameters to ensure the quality of the products, KaHS data are notprocessed in V05B L2 algorithm. Missing values are stored in the output variables that use KaHS
data. KaHS’s received power data are available only in L1 products in V05B.

27

2. All beam directions of KuPR and KaPR were adjusted to match better with the nominal footprint
locations. The difference between KuPR and KaPR’s footprint centers is now about 30 m at nadir.
It was about 300 m before May 21, 2018. This improvement of beam matching is not considered
to make a big difference in the L2 output products except for very heavy rain cases because themismatch was small (300 m) from the beginning.

28

＜Major changes in the DPR Level2 products from Version 04A to Version 05A＞

All users of DPR L2 data should keep in mind the following changes in V5 products.
This document describes only the major changes in the level 2 products. There are somechanges in level 1 that affect the level 2 products. Please refer to “Release Notes for the DPR
Level 1 products” for the details of the changes in level 1 products.
Among several changes in level 1 products, the important changes that affect level 2 products
substantially are the following points.
The DPR’s system parameters were re-examined. Based on the new calibration results, the offset
parameters for the transmitting powers, receiver’s gains, the beam widths, and the pulse width ofboth KuPR and KaPR are redefined. As a result, Zm of KuPR has increased by about +1.3 dB,
and Zm of KaPR by about +1.2 dB. The radar surface cross section (sigma0) of KuPR has
increased by about +1.2 dB and that of KaPR by about +1.1 dB, although the changes in sigma0
depend slightly on the incidence angle due to the changes in the beam widths. Because of theintroduction of the adjustment factors in L2 (see below) whose magnitudes vary with time, thenumbers mentioned above are not fixed numbers but change with time, especially near the
beginning of the GPM mission. In fact, for example, statistics show that an average (over angle)
increase in sigma0 at Ku-band is about +1.0 dB, that at KaMS is about +0.6 dB and that at KaHS
is about +0.9 dB on the first 5 days in June 2014.
•

The FCIF-LUT for the KuPR near the saturation was improved so that the effect ofsaturation near the saturation level was mitigated.

Changes in level 2 algorithm•

In addition to the changes in the DPR L1 calibration, adjustment factors are introduced toremove small trends in the overall system gains in KuPR and KaPR. The adjustment factors
change the measured received powers only by a small fraction of dB.

•

Since the FCIF-LUT for the KuPR near the saturation level was modified to mitigate the effectof saturation, the statistics of sigma0 near the saturation level in the KuPR has changed. Thischange affects the SRT performance as well. The side-lobe echo cancellation parameters areadjusted to cope with this change as well•

Measured radar reflectivity factor Zm and surface radar cross section sigma0 are calculated
based on the new values of the pulse widths of both KuPR and KaPR. Accordingly, the angle
bin dependence of sigma0 has changed slightly.

•

A DSD database that depends on the month, region, surface type and rain type was created29

from the statistics of DSD parameters estimated with the dual-frequency algorithm. The RDm relationship in the DSD database is used as the default R-Dm relationship in the single
frequency (Ku-only and Ka-only) data processing before it is modified by other constraints
such as the path-integrated attenuation. The introduction of the DSD database has modified
the precipitation estimates substantially when they are light (less than about 3mm/h) in manycases. Rain estimates from the Ku-only and dual-frequency algorithms now agree very well.
•

New flags are introduced. They are snowIceCover in the preparation module,
flagHeavyIcePrecip

andflagAnvil

intheclassification

module,andflagSurfaceSnowfall and surfaceSnowfallIndex in the experimental module.

The meanings of these flags should be referred to the user’s manual.
•

Winter convective storms that give large DFRm (measured Dual-Frequency Ratio) at thestorm top are flagged and the corresponding pixels are classified as convective in V5. This
category only appears in the inner swath since DFRm is available only there.

30

Release Notes for TRMM/GPM SLH V07
December 2021
Revised in May 2022
For the GPM SLH V07, the LUTs for great mountain ranges in the tropicalprecipitation regime (rainTypeSLH>200) is newly developed. The areas of the great
mountain ranges in the tropical precipitation regime are shown in Figure 1. The
separation among the tropics, the great mountain ranges in the tropical precipitation
regime, and midlatitudes should be done referring to the rainTypeSLH values stored in
the orbital data, and described in Table 1. Note only latent heating is retrieved for great
mountain ranges in the tropical precipitation regime in the GPM SLH V07. TRMM SLH
V07 algorithm is the same as the GPM SLH V07 algorithm.
The LUT for mid and higher latitudes was newly developed in the GPM SLH V05. In
the TRMM/GPM SLH V06A, the same LUT for mid and higher latitudes is applied and
LUT for tropics is the same as TRMM SLH V7A. Some recommendations to users oforbital data are listed below, for TRMM/GPM SLH V06A retrieved as tropicalprecipitation or those as mid latitude precipitation.
Although the SLH algorithm and Tables are the same as GPM SLH V05 for midlatitude and TRMM SLH V7A for tropics, respectively, because of the change in input
PR/KuPR Level 2 data (2APR/2AKu), TRMM/GPM SLH V06A products differ from
TRMM SLH V7A and GPM SLH V05 products, respectively.

Figure 1 Distribution of the areas (6: Highland) which the LUTs for great mountain ranges
in the tropical precipitation regime are applied with the areas (5: Tropics over Landand 10: Tropics over Ocean) where the LUTs for tropics are applied and the areas (4:Extratropics over Land and 8: Extratropics over Ocean) where the LUTs for mid
and higher latitudes are applied for July (left) and January (right).

