Spectral Latent Heating (SLH) for GPM
Algorithm Theoretical Basis Document (ATBD)

Algorithm Ver. 06B

Prepared by
GPM SLH Algorithm Development Team

October 2018
revised in December 2018minor-version-up to 06B in July 2020

ATBD for TRMM/GPM SLH

TABLE OF CONTENTS
1.

General Summary for Spectral Latent Heating (SLH) .............................................................1
1.1.

Retrieval for the Tropics ..................................................................................................2

1.2.

Retrieval for mid latitudes ...............................................................................................3

1.3.

Revision for the SLH V06B product ................................................................................8

1.3.1. Revision for the tropics .................................................................................................8
1.3.2. Revision for the mid-latitude.........................................................................................82.

I/O data description ................................................................................................................9
2.1.

Overview ........................................................................................................................9

2.2.

L2 product (LHP for TRMM and SLP for GPM)............................................................ 10

2.2.1.

Input data for LHP/SLP.......................................................................................... 10

2.2.2.

Output data for LHP/SLP ....................................................................................... 10

2.3.

2.3.1.

Input data for LHG/SLG ........................................................................................ 122.3.2.

Output data for LHG/SLG...................................................................................... 13

2.4.

3.

L3 products (LHG for TRMM and SLG for GPM)......................................................... 12

L3 products (LHM for TRMM and SLM for GPM) ....................................................... 15

2.4.1.

Input variables for LHM/SLM................................................................................ 15

2.4.2.

Output data for LHM/SLM .................................................................................... 16

REFERENCES .................................................................................................................... 18

ATBD for TRMM/GPM SLH

1.

General Summary for Spectral Latent Heating (SLH)
Convective latent heating plays essential roles for tropical convection, generating mesoscale

circulations (e.g. Houze et al. 1989). Moreover, vertical profiles of diabatic heating, in whichconvective latent heating contributes largely, affects the general circulation of the atmosphere (e.g.
Hartmann et al. 1984). For mid-latitude systems also, latent heating contributes to generations of PVs
resulting in, for example, explosive intensifications of mid-lattitude storms (Boettcher and Wernli,
2011).
Previously, with TRMM PR V7A, we developed SLH latent heating algorithm (Shige, Takayabu
et al. 2004, 2007, etc) for tropical and subtropical TRMM observation region (35N-35S). With GPM
DPR, the observation region extended to 65N-65S. Now we are to modify our latent heating retrieval
algorithm (SLH) to be applicable from the tropics to mid-latitudes.
For GPM SLH V05 algorithm, we retrieve latent heating variables utilizing two separate
algorithms for tropics and for mid-latitudes. First, location of each GPM KuPR pixel is assigned toeither tropics or mid-latitudes, depending on monthly maps of precipitation types determined in asimilar manner as described in Takayabu (2008). Then, we retrieve three dimensional convective
latent heating, Q1-QR (Q1R), and Q2, applying either tropical/mid-latitude algorithms toprecipitation data observed from GPM DPR (KuPR). Here, Q1 and Q2 are apparent heat source andapparent moisture sink, respectively, introduced by Yanai et al. (1973), and QR is radiative heatingof the atmosphere. In following subsections, we briefly describe the Spectral Latent Heating (SLH)
algorithms for tropics and for mid-latitude precipitation.

For TRMM V8A/ GPM V06A algorithm, the SLH algorithm and Tables are the same as GPM
SLH V5 for mid-latitude and TRMM SLH V7A for tropics. However, the input PR/KuPR Level 2
data was changed.
After the release of the SLH V06A product, we found an issue that the revision of its input data
KuPR level 2, which increased the stratiform precipitation, also increased a gap between near-surface
precipitation observed by the KuPR and vertically integrated latent heating retrieved by the SLH
algorithm. Besides, some unnatural heating profiles were found around tropical cyclones. To fix
these problems, we revised the algorithm to release the SLH V06B product. Details of the revision
are shown in section 1.3.

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1.1.

