TRMM Available Products[As of November 15, 2022.]

Standard Products
Processing
Satellite / Instrument
Level

Product [Product
Identifier]

Key ParametersFilecoverage

Available Latest Product Version (Caveats)

TRMM orbit(Torbit*)Ver. 07(See: page 2)

TRMM/PR

PR L1B [PU1]

Received PowerTRMM/TMITMI L1B [TMI]Brightness Temperature (Tb)

TorbitVer. 07(See: page 3)

TRMM/TMITMI L1C [TMI]

Inter-Calibrated
Brightness Temperature (Tb)

TorbitVer. 07(See: page 3)

TRMM/VIRS

VIRS L1B [ V1B ]Radiance

TorbitVer. 07(To be prepared)

Reflectivities, 3D Precipitation

TorbitVer. 07(See: page 4)

Spectral latent heating

TorbitVer. 07(See: page 6)

Precipitation, Total Precipitable
Water

TorbitVer. 07(See: page 11)

PR-TMI retrieval 3D
Precipitation

TorbitVer. 07(See: page 14)

PR L3 Daily (TEXT)
Precipitation[P3D]

0.1°x 0.1°
Daily

Ver. 07(See: page 4)

PR L3 Daily(HDF5)
Precipitation[P3Q]

0.25° x 0.25°
Daily

Ver. 07(See: page 4)

0.25° x 0.25°
MonthlyVer. 07(See: page 4)

0.5°x 0.5°
GorbitVer. 07(See: page 6)

1

TRMM/PR

PR L2
[PU2]
SLH-PR L2[LHP]

2
TRMM/TMI/GPROF

TMI L2[TL2]

TRMM/PR-TMI/COMBPR-TMI Comb L2[TC2]

TRMM/PR

PR L3 Monthly[P3M]

Precipitation

SLH-PR L3
Spectral latent heating
Gridded orbit [LHG]
3

TRMM/TMI/GPROF

TRMM/PR-TMI/COMB

SLH-PR L3
Monthly [LHM]

Spectral latent heating

0.5°x 0.5°
MonthlyVer. 07(See: page 6)

TMI L3 Monthly[TL3]

Precipitation

0.25° x 0.25°
MonthlyVer. 07(See: page 11)

PR-TMI Comb L3[TC3]

Precipitation

0.25° x 0.25°
MonthlyVer. 07(See: page 14)

PR-TMI CSH L3[CSH]

Gridded Orbital Convective
Stratiform Heating

0.25° x 0.25°
GorbitVer. 07(See: page 14)

PR-TMI CSH L3[CSF]Monthly Convective Stratiform 0.25° x 0.25°
HeatingMonthlyVer. 07(See: page 14)* Torbit is the TRMM orbit calculated from the southern most point back to the southern most point TRMM Product is available from the below site.
https://gportal.jaxa.jp/gpr/

May 17, 2022

Release Notes for the PR Version 9/GPM PR Version 07ALevel 1 products＜Major changes in the PR Level1 products from Version 8 /V06A to Version 9/V07A＞

1. The format of V07A products has been changed from V05C (for example, adopting “FS”,
instead of “NS”) as described in the format description. The format of the TRMM/PR isconsistent of that of the GPM/DPR (in particular, the KuPR). Note that a new variable of“binMirrorImage” is set to missing value for the TRMM/PR.
Format description:
https://www.eorc.jaxa.jp/GPM/doc/product/format/en/02.GPM_DPR_L1_Product_Format_Doc
umentation_V7_E.pdf

2. Geolocation toolkit has been updated. The following variables are added and there is no change
in the existing values for the geolocation (e.g. longitude, latitude).

-navigation/scHeadingGround-navigation/scHeadingOrbital-

sunData Group-

sunLocalTime

3. Minor bugs have been fixed. “FractionalGranuleNumber” was corrected for the processing ofmonth boundaries. “SCorientation” was corrected for the orbits with no science data.

2

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;)3

May 7, 2022

Release Notes for the PR Version 9/GPM PR Version 07ALevel 2 and Level 3 products＜Major changes in the PR Level2 products from Version 8 /V06A to Version 9/V07A＞

1. The format of V07A products has been changed from V06A (for example, adopting “FS”, insteadof “NS”) as described in the ATBD. The format of the TRMM/PR is consistent of that of the
GPM/DPR (in particular, the KuPR).

2. PR data are processed by essentially the similar algorithm that is used to process the DPR/KuPR
data. However, algorithm differences in a scattering database appeared in V07A. In V06A, thescattering table of KuPR is used for PR by neglecting the difference of frequency (13.6 GHz for
KuPR and 13.8 GHz for PR). In V07A, we implemented the scattering table calculated for
13.8GHz in the TRMM/PR algorithm. Preliminary analyses showed a histogram of PR
precipitation estimates became closer to that of KuPR precipitation estimates.

3. There are several improvements associated with the DPR V07A algorithm developments. In V07A,
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 improve the detectability of
precipitation signals. This method uses signals not only in the vertical direction but also the incross-track and along-track directions. See the ATBD for full descriptions.

Caveat:1. PR’s rain estimates over land have significantly (about 15%) increased in V07A from V06A
because of considering a soil moisture effect. Please see Seto et al. (2022) for the soil moistureeffect. The PR estimates over ocean have decreased slightly.

Reference:
GPM/DPR Level-2 Algorithm Theoretical Basis Document, available fromhttps://arthurhou.pps.eosdis.nasa.gov/Documents/ATBD_DPR_V07A.pdf
https://arthurhou.pps.eosdis.nasa.gov/Documents/ATBD_DPR_L3_V7.pdf
Kanemaru, K., T. Iguchi, T. Masaki, and T. Kubota, 2020: Estimates of Spaceborne Precipitation
Radar Pulsewidth and Beamwidth Using Sea Surface Echo Data, IEEE Trans. Geosci. Remote Sens,
pp.1-13, https://doi.org/10.1109/TGRS.2019.2963090.

4

May 7, 2022
Kanemaru, K., H. Hanado, K. Nakagawa, 2021: Improvement of the clutter removal method for the
spaceborne precipitation radars, IGARSS2021, https://doi.org/10.1109/IGARSS47720.2021.9554974
Seto, S., T. Iguchi, and R. Meneghini, 2022: Correction of path integrated attenuation estimatesconsidering the soil moisture effect for the GPM Dual-frequency Precipitation Radar, J. Atmos.
Oceanic Technol., https://doi.org/10.1175/JTECH-D-21-0111.1

5

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).

6

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 overlapping7

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.

8

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.

9[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.

10

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.

11

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.
12

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.

13

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

14

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.

15

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

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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.

17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 relative18

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.

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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.

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