31

Table 1. Description for rainTypeSLH(a) Tropics andsubtropics(b) Mid and higher latitudes(c) Great mountain ranges in the tropicalprecipitation regime

0: No precipitation

100: No precipitation

200: No precipitation

11: Convective

111: Convective

211: Convective over high-elevation areas
212: Convective over low-elevation areas

21: Shallow stratiform

121: Shallow stratiform

221: Shallow stratiform over high-elevation areas
222: Shallow stratiform over low-elevation areas

31: Deep stratiform
32: Deep stratiform, DI(Intermediary)131: Deep stratiform, DD, Pmax aloft
132: Deep stratiform, DD, Pmax NS
133: Deep stratiform, DI, Pmax aloft
134: Deep stratiform, DI, Pmax NS
135: Deep stratiform, subzero, Pmax aloft
136: Deep stratiform, subzero, Pmax NS

231: Deep stratiform, DD, Pmax aloft
232: Deep stratiform, DD, Pmax NS
233: Deep stratiform, DI, Pmax aloft
234: Deep stratiform, DI, Pmax NS
235: Deep stratiform, subzero, Pmax aloft
236: Deep stratiform, subzero, Pmax NS

61: Other, Applying
table forrainTypeSLH=21

160: Other

261: Other, Applying table for rainTypeSLH=221
262: Other, Applying table for rainTypeSLH=222
263: Other, Applying table for rainTypeSLH=231
264: Other, Applying table for rainTypeSLH=232
265: Other, Applying table for rainTypeSLH=233
266: Other, Applying table for rainTypeSLH=234
267: Other, Applying table for rainTypeSLH=235
268: Other, Applying table for rainTypeSLH=236

Mask
900: Areas with low melting levels including some mountains except for of great mountain ranges in thetropical precipitation regime
910: Suspicious extreme
920: No precipitation in SLH (precipRate < 0.2 mm/h (0.3 mm/h for tropics) or precipitation depth < 500m) but
precipitation exists in 2Aku
DI: Downward Increasing,

DD: Downward Decreasing,

NS: Near Surface

It was found that the change of the input KuPR level 2 data from V05 to V06 increased
the bias between the KuPR near-surface precipitation and vertically integrated latentheating of SLH V06A. Moreover, some unnatural heating profiles were found associatedwith precipitation in tropical cyclones. To fix these problems, we revise the algorithm
and release the SLH V06B product. Note that the SLH V06B product is available for
GPM era since February 2014.
(i) No precipitation or Masked out pixels (rainTypeSLH=0, 100, 200, 300, 900, 910, or
920)
SLH values are not estimated (missing) for raintypeSLH=900 or 910.
SLH values are not estimated (0) for raintypeSLH=0, 100, 200, 300, 920.
(ii) Release note for tropical algorithm (0≦rainTypeSLH <100)
Analysis showed consistency among GPM SLH V04, V05, V06 and TRMM SLH V7A,
V07 estimates over the coverage of TRMM/PR during a GPM and TRMM overlapping32

observation period (April-June 2014). Note that:
0. Vertical levels are 80 levels.
1.

In the V06B SLH, there was a shallow stratiform type (raintypeSLH=21). In V07
SLH this type is no longer used. Heating in all shallow pixels with their precipitation
top heights (threshold 0.3mm/hr) lower than the melting level is estimated asconvective type (raintypeSLH=11).

2.

Differences of sampling between TRMM/PR and GPM/KuPR affect SLH estimates.
The greater global coverage of the GPM Core Observatory (65°N/S) compared to the
TRMM coverage (35°N/S) decreases sampling of GPM/DPR over the coverage of
TRMM/PR, especially at around the satellite inclination latitudes of 35°N/S,
affecting SLH estimates there.

3.

Retrieval for tropical cyclones and high mountains/winter mid-latitudes pixels will
be developed.

4.

For tropical latent heating provided in 80 levels, users are recommended to smooththe profile vertical for a few levels to avoid the spurious peak at around 0degC level.

5.

The tropical algorithm is sometimes applied at high latitudes in summer. In suchcases, pixels to which the tropical algorithm are applied and those to which the midlatitude algorithm are applied are distributed disorderly. This problem will be fixedin future version.

(iii) Release Note for Mid-latitude algorithm (100≦rainTypeSLH<200)
A. In look up table ranges where sampling numbers did not satisfy the criteria, values
are discarded or extrapolated from nearest neighbor bins, depending on the
precipitation type. Sampling number criterion is basically 30, but 60 is chosen fordeep stratiform LUT with precipitation maximum at the near surface level.
Corresponding range for the convective LUT is PTH>11km. Note that in case that alatent heating profile at the candidate bin used for extrapolation has cooling aroundthe middle (or 0.35-0.45) of relative altitudes, for deep stratiform LUT with
precipitation maximum at the near surface level, values at the bin are not used forextrapolation and those at its next bin are utilized.
B. Recommendation for horizontal averaging at the utilization of pixel products
calculated with the mid-latitude algorithm for SLP or SLG.

33

B1. Eddy flux convergence in Q1R and Q2 are estimated assuming that the size of“large-scale grids” is 100kmx100km. Therefore, it is recommended to average
horizontally in this spatial scale to utilize Q1R
or Q2.
B2. Horizontal averaging of about 50km x 50km,
or 100 pixels with GPM DPR sampling, isrecommended, in order to reduce root meansquare errors (RMSE) calculated between
estimated

LH

from

LFM-simulated

precipitation, to less than a half of the mean

NOTE: divided by LH(x=2)

value at the LH peak height of ~5.5km (forCase 1).
C. CorrectionFactorMidlatType is introduced in the SLH V06B algorithm to consider LH
associated with small hydrometeors condensed outside of the precipitating area,transported some distance into the precipitating area, and precipitate. However, theapplication of this factor in the L2 product is inconsistent in terms of pixel-by-pixel
estimation. In the SLH V07 algorithm, this factor is not applied to SLP and SLG
products

but

appliedtoSLM

products.

The

specific

value

of

the

correctionFactorMidlatType is stored in the AlgorithmRuntimeInfo in the L2 productand correctionFactorMidlatType in the L3 product.

(v) Release Note for L3 (gridded; SLG and Monthly; SLM) productFrom the TRMM/GPM SLH V06A product, we added the unconditional variables(UnCnd) for all rain type, and modified their variable names to include conditionalvariables (Cnd). Please refer to the ATBD.
[Note about the missing value for conditional mean]
Note that there are two reasons for missing values for conditional mean (LHCndMean,
Q1RCndMean, Q2CndMean), which can be discriminated by 'allPix' values as follows.
1. conditional mean is not defined because there is no precipitation in the grid(precipPix=0), when allPix≠0.
2. missing value is given because the grid value is masked out related to the
topography, when and allPix = 0.