Retrieval for the Tropics

With the TRMM PR, we
TRMM Spectral Latent Heating (SLH) : Tropics

first developed the spectral

e.g. Shige, Takayabu et al. 2004 JAMC

latent heating (SLH) algorithm

TRMM PR2A25 orbital data
Rain Type
PTH
Melt-Lvl Precip.
Sfc Precip.

for the tropics as summarized
in Fig.1,

in afollowingmanner. First, utilizing the
Goddard Cumulus Ensemble
Model,

we

simulatedtheRain-LH tables
Convective

Rain Top Height
Deep Stratiform

GoddardCEM:
TOGA-COARE
simulation

Rain-LH tables
total

Estimate LH Data

Melting-l Precip Rate
ShallowStratiform

Strat

Conv

TOGA-COARE precipitation,from which we made three

Rain Top Heightspectral look-up tables (LUTs)

Fig.1: Summary of the SLH for tropics.

of latent heating profiles interms of precipitation top height (PTH) for convective and shallow stratiform rain, melting level
precipitation rate (MLP) for deep stratiform rain.

Utilizing TRMM PR precipitation profiles andthese tables, we estimated the latent heating associated with the precipitation. Please refer to Shige
et al. (2004, 2007, 2008, and 2009) for details.
Deep stratiform rain is now further divided into two new categories: deep stratiform withdecreasing precipitation from the melting level toward the surface and deep stratiform withincreasing precipitation from the melting level toward the surface. The SLH algorithm computes
deep stratiform cooling magnitudes as a function of the precipitation rate at the melting level (Pm) –the precipitation rate at near surface(Pnsfc), assuming the evaporative
cooling rate below the melting level
in

deep

stratiform

regions

Heating (color; TRMM SLH)
stratified against ω=dp/dt 500hPa

isascending

proportional to the reduction in the
precipitation

profiletoward⇔subsidence

thesurface from the melting level (basedon 1D water substance conservation).
However,increasing precipitation

profiles are found in some portions ofstratiform
regionsregions,
adjacent

especiallytoinconvective

regions where 1D water substanceconservation may be invalid. An LUT
for deep stratiform with increasing

Fig.2: SLH-estimated Q1-QR profiles in 30N-30S at all longitudes
over oceans, stratified with pressure velocity at 500hPa. Congestus
regime is found in the large-scale subsidence region with Q1R peakat around 2km.

precipitation toward the surface fromthe melting level (called “intermediate”) is produced with the amplitude determined by Pnsfc.
Utilizing these TRMM SLH, we found an existence of clear congestus regime with relatively highsea surface temperature with suppressed deep convection associated with large-scale atmospheric
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subsidence (Fig. 2). It was connected to solve the double ITCZ problem that climate model
developers have long been plagued (Hirota et al. 2011).
1.2.

Retrieval for mid latitudes

For mid-latitude systems, we employ the Japan Meteorological Agency (JMA)’s high resolution(horizontally 2km) local forecast model (LFM) to construct the LUTs. With collaborations of JMA’s
forecast group, 3hr and 4hr forecast data for 8 extratropical cyclone cases (16 shots) are collected
and utilized for LUTs.
In treating mid-latitude systems, in contrast to the tropical systems, we first faced a “cloud baseproblem”. In a tropical Mesoscale Convective System (MCS), we basically assume that freezing
level coincides with its cloud base level for its stratiform precipitation area. For mid latitudestratiform precipitation, however, this assumption does not hold.
LH is closely related to various phase changes
of water. Figure 3 shows the LH profiles
associated with various phase change processes.
Left panel is an example, where cloud base isbelow freezing level, melting and condensation
occur at similar levels, and they cancel eachother. While, when cloud base is above thefreezing level, evaporation and melting occur
together and result in a large cooling. So, we

Fig.3: Latent heating associated with cloud processes forreally need cloud base level information. But wecloud base level below the freezing level case (left) andonly observe radar reflectivity profiles forcloud base level above the freezing level (right).

precipitation with GPM DPR. Here, we tried toobtain the cloud base information from the precipitation profiles.
Since precipitation should evaporate out of the cloud base, we may expect that precipitation
maximum level (Pmax) can represent the cloud base. Figure 4 scatter the precipitation maximum
heights against cloud base heights for stratiform precipitation: Downward Decreasing (DD)precipitation from the 0degC level, Downward Increasing (DI) precipitation, and others.

Fig. 4: Scatter histograms between cloud base levels (Zcb) and maximum precipitation heights (Zpmax). For DD, DI
and other stratiform precipitation.

Finally, 6 tables, convective, shallow stratiform, 3 types of deep stratiform, and other were
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constructed for LH, Q1R, and Q2, respectively. Figure 5 shows the look up tables for LH. Convective
and shallow stratiform tables are made against the precipitation top heights. Ordinate is the altitudes,and color shades are LH profiles for each bin. For deep stratiform and other precipitation, LUT aremade against maximum precipitation (Pmax) chosen for the abscissa. In these LUTs, ordinate is thestandardized altitude with zero at the Pmax level, and +1.0 and -1.0 correspond to PTH and theprecipitation bottom height (PBH), respectively.

Fig.5: LUTs constructed from 8 extratropical cyclone cases simulated by JMA LFM. See text for details.

Utilizing the 6 LUTs and precipitation profile data from GPM 2AKu, LH is retrieved. Precipitation
type (convective/ stratiform/ other), PTH, PBH, Pmax, and Pnsfc are utilized for input.
In order to see the performance of the retrieval, we first performed a consistency check. We
compared LH retrieved from model precipitation and Model simulated LH along the red line of Fig.
6. Results are shown in Fig. 7. Left panels show the cross sections for precipitation, simulated LH
and retrieved LH from top to bottom. We can confirm that retrieved LH looks very similar tosimulated LH. Right panel show the averaged profiles for simulated LH in black and retrieved LH in
red curves. Examining each section separately (Fig. 8), we can confirm successful reconstruction of
LH by this algorithm.

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Fig. 6: LFM-simulated precipitation for extratropical cyclone case 1.

Fig. 7: Left: Precipitation, simulated LH, and SLH-retrieved LH using simulated precipitation, from top to bottom,
respectively. Right: Averaged (in x-dir) profiles of precipitation (blue), simulated LH (black) and retrieved LH (red).

Fig. 8: Same as Fig.5 but the profiles are averaged for separate parts of the system numbered 1-6.

Finally, GPM KuPR is utilized for the retrieval. The left panels show precipitation andprecipitation types from GPM KuPR. Note that there is no guarantee that the simulation
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corresponds well to the observation along a satellite orbit. But it seems to be a marvelous
simulation (Fig.9).

Fig. 9: Upper panels: GPM-observed precipitation and LFM-simulated precipitation at 18z 16Apr, 2016 (4hrF). Lower
panels: Precipitation types

Figure 10 compare simulated and retrieved LH along the GPM orbit. Left panels are LFMsimulated precipitation and LH, middle are LFM precipitation and retrieved LH from LFM
precipitation, Right panels show GPM-precipitation and retrieved LH. Precipitation simulation isvery good, so we can compare these cross sections. The retrieval looks quite successful.

Fig. 10: Simulated and (LFM- & GPM-) retrieved LH along the GPM orbit shown in Fig. 9.

Finally, average profiles of LH are compared for eight cases (Fig. 11). Red dashed are GPM
retrieved profiles and Black is LFM-simulated LH, for total, convective, stratiform, and others. They
are surprisingly consistent. Excellent correspondences are confirmed for cases with reasonableprecipitation coverages (cases 1, 3, 5, 6, 8) in the evaluation regions of GPM swaths.

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Fig. 11: Evaluations of GPM-retrieved LH for 8 cases. Red dotted curves represent retrieved GPM LH, and black
curves are for simulated LH. Cases with mid-lat precipitation coverage are inefficient for evaluations arecovered with gray shades.

We also confirmed a good continuities of LH distributions between tropics and midlatitudes inhorizontal maps (Fig. 12) as well as in zonal mean vertical structure.

Fig. 12: Horizontal distributions of LH derived with GPM SLH V5 algorithm.

To summarize, we developed a new Spectral LH algorithm for mid-latitude precipitation, utilizing
8 extratropical cyclone cases simulated with JMA LFM. To this end, cloud Base vs Freezing Level
relationship was an issue, for we utilize precipitation profiles observed from the GPM KuPR.
We found that precipitation maximum height can well represent the cloud base height. Six LUTs
were constructed to retrieve LH from GPM KuPR. This new algorithm performed successfully:
Consistency check and retrieval results were quite satisfactory.

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1.3.