34

[Note about CorrectionFactorMidlatType in SLM]
After the SLH V06B algorithm, heating (LH, Q1R, Q2) has been corrected by theCorrectionFactorMidlatType=0.88 in the area where the midlatitude algorithm isapplied. In V07, because this factor is changed to be applied to only the SLM product,
but not to the SLP product, the monthly mean of the L2 heating divided by the factor
corresponds to the SLM product in the midlatitude area.

35

December 1, 2021

Release note for GPM Global Rainfall Map (GPM-GSMaP)The GPM Global Rainfall Map (GPM-GSMaP) Level 3 product version 05A (Algorithm version 8) was released tothe public since December 1, 2021.
Update from version 04 (Algorithm version 7) to version 05A (Algorithm version 8) are following.
A. Passive microwave (PMW) algorithm
1.

Improvement of PMW retrieval technique­

Adding MHS (Metop-C) and ATMS (Suomi-NPP/ATMS, NOAA-20/ATMS)­

Update L1 data (TMI, AMSR-E) and decoding tool (AAPP)­

Retrievals extended to the pole-to-pole (PMW only)­

Application of dynamic land/ocean classification for all PMW sensors­

PMW retrieval algorithm considering frozen precipitation depth and static stability in low-leveltroposphere­

PMW retrieval for low temperature conditions using scattering signature­

Implementation of fake precipitation screening due to sea ice, snow etc. over Greenland and Hudson
Bay­

Improvement of snowfall retrievals­

Improvement of MWS algorithms (Precipitation detection for coastal region etc.)­

Improvement of rainfall detection method over ocean (implementation of correction method forbrightness temperature for rain-free areas)

2.

Improvement of heavy orographic rainfall retrievals considering static stability in low-level troposphere3.

Update of Database­

Precipitation profile database (5-year DPR(MS) 06A, algorithm update)­

Precipitation/no-precipitation classification (5-year KuPR & GMI combined)­

Surface emissivity (adding TELSEM database outside of PR observation)­

Change AUTOSNOW format

B. Implementation of Normalization module for PMW retrievals
C. PMW-IR Combined algorithm
1.

Improvement of MVK algorithm

2.

Implementation of histogram matching method

D. Gauge-adjustment algorithm
1.

Improvement of the gauge-calibrated method2.

Mitigation of artificial patterns appeared in V04

E. Data format
Adding new variables

36

3GSMAPH: reliability flag, surface type, and orographic rain flag
3GSMAPM: orographic rain ratio
F. Others
Minor bug fix
For details, following URLs can be helpful for your reference.
http://www.eorc.jaxa.jp/GPM/doc/product_info/release_note_gsmapv05-v8_en.pdf(For the Japanese)
http://www.eorc.jaxa.jp/GPM/doc/product_info/release_note_gsmapv05-v8_ja.pdf
Followings are remarks and known bugs in current version of GPM-GSMaP product to be fixed in future versions.
Remaining problems
A. Retrieval issues
1. The PMW algorithm introduced in the V05 product overestimates the amount of precipitation for heavy
precipitation such as typhoons and convective precipitation, and also confirms that weak precipitation is
generally difficult to be detected in MWS. Misidentifying snow or sea ice as precipitation signals has been
issued in the V04 product, but this issue has not been fully solved in the V05 product; the extension to the polar
regions and the introduction of precipitation and snowfall estimation in low temperature regions make false
strong precipitation more apparent, especially in the winter hemisphere. Users should be cautious of estimations
over the cold surface (in particular, below 273.2 K).
2. The use of static stability in low-level troposphere as a condition for determining topographic precipitation has
made it possible to identify orographic heavy precipitation in inland areas and areas where convective
precipitation is dominant. However, overestimation and false-positive of orographic rainfall remain. We
examine to resolve this issue.
3. The MMN module introduced in the V05 product largely affects the accuracy after precipitation correction due
to the amount and bias of sampling of each sensor. For example, if there is a missing period and the number ofmissed rainfall cases is large, the average rainfall is likely to deviate from the true value, resulting in an incorrect
value. In addition, the MMN algorithm is not able to change no rainfall to rainfall presence. Improvements inrain/no-rain classification method and the brightness temperature correction method are required.
4. The precipitation estimation of gauge-calibrated hourly rainfall product (GSMaP_Gauge) depends on a largepart on the Climate Prediction Center (CPC) Unified Gauge-Based Analysis of Global Daily Precipitation data
sets provided by NOAA. If the CPC data sets have good estimation of precipitation in a region, the
GSMaP_Gauge data sets also will show good scores in the region. However, in case the CPC data sets underor overestimate the rain fall rate seriously or miss the rainfall event, the GSMaP_Gauge product also estimatesor misses the precipitation in a similar manner as the CPC data sets. Note that the CPC data sets and hence the
GSMaP_Gauge data do not always show accurate estimation particularly over less dense gauge region.

37

5. Although the GSMaP_Gauge_NRT is a near real time version of the GSMaP_Gauge, the products do not use
the gauge measurement directly. Since the global gauge measurement takes much time to collect and process
the data from all over the world, the gauge data is not available in near real time. Hence, in the
GSMaP_Gauge_NRT product, only the error parameters derived from the GSMaP_Gauge are used to adjust
the GSMaP_NRT estimation, which is named as the GSMaP_Gague_NRT. We would like to know evaluationand validation results of this product for improvement. We appreciate if you give us some feedback.
B. Calibration issues
6. Brightness temperatures used in rainfall retrievals of GCOM-W/AMSR2 and GPM-Core/GMI are biascorrected using parameters provided by JAXA. These parameters may be modified in future when calibrationof each Level 1B data is updated.

38

March 30, 2022.