Revision for the SLH V06B productTo fix the problems found in the SLH V06A product, we revised the GPM/SLH algorithm asfollows. Note that the1.3.1. Revision for the tropics
Unnatural heating profiles were found in shallow stratiform (rainTypeSLH=2) and deep stratiformwith downward-increasing precipitation (classified as intermediary, rainTypeSLH=5), especiallyaround tropical cyclones. We revised the algorithm for these pixels in a following manner. These
revisions contributed to reduce the gap between vertically integrated LH and near-surface
precipitation.
LH for the shallow convective pixels was estimated using the LUT for shallow stratiform pixels
in the V06A algorithm, which caused the unnatural LH profiles around the eye of tropical cyclones
where shallow strong convection occurs. In the new V06B algorithm, LH for the shallow convective
precipitation is retrieved using the LUT for convective precipitation. Note that since convection
characteristics associated with tropical cyclones may have different characteristics from those withtropical precipitation in general, currently we are developing a new SLH algorithm for tropical
cyclones.
In addition, LH for the intermediary pixels are changed to be retrieved with the deep stratiform
LUT but without cooling below the melting level in the V06B algorithm. This revision is based on
consideration that cooling below the melting level is unrealistic under the condition which produces
downward-increasing precipitation below the melting level.
1.3.2. Revision for the mid-latitude
The LUTs for mid-latitude regions were constructed with data collected at each grid individuallyfrom the LFM simulation with the precipitation intensity exceeding 0.2 mm h-1. Although small
frozen hydrometeors can fall as precipitation after being advected by large-scale flow associated withextratropical cyclones, the LUTs does not consider LH related to such advected small hydrometeors.
Therefore, vertically integrated LH is probably smaller than near-surface precipitation over GPM
observation areas.
In the SLH V06B algorithm, LH associated with generations of such small hydrometeors is estimatedby the LFM simulation output and is added to correct the LH retrieved with the LUT. The additional LH
is calculated in a following manner: At first, two types of sum of vertically integrated LH ,namely LHt and
LHp, are calculated for each snapshot of the simulations (8 cases, 16 snapshots). LHp is the sum ofvertically integrated LH at the precipitating grids in which the precipitation regime is midlatitude ocean,
whereas LHt is the sum of vertically integrated LH at all grids in which the precipitation regime is
midlatitude ocean. Then, the ratio between LHp and LHt, LHp/LHt, is calculated for each snapshot. The
average of the ratio from 16 snapshots turns out to be 0.88. Finally, the LH retrieved from the original SLH
algorithm is corrected by dividing by thevalue “0.88”.

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2.

I/O data description

2.1.

Overview

TRMM/GPM-SLH level-2 algorithm uses 2APR/2AKu product as input. Followings are summaryof PR and KuPR. L2 algorithm also uses a precipitation regime database as an auxiliary data.
TRMM/GPM-SLH level-3 algorithms use LHP/SLP product as input.
Table 1 Major specification of TRMM/PR and GPM/KuPR
Satellite

TRMM

GPM
Core Observatory

Height (km)350
(November 1997–
August 2001)
402(August 2001April 2015)

Instrument

frequency (GHz)

Data Period

PR

13.8

November 1997–
April 2015

407

KuPR

13.6

March 2014–
present

As for output data, we provide LHP/SLP, LHD/SLG, and LHM/SLM. Table 2 shows the brief
description for them.
Table 2. Description for GPM SLH products
Products

Level

LHP/SLP

L2

Descriptions
Basic orbital
SLH.
Gridded

LHG/SLG

orbital SLH.

TemporalHorizontal

Verticalresolution

resolution

resolution

-

5km

L3

averaged

daily

SLH

0.5-degree

averaged

format-80 layers*

HDF5
180°W tolatitude/longitude

180°E/,

grid.

67°S to 67°N.

Monthly
LHM/SLM

Outputpath

Daily
LHD/SLD

Coverageformatsmonthly

SLH* at the fixed height of 0.00-0.25 km, 0.25-0.50 km0.0-0.5km, 0.5-1.0km, ..., 19.50-19.75 km, and 19.75-20.00 km.

It should be noted that conditional mean values were added to L3 product from TRMM/GPM
Version 06A, and variable names for L3 product were changed from TRMM/V7A and GPM/V05A.
Detail is explained in following subsections.
[Note about the missing value conditions]
Note that there are two reasons for missing values for conditional mean(LHCndMean, Q1RCndMean,
Q2CndMean), which can be discriminated by 'allPix' values as follows.
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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.

2.2.

L2 product (LHP for TRMM and SLP for GPM)

2.2.1. Input data for LHP/SLP
LHP and SLP products are generated by using the following variables from 2APR/2AKu products as
input data:
scanStatus/dataQuality
scanTime
LatitudeLongitude
PRE/binClutterFreeBottom
PRE/binRealSurface
PRE/flagPrecip
PRE/landSurfaceType
PRE/localZenithAngle
VER/heightZeroDeg
CSF/typePrecip
SLV/binEchoBottom
SLV/precipRate
SLV/zFactorCorrected
2.2.2. Output data for LHP/SLP
2.2.2.1. Dimension Definition
⚫

⚫
⚫

nscan
➢
nray
➢
nlayer➢var Number of scans in the granule.
49 Number of angle bins in each scan
80 Number of layers at the fixed height of 0.00-0.25 km, 0.25-0.50 km, ...,
19.50-19.75 km, and 19.75-20.00 km.