Release note for GPM Global Rainfall Map (GPM-GSMaP)The GPM Global Rainfall Map (GPM-GSMaP) Level 3 product version 04A (Algorithm version 7) was released tothe public since January 17, 2017, V04B was released since March 2, 2017, V04C was released since March 27,
2017, V04D was released since May 9, 2017, V04E was released since October 11, 2017, V04F was released since
May 11, 2018, and V04G was released since May 15, 2019. However, because of some reasons, the GPM-GSMaP
Level 3 product version 04H was released to the public since March 30, 2022.
Updates from version 04G to version 04H
Changes of AUTOSNOW format (code update only (i.e. no difference in quality with 04G).

Updates from version 04F to version 04G


Fix in initialization bugs in the passive microwave radiometer algorithm.



Changes of the processing environments (OS, intel compiler versions).

Updates from version 04E to version 04F


Fix in the orographic/nonorographic rainfall classification scheme. Note that this will be effective
over the land in latitudes of 20S-60S.



Improvement in screening of surface snow over the land which may lead to abnormal precipitation.

Improvement of the gauge-calibrated method in the near-real-time productUpdates from version 04D to version 04E
Improvement in handling abnormal IR values in the GSMaP_MVK algorithm

Fix of a very minor issue of the GSMaP_GMI algorithm

Updates from version 04C to version 04D
Install of a table related to the Tb calibration of GMI L1 V05

Updates from version 04B to version 04C, connected with bug-fixing of “PrecipRateGC” in the following products.
All standard products in V04A

Standard products since March 1, 2017 in V04B.

Updates from version 04A to version 04B are following.


Adding a missing value in “snowProbability” of the GSMaP Hourly (3GSMAPH).



Bug-fixing in “snowProbability” of the GSMaP Monthly (3GSMAPM).



Bug-fixing in “satelliteInfoFlag”.

39

Update from version 03 (Algorithm version 6) to version 04A (Algorithm version 7) are following.
1) Improvement of the GSMaP algorithm using GPM/DPR observations as its database2) Implementation of a snowfall estimation method in the GMI & SSMIS data and a screening method using
NOAA multisensor snow/ice cover maps in all sensors3) Improvement of the gauge-correction method in both near-real-time and standard products4) Improvement of the orographic rain correction method
5) Improvement of a weak rain detection method over the ocean by considering cloud liquid water
For details, following URLs can be helpful for your reference.
http://www.eorc.jaxa.jp/GPM/doc/product_info/release_note_gsmapv04-v7_en.pdf(For the Japanese)
http://www.eorc.jaxa.jp/GPM/doc/product_info/release_note_gsmapv04-v7_ja.pdf
Followings are remarks and known bugs in current version of GPM-GSMaP product to be fixed in future versions.
Remaining problems
A. Retrieval issues
1. The snowfall estimation method for the GMI & SSMIS data was installed in the V04 product, but it still needs
to be validated and improved further. In addition, several biases and/or gaps may be appeared in the mid-latitude
ocean, due to changes of the estimation method. In addition, sometimes, surface snow or sea ice may be
misidentified as precipitation signal, especially in spring season. Users should be cautious of estimations overthe cold surface (in particular, below 273.2 K).
2. The orographic/non-orographic rainfall classification scheme has been implemented in the GSMaP algorithm
for passive microwave radiometers (Yamamoto and Shige, 2014). The scheme is switched off for regions (e.g.
the Sierra Madre Mountains in the United States and Mexico) where strong lightning activity occurs in therainfall type database because deep convective systems for the regions are detected from the scheme involvedin the orographic rain condition. The scheme improves rainfall estimation over the entire Asian region,
particularly over the Asian region dominating shallow orographic rainfall. However, overestimation and falsepositive of orographic rainfall remain. This is because the orographic rainfall conditions have moderate
thresholds for global application. We examine to resolve their problems.
3. The precipitation estimation of gauge-calibrated hourly rainfall product (GSMaP_Gauge) depends on a largepart on the Climate Prediction Center (CPC) Unified Gauge-Based Analysis of Global Daily Precipitation data
sets provided by NOAA. If the CPC data sets have good estimation of precipitation in a region, the
GSMaP_Gauge data sets also will show good scores in the region. However, in case the CPC data sets underor overestimate the rain fall rate seriously or miss the rainfall event, the GSMaP_Gauge product also estimatesor misses the precipitation in a similar manner as the CPC data sets. Note that the CPC data sets and hence the40

GSMaP_Gauge data do not always show accurate estimation particularly over less dense gauge region.
4. Although the GSMaP_Gauge_NRT is a near real time version of the GSMaP_Gauge, the products does not use
the gauge measurement directly. Since the global gauge measurement takes much time to collect and process
the data from all over the world, the gauge data is not available in near real time. Hence, in the
GSMaP_Gauge_NRT product, only the error parameters derived from the GSMaP_Gauge are used to adjust
the GSMaP_NRT estimation, which is named as the GSMaP_Gague_NRT. We would like to know evaluationand validation results of this product for improvement. We appreciate if you give us some feedback.
B. Calibration issues
5. Brightness temperatures used in rainfall retrievals of GCOM-W/AMSR2 and GPM-Core/GMI are biascorrected using parameters provided by JAXA. These parameters may be modified in future when calibrationof each Level 1B data is updated.
6. Scan errors may be occasionally found in rainfall retrievals of SSMIS (microwave imager/sounder) on board
the DMSP-F16, DMSP-F17 and DMSP-F18 satellites. This problem will be corrected in the future version of
L1c data.
7. MHS data used in the GSMaP product was changed form Level 1B to Level 1C. The Scattering Index (SI) in
the AMSU-A/MHS algorithm is changed at altitude higher than 40 degrees. However, we have not yet fully
evaluated the effect. We would like to know evaluation and validation results of the GSMaP AMSU-A/MHS
rainfall retrievals. We appreciate if you give us some feedback.