2.2.2.2. Output variables
ScanTime/Year (2-byte integer, size: nscan)
4-digit year. Missing value is −9999.
ScanTime/Month (1-byte integer, size: nscan)Month of the year. Missing value is −9999.
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ScanTime/DayOfMonth (1-byte integer, size: nscan)Day of the month. Missing value is −9999.
ScanTime/Hour (1-byte integer, size: nscan)UTC hour of the day. Missing value is −9999.
ScanTime/Minute (1-byte integer, size: nscan)
Minute of the hour. Missing value is −9999.
ScanTime/Second (1-byte integer, size: nscan)
Second of the minute. Missing value is −9999.
ScanTime/MilliSecond (2-byte integer, size: nscan)Thousandths of the second. Missing value is −9999.
ScanTime/DayOfYear (2-byte integer, size: nscan)Day of the year. Missing value is −9999.
ScanTime/SecondOfDay (4-byte float, size: nscan)
Second of the day. Missing value is −9999.
Latitude (4-byte float, size: nray x nscan)
Latitude of the pixel center. Positive north, negative south. Missing value is −9999.9.
Longitude (4-byte float, size: nray x nscan)Longitude of the pixel center. Positive east, negative west. Missing value is −9999.9.
latentHeating (4-byte float, size: nlayer x nray x nscan)
Latent heating. Unit is [K/h]. Missing value is −9999.9.
Q1minusQR (4-byte float, size: nlayer x nray x nscan)
Apparent heat source minus radiative heating rate; Q1−QR. Unit is [K/h]. Missing value is −9999.9.
Q2 (4-byte float, size: nlayer x nray x nscan)
Apparent moisture sink Q2. Unit is [K/h]. Missing value is −9999.9.
rainTypeSLH (2-byte integer, size: nray x nscan)
Precipitation type decided by SLH algorithm. The values are listed below. Values 1–6 are tropicaltypes, and values 110–160 are midlatitude types. For no precipitation or Masked out pixels(rainTypeSLH=0, 900, or 910), SLH values are not estimated.
Table 3. Description for rainTypeSLH(a) Tropics and subtropics(b) Mid and higher latitudes

0: No precipitation

0: No precipitation

1: Convective

110: Convective

2: Shallow stratiform

121: Shallow stratiform

3: Deep stratiform

122: Deep stratiform, downward decreasing

4: Deep stratiform with low melting level

123: Deep stratiform, downward increasing

5: Intermediary124: Deep stratiform, subzero

6: Other160: Other
Mask

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900: Tibet etc.
910: Suspicious extreme

rainType2APR/rainType2ADPR (2-byte integer, size: nray x nscan)
Precipitation type copied from 2AKu. Missing value is −9999.
method (2-byte integer, size: nray x nscan)
Method copied from 2AKu. Missing value is −9999.
stormTopHeight (2-byte integer, size: nray x nscan)
For tropical types, height of storm top. For midlatitude types, precipitation-top height of thelowest precipitation layer. Unit is [m]. Missing value is −9999.
meltLayerHeight (2-byte integer, size: nray x nscan)
For tropical types, height of melting layer. All missing for midlatitude types. Unit is [m]. Missing
value is −9999.
nearSurfLevel (2-byte integer, size: nray x nscan)
For tropical types, height of the near-surface level. For midlatitude types, precipitation-bottom
height of the lowest precipitation layer. Unit is [m]. Missing value is −9999.
topoLevel (2-byte integer, size: nray x nscan)
Height of topography. Unit is [m]. Missing value is −9999.
climMeltLevel (2-byte integer, size: nray x nscan)
Climatological melting level. Unit is [m]. Missing value is −9999.
climFreezLevel (2-byte integer, size: nray x nscan)
For tropical types, climatological freezing level. All missing for midlatitude types. Unit is [m].
Missing value is −9999.
nearSurfPrecipRate (4-byte float, size: nray x nscan)
For tropical types, precipitation rate at the near-surface level. For midlatitude types, precipitation
rate at the bottom level of the lowest precipitation layer. Unit is [mm/h]. Missing value is −9999.9.
precipRateMeltLevel (4-byte float, size: nray x nscan)
For tropical types, precipitation rate at the melting level. All missing for midlatitude types. Unitis [mm/h]. Missing value is −9999.9.
precipRateClimFreezLevel (4-byte float, size: nray x nscan)
For tropical types, precipitation rate at the freezing level. All missing for midlatitude types. Unitis [mm/h]. Missing value is −9999.9.
2.3.