41

April 12, 2022

Caveats for the COmbined Radar-Radiometer Algorithm (CORRA) Level
2 Products in the GPM/TRMM V07 Public Releases
The GPM COmbined Radar-Radiometer Algorithm (CORRA) L2 V07 product includes
precipitation estimates over the full, 245 km Ku radar swath (Ku+GMI inputs, as wellas Ku+Ka+GMI inputs after May 21, 2018), and estimates over the narrower, 125 km
inner swath (Ku+Ka+GMI inputs before May 21, 2018). The inputs for the GPM
CORRA L2 algorithm are derived from DPR L2 and GMI L1C products. In particular,
the CORRA L2 algorithm draws upon inputs from the DPR L2 Preparation Module,
Classification Module, Surface Reference Technique Module, and the Vertical
Structure Module. From GMI L1C, the GPM L2 algorithm inputs the intercalibrated
brightness temperature observations. CORRA has also been extended to produce
TRMM precipitation products from a combination of precipitation radar (PR) andTRMM microwave imager (TMI) data, using essentially the same estimation methodthat is applied to GPM. In this case, estimates based on only the Ku+TMI inputs over
the Ku radar swath are produced, since no Ka band radar data are available fromTRMM.
During the early GPM mission (prior to June 2014) many tests and modifications of
the DPR performance were carried out, and these had an impact on not only DPR
products but also the GPM CORRA L2 estimates that depend on them. Therefore, GPM
CORRA L2 precipitation estimates from the early mission should be used with caution.
A listing of the orbits impacted by these tests and modifications can be obtained fromthe GPM Radar Team.
Mainlobe and sidelobe clutter contamination of DPR reflectivities is reduced
using radar beam reshaping and statistical corrections. The combination ofthese applications reduces clutter successfully over most surfaces, but there are still“exceptional” regions where clutter signatures are still evident. Also, ice-covered
land surfaces produce Ku-band radar surface cross-sections at nadir view thatsometime exceed the upper limit of the radar receiver range. Estimates of Ku-band
path-integrated attenuation from the Surface Reference Technique Module arepossibly biased in these regions. Since radar reflectivities and path-integrated
attenuations are utilized by the CORRA L2 algorithm, precipitation estimates in these“exceptional” regions should be used with caution.
The current GPM CORRA L2 algorithm uses the Ku-band radar reflectivities from the
Preparation Module to detect either liquid- or ice-phase precipitation. The lowest
detectable reflectivity for DPR at Ku band is ~13 dBZ, and so light snow or very lightrainfall may not be detected and quantified by the algorithm. The TRMM PR radar
has a minimum detectable signal of ~18 dBZ, and so even more light snow and rainfall

42

may not be quantified by TRMM CORRA.
In addition to the impact of input data from DPR L2, there are uncertainties due to thecurrent limitations of the CORRA L2 algorithm’s physical models and otherassumptions that can also have an impact on precipitation estimates. In particular,
the physical models for scattering by ice-phase precipitation particles now feature
realistic nonspherical particle geometries, but these particle models are still
undergoing development. The scattering models for ice- and mixed-phase
precipitation will likely be improved in future product releases. Also, the effects ofradar footprint non-uniform beamfilling and multiple scattering of transmitted
power are addressed in CORRA L2, but the strategies that have been implemented tohandle these effects are not yet generalized and have not been analyzed in detail.
Multiple scattering primarily affects Ka-band reflectivities, and it sometimes
eliminates Earth surface reflection in regions of strong radar attenuation (althoughattenuation can sometimes eliminate the surface signal even if multiple scattering
effects are small). Footprint non-uniform beamfilling impacts the interpretation ofboth Ku- and Ka-band radar data. As a consequence, CORRA L2 precipitation
estimates associated with intense convection, in particular, will have greater
uncertainties. Finally, the assumed a priori statistics of precipitation particle size
distributions can have an influence on estimated precipitation. As particle size
distribution data are collected during the mission, more appropriate assumptions
regarding the a priori statistics of particle sizes will be specified in the algorithm. At
this stage of the mission, however, relatively simple assumptions regarding particle
size distributions have been introduced into the algorithm, and so biases in estimated
precipitation rates and the associated particle size distributions can occur. The
correct diagnosis of particle sizes in CORRA L2 estimates may require more general
constraints on particle size distribution parameters, and these constraints are thesubject of ongoing studies.
It should also be noted that both precipitation estimates and retrievals ofenvironmental parameters from CORRA L2 have not yet been comprehensively
validated using ground observations. Such a validation effort is under way and willcontinue to expand after the GPM/TRMM V07 release of products. Therefore, it isvery important that users of these public release products keep in contact with the
Combined Algorithm Team for updates on the validation of precipitation estimatesand any reprocessing’s of CORRA L2 products.
Preliminary validation of the GPM CORRA L2 V07 product has revealed goodconsistency between estimated surface precipitation rate and raingauge-calibrated
radar. At footprint scale, matches of the GPM CORRA V07 with raingauge-calibrated
radar (Multi-Radar Multi-Sensor [MRMS] product) over the continental US/lower
Canada and coastal waters during the June – December 2018 period (post HS scan
shift) yielded total low biases of 9% for the Ku+Ka+GMI product and 5% for the
Ku+GMI product, with correlations to MRMS of about 0.76 for both products.
Considering that the lack of DPR sensitivity contributes a low bias of approximately
7% to the total bias, these are respectable results.

43

Zonal mean precipitation rates agree well with zonal mean precipitation rates fromthe Global Precipitation Climatology Project (GPCP V3.2) product within the 40oS to
40oN latitude band over ocean backgrounds. Estimated zonal means at higher
latitudes are underestimated relative to GPCP, due in part to the limited sensitivity of
the DPR radar to light snow and drizzle. Over the October 2018 – December 2019
period, in the 40oS and 40oN latitude band, CORRA Ku+Ka+GMI L2 estimates overocean are low-biased by 4% relative to GPCP, while Ku+GMI estimates over ocean are
low-biased by 1%. For the same time period and latitude band, Ku+Ka+GMI and
Ku+GMI CORRA L2 estimates over land are currently low biased by 13% and 9%,
respectively, with the greatest biases occurring in warm, moist tropical andsubtropical regimes.
Note that relative to V06, V07 CORRA Ku+Ka+GMI estimates in the 40oS and 40oN
band have decreased by 2% over ocean, while Ku+GMI estimates have increased by
7%. Over land in the same latitudinal band, both Ku+Ka+GMI and Ku+GMI estimateshave increased by 9% relative to V06. These changes are effected by nudging initial
guess drop-size parameters closer to values established from climatologicalrelationships, which leads to greater proportions of smaller drops and higher rain
rates at the lighter end of the estimated rain spectrum, particularly over land regions,where radar-derived, path-integrated attenuations and microwave brightness
temperatures do not supply additional drop-size information with great certainty.
The changes are justified by the greater fidelity of mean CORRA estimates withraingauge-calibrated ground radar estimates over the continental US, better
agreement of CORRA with GPCP V3.2 overall, and greater consistency of drop-size
distribution parameter relationships relative to climatology.
The TRMM CORRA V07 algorithm precipitation rate estimates compare well with the
GPM CORRA V07. Based upon a limited sample of crossover data derived from theApril – September, 2014 GPM-TRMM overlap period, there is an overall high bias of
TRMM estimates relative to GPM of approximately 6% based upon matched GPM and
TRMM footprints, with a correlation of 0.73 between GPM and TRMM.
Mitigation of the low biases in CORRA L2 estimates, particularly over land, is apriority of the algorithm developers and is an active area of PMM research. A
radiometer-only precipitation estimation method designed to quantify precipitation
rates below DPR detection limits is currently under development and will be
introduced in V08 production. In addition, an experimental product for statisticallyestimating precipitation at the Earth’s surface within the DPR ground clutter, basedon retrieved values of precipitation above the clutter (near-surface estimates), havebeen produced and output in CORRA V07. Although preliminary, these surface
estimates are substantially increased relative to near-surface estimates, particularly
in ocean regions where climatologically, precipitation tends to increase from levels
above the clutter toward the surface. It should be emphasized that these surface
estimates are currently experimental, and more work will be required to stabilize andrefine these estimates. Users are therefore recommended to still use near-surface

44

precipitation estimates until a more rigorous and meteorologically specific clutter
correction method is implemented.
There could potentially be significant changes in the CORRA L2 rain rate products inthe transition from GPM V07 to V08 due to modifications and improvements of the
CORRA algorithm. Since the GPM and TRMM algorithms share the same software“core”, future changes in CORRA should apply to both GPM and TRMM. Again, usersof the GPM/TRMM V07 public release products should keep in contact with the
Combined Algorithm Team for information regarding these changes.

45

GPM/TRMM CORRA L2 V06 to V07 Changes
Several modifications have been made to the GPM CORRA L2 algorithm in thetransition from V06 to V07. These changes can be categorized as changes in algorithm
function and changes in output parameters and format. Regarding algorithm function,
the basic algorithm mechanics (i.e., estimation methodology) has not changed, andthe same mechanics are applied to both the GPM and TRMM data. The GPM
estimation method filters ensembles of DPR Ku reflectivity-consistent precipitation
profiles using the DPR Ka reflectivities, path integrated attenuations and attenuatedsurface radar cross-sections at Ku and Ka bands, and GMI brightness temperatures.
The filtered profile ensembles are consistent with all of the observations and theiruncertainties, and the mean of the filtered ensemble gives the best estimate of the
precipitation profile. The TRMM CORRA algorithm filters PR Ku reflectivity-based
precipitation profile estimates using path integrated attenuations and attenuatedsurface radar cross-sections at Ku band, as well as TMI brightness temperatures.
Probably the most impactful change in CORRA V07 concerns the way in which theprecipitation drop-size distribution a priori assumptions are applied. In both V06 andV07, the precipitation drop-size distribution is assumed to follow a normalized
gamma distribution, and the intercept, Nw, and mass-weighted mean diameter, Dm,
are adjusted in CORRA during the estimation process. Initial guess values of Nw are
drawn from a lognormal distribution and combined with the Ku reflectivity data toderive a Dm profile using a modified Hitschfeld-Bordan procedure. The Dm values arethen used to make a second estimate of the Nw, using an empirical logNw – Dm curve.
Empirical logNw – Dm relationships were previously established by Dolan et al. (2018),
Bringi et al. (2021) and others, using disdrometer and microphysics probe
observations at various locations over the globe. The initial Nw are nudged toward
the empirical Nw, and the nudged values are used to again estimate a revised, initial
Dm profile using Hitschfeld-Bordan. This two-step initial guess procedure moves
initial Nw values higher for lower Dm (lighter precipitation) and lower for higher Dm(heavier precipitation).
In CORRA V06, the procedure just described was only applied to stratiform
precipitation, and there were somewhat different drop-size distribution treatmentsfor ocean and land regions. In CORRA V07, there is (a) no distinction between ocean
and land in the initial drop-size distribution adjustment, (b) the empirical curve
relating logNw and Dm for convective precipitation is shifted to slightly higher logNw
values relative to stratiform, and (c) the nudging of the initial guess Nw toward theempirical values is done more strongly. The upshot of these changes is that the
proportion of smaller drops in the initial guess generally increases for smaller Dm, andthis leads to increases in precipitation rates at the lighter end of the precipitation
spectrum. For heavier precipitation, drop-size distribution adjustments tend to be
more controlled by path integrated attenuations and brightness temperatures, if they
are reliable. Overall, however, substantial increases in estimated total precipitation
are realized in the mean, especially over land regions. Biases of CORRA V07 relative46

to ground validation radar and GPCP over land are reduced, and disparities of
Ku+Ka+GMI estimates and Ku+GMI estimates over ocean are lessened. In addition,
the ocean vs. land contrast of estimated drop-size distributions is less, and therelationships of estimated logNw and Dm are closer to empirical relationships.
Introduced in CORRA V07 is a clutter or “blind zone” correction of near-surface(above clutter) estimates to create experimental, estimated-surface precipitation
rates. The clutter zone of the DPR is roughly 0.7 km at nadir view, rising to over 2 km
at the swath edges. Climatological relationships between the near-surface estimatesand “surface” estimates (actually, at about 0.8 km above the Earth’s surface), were
established using nadir-view CORRA V07 precipitation rate profile estimates for
ocean/land and convective/stratiform classes. This work follows similar studies by
Hirose and Nakamura (2004), Liu and Zipser (2013), and Hirose et al. (2021), butuses CORRA V07 profiles to establish the vertical structure climatologies. The
climatological relationships are used to scale the near-surface precipitation rates tostatistically estimate surface precipitation rates when the surface level is within the
clutter. Over ocean, where cloud updraft speeds are relatively weak, climatologicalmean precipitation rate profiles tend to increase from radar bins just above theclutter toward the surface, and so precipitation rates are significantly increased overocean (8%) relative to near-surface values. Over land, climatological profiles tend toshow less variation with height, and smaller increases in surface estimates are
realized (3%). Since the climatological precipitation profiles were established based ona limited number of CORRA-retrieved profiles, this clutter correction is deemedexperimental in V07, and users are recommended to still use near-surface precipitation
estimates until a more rigorous and meteorologically specific clutter correction method
is implemented.
New output variable names and formats have also been introduced in CORRA V07. In
particular, the structure name for the Ku+Ka+GMI estimates are called KuKaGMI, andlikewise the structure name for the Ku+GMI estimates is now KuGMI. These replace
the NS and MS mode structure names of V06, respectively. Similarly, the structurename for the Ku+TMI estimates is now KuTMI. Also, a new structure called OptEst
has been included to hold variables derived from an optimal estimation method thatfits non-precipitating variables like temperature, humidity, cloud liquid water, andsurface emissivity parameters to the radiometer data. These output variables are
currently just placeholders, but they will be used in the future to establish
background conditions for radiometer-only estimates of precipitation in regions
where radar reflectivities are below the minimum detectable of the DPR. Finally,
selected variables that were previously estimated at only specified nodal bins of theradar profiles are now defined at full vertical resolution. These include the Nw and Dm
profiles, their initial guesses, as well as the profiles of total hydrometeor watercontents and fluxes and the liquid portions of those water contents and fluxes. Thesechanges will make it easier to extract and interpret the profiles from CORRA V07.

47

References:
Bringi, V., M. Grecu, A. Protat, M. Thurai, and C. Klepp, 2021: Measurements ofrainfall rate, drop size distribution, and variability at middle and higher
latitudes: application to the Combined DPR-GMI Algorithm Remote Sens., 13,2412; https://doi.org/10.3390/rs13122412 .
Dolan, B., B. Fuchs, S. A. Rutledge, E. A. Barnes, and E. J. Thompson, 2018: Primarymodes of global drop size distributions. J. Atmos. Sci., 75, 1453-1476.
Hirose, M., and K. Nakamura, 2004: Spatiotemporal variation of the verticalgradient of rainfall rate observed by the TRMM Precipitation Radar. J. Climate,17, 3378-3397.
Hirose, M., S. Shige, T. Kubota, F. A. Furuzawa, H. Minda, and H. Masunaga, 2021:
Refinement of surface precipitation estimates for the Dual-frequency
Precipitation Radar on the GPM Core observatory. J. Met. Soc. Japan (early online
release) DOI:10.2151/jmsj.2021-060.
Liu, C., and E. J. Zipser, 2013: Why does radar reflectivity tend to increase
downward toward the ocean surface, but decrease downward toward the land
surface? J. Geophys. Res.: Atmos., 118, 135-148, doi:10.1029/2012JD018134,
2013.

48

Release Notes for the CSH V05 Level 3 gridded product (3GCSH)
5 July 2017

Changes from V04:
Major changes from V04 include the retrieval of latent heating (LH) over the entire GPM domain(i.e., 67N to 67S), not just the TRMM domain (i.e., 37N to 37S). However, the other remaining
CSH products (i.e., eddy heating, microphysical and eddy moistening, and radiation) are still
retrieved only over the TRMM domain.
All products are now retrieved at 80 vertical levels every 250 meters AGL starting at the surface(i.e., 0, 250, 500, etc.).
The retrievals for the Tropics (i.e., TRMM domain) are based upon an updated version of theprevious CSH algorithm design (Tao et al. 2010). The algorithm still relies upon look-up-tables(LUTs) of model-simulated heating/moistening profiles generated from the Goddard Cumulus
Ensemble Model (or GCE), a CRM, which are stored and mapped to satellite grids according toprecipitation characteristics. In V04, the previous TRMM V7 CSH LUTs were used. Those
LUTs were designed for 0.5 x 0.5 degree TRMM grids (versus the 0.25 x 0.25 degree GPM
grids), so the GPM input data in V04 were pre-smoothed to accommodate the coarser
resolution of the LUTs. In V05, the LUTs are generated at the GPM grid resolution (0.25
degrees) and are based on 2D multi-week simulations for 6 ocean (vs 5) and 4 land (vs 2)
cases (see Table 1) using larger domains (512 vs 256 km) and an improved Goddard 4ICE
(Lang et al. 2014; Tao et al. 2016) microphysics scheme that includes hail as well as a rain
evaporation correction scheme (vs an improved Goddard 3ICE scheme). In addition to thesame rain intensity (36) and stratiform fraction bins (20), the LUTs are further differentiated by
two new metrics: mean echo top heights (5 bins: 0-2, 2-4, 4-6, 6-8, and above 8 km) and mean
low-level (0-2 km) dBZ gradient (increasing or decreasing towards the surface).
Outside the Tropics (i.e., poleward of 37N and 37S), the LH retrievals are based upon a newcold season/ higher latitude algorithm that maps LH profiles based upon 6 NU-WRF (NASAUnified Weather Research and Forecasting Model) simulations using the same improved 4ICE
scheme for 3 eastern US synoptic snow storms and 3 West Coast atmospheric river events.
The LUTs are constructed and mapped using the following domain average quantities: storm
top heights (6 bins), freezing level (13 bins), max dBZ level (6 bins), dBZ gradient (2 bins), andcomposite dBZ intensity (90 bins, every 1 dBZ). As with the Tropics, the radar quantities aremean conditional values over each 0.25 x 0.25 degree GPM grid. A radar (composite) coveragefactor is then used to scale the corresponding LUT conditional LH profile to obtain the GPM grid
average value.

49

Table 150

Caveats:
CSH retrievals are derived from Level 2 products from the Combined Radar-Radiometer
Algorithm (CMB). Users are encouraged to check related CMB documentation.
CSH retrievals in the tropical TRMM domain are based upon GCE model simulations that do notinclude terrain. At higher latitudes, the CSH LUTs are based upon NU-WRF simulations that doinclude terrain. However, areas with domain heights above 500 m were screened out in theconstruction of the LUTs. Therefore, CSH retrievals both in the Tropics and at higher latitudes
in areas with higher terrain should not be relied upon.

Sample Analyses:

Figure 1. 3GCSH (gridded orbital) V05 LH at 2 and 7 km for April 2014.

51

Figure 2. 3GCSH (gridded orbital) V05 zonal average total, land, and ocean LH for
April 2014.

References:Lang, S., W.-K. Tao, J.-D. Chern, D. Wu, and X. Li, 2014: Benefits of a 4th ice class in thesimulated radar reflectivities of convective systems using a bulk microphysics scheme. J. Atmos.
Sci., 71, 3583-3612. doi: http://dx.doi.org/10.1175/JAS-D-13-0330.1
Tao, W.-K., S. Lang, X. Zeng, S. Shige, and Y. Takayabu, 2010: Relating convective andstratiform rain to latent heating. J. Climate., 23, 1874-1893.
Tao, W.-K., D. Wu, S. Lang, J.-D. Chern, C. Peters-Lidard, A. Fridlind, and T. Matsui, 2016: Highresolution NU-WRF simulations of a deep convective-precipitation system during MC3E: Further
Improvements and Comparisons between Goddard microphysics schemes and observations. J.
Geophy. Rev. Atmos., 121, 1278-1305, doi:10.1002/2015JD023986.

52

Release Notes for the CSH V05 Level 3 gridded product (3HCSH)
5 July 2017

Changes from V04:
Major changes from V04 include the retrieval of latent heating (LH) over the entire GPM domain(i.e., 67N to 67S), not just the TRMM domain (i.e., 37N to 37S). However, the other remaining
CSH products (i.e., eddy heating, microphysical and eddy moistening, and radiation) are still
retrieved only over the TRMM domain.
All products are now retrieved at 80 vertical levels every 250 meters AGL starting at the surface(i.e., 0, 250, 500, etc.).
The retrievals for the Tropics (i.e., TRMM domain) are based upon an updated version of theprevious CSH algorithm design (Tao et al. 2010). The algorithm still relies upon look-up-tables(LUTs) of model-simulated heating/moistening profiles generated from the Goddard Cumulus
Ensemble Model (or GCE), a CRM, which are stored and mapped to satellite grids according toprecipitation characteristics. In V04, the previous TRMM V7 CSH LUTs were used. Those
LUTs were designed for 0.5 x 0.5 degree TRMM grids (versus the 0.25 x 0.25 degree GPM
grids), so the GPM input data in V04 were pre-smoothed to accommodate the coarser
resolution of the LUTs. In V05, the LUTs are generated at the GPM grid resolution (0.25
degrees) and are based on 2D multi-week simulations for 6 ocean (vs 5) and 4 land (vs 2)
cases (see Table 1) using larger domains (512 vs 256 km) and an improved Goddard 4ICE
(Lang et al. 2014; Tao et al. 2016) microphysics scheme that includes hail as well as a rain
evaporation correction scheme (vs an improved Goddard 3ICE scheme). In addition to thesame rain intensity (36) and stratiform fraction bins (20), the LUTs are further differentiated by
two new metrics: mean echo top heights (5 bins: 0-2, 2-4, 4-6, 6-8, and above 8 km) and mean
low-level (0-2 km) dBZ gradient (increasing or decreasing towards the surface).
Outside the Tropics (i.e., poleward of 37N and 37S), the LH retrievals are based upon a newcold season/ higher latitude algorithm that maps LH profiles based upon 6 NU-WRF (NASAUnified Weather Research and Forecasting Model) simulations using the same improved 4ICE
scheme for 3 eastern US synoptic snow storms and 3 West Coast atmospheric river events.
The LUTs are constructed and mapped using the following domain average quantities: storm
top heights (6 bins), freezing level (13 bins), max dBZ level (6 bins), dBZ gradient (2 bins), andcomposite dBZ intensity (90 bins, every 1 dBZ). As with the Tropics, the radar quantities aremean conditional values over each 0.25 x 0.25 degree GPM grid. A radar (composite) coveragefactor is then used to scale the corresponding LUT conditional LH profile to obtain the GPM grid
average value.

53

Table 154Caveats:
CSH retrievals are derived from Level 2 products from the Combined Radar-Radiometer
Algorithm (CMB). Users are encouraged to check related CMB documentation.
CSH retrievals in the tropical TRMM domain are based upon GCE model simulations that do notinclude terrain. At higher latitudes, the CSH LUTs are based upon NU-WRF simulations that doinclude terrain. However, areas with domain heights above 500 m were screened out in theconstruction of the LUTs. Therefore, CSH retrievals both in the Tropics and at higher latitudes
in areas with higher terrain should not be relied upon.

References:Lang, S., W.-K. Tao, J.-D. Chern, D. Wu, and X. Li, 2014: Benefits of a 4th ice class in thesimulated radar reflectivities of convective systems using a bulk microphysics scheme. J. Atmos.
Sci., 71, 3583-3612. doi: http://dx.doi.org/10.1175/JAS-D-13-0330.1
Tao, W.-K., S. Lang, X. Zeng, S. Shige, and Y. Takayabu, 2010: Relating convective andstratiform rain to latent heating. J. Climate., 23, 1874-1893.
Tao, W.-K., D. Wu, S. Lang, J.-D. Chern, C. Peters-Lidard, A. Fridlind, and T. Matsui, 2016: Highresolution NU-WRF simulations of a deep convective-precipitation system during MC3E: Further
Improvements and Comparisons between Goddard microphysics schemes and observations. J.
Geophy. Rev. Atmos., 121, 1278-1305, doi:10.1002/2015JD023986.

55