L3 products (LHG for TRMM and SLG for GPM)

2.3.1. Input data for LHG/SLG
LHG and SLG (orbit-based gridded) products are generated by using the following variables from
LHP/SLP products as input data:
Swath/Latitude
Swath/Longitude
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Swath/Q1minusQR
Swath/Q2
Swath/ScanTime/DayOfMonth
Swath/ScanTime/DayOfYear
Swath/ScanTime/Hour
Swath/ScanTime/MilliSecond
Swath/ScanTime/Minute
Swath/ScanTime/Month
Swath/ScanTime/Second
Swath/ScanTime/Year
Swath/latentHeating
Swath/rainType2APR (for TRMM)
Swath/rainType2ADPR (for GPM)
2.3.2. Output data for LHG/SLG
2.3.2.1. Dimension Definition
⚫

nlat
➢

⚫

nlon
➢

⚫

268 number of high resolution 0.5° grid intervals of latitude from 67°S to 67°N.
720 number of high resolution 0.5° grid intervals of longitude from 180°W to 180°E.

nlayer➢80 number of layers at the fixed heights of 0.00-0.25 km, 0.25-0.50 km, ..., 19.50-19.75 km, and19.75-20.00 km.

2.3.2.2. Output variables
GridTime/Year (2-byte integer, size: nscan)
4-digit year. Missing value is -9999.
GridTime/Month (1-byte integer, size: nscan)Month of the year. Missing value is -99.
GridTime/DayOfMonth (1-byte integer, size: nscan)Day of the month. Missing value is -99.
GridTime/Hour (1-byte integer, size: nscan)UTC hour of the day. Missing value is -99.
GridTime/Minute (1-byte integer, size: nscan)
Minute of the hour. Missing value is -99.
GridTime/Second (1-byte integer, size: nscan)
Second of the minute. Missing value is -99.
GridTime/MilliSecond (2-byte integer, size: nscan)Thousandths of the second. Missing value is -9999.
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GridTime/DayOfYear (2-byte integer, size: nscan)Day of the year. Missing value is -9999.
allLHCndMean (4-byte float, size: nlat x nlon x nlayer)
All Conditional mean of latent heating. Unit is [K/h]. Missing value is -9999.9.
allLHUnCndMean (4-byte float, size: nlat x nlon x nlayer)
All Unconditional mean of latent heating . Unit is [K/h]. Missing value is -9999.9.
allPix (2-byte integer, size: nlat x nlon x nlayer)
All pixel counts. Missing value is -9999.9.
allQ1RCndMean (4-byte float, size: nlat x nlon x nlayer)
All Conditional mean of Q1-QR. Unit is [K/h]. Missing value is -9999.9.
allQ1RUnCndMean (4-byte float, size: nlat x nlon x nlayer)
All Unconditional mean of Q1-QR. Unit is [K/h]. Missing value is -9999.9.
allQ2CndMean (4-byte float, size: nlat x nlon x nlayer)
All Conditional mean of Q2. Unit is [K/h]. Missing value is -9999.9.
allQ2UnCndMean (4-byte float, size: nlat x nlon x nlayer)
All Unconditional mean of Q2. Unit is [K/h]. Missing value is -9999.9.
convLHCndMean (4-byte float, size: nlat x nlon x nlayer)
Conditional mean of convective latent heating. Unit is [K/h]. Missing value is -9999.9.
convPix (2-byte integer, size: nlat x nlon x nlayer)
Convective pixel counts. Missing value is -9999.9.
convQ1RCndMean (4-byte float, size: nlat x nlon x nlayer)
Conditional mean of convective Q1-QR. Unit is [K/h]. Missing value is -9999.9.
convQ2CndMean (4-byte float, size: nlat x nlon x nlayer)
Conditional mean of convective Q2. Unit is [K/h]. Missing value is -9999.9.
dpstrLHCndMean (4-byte float, size: nlat x nlon x nlayer)
Conditional mean of stratiform latent heating. Unit is [K/h]. Missing value is -9999.9.
dpstrPix (2-byte integer, size: nlat x nlon x nlayer)
Stratiform pixel counts. Missing value is -9999.9.
dpstrQ1RCndMean (4-byte float, size: nlat x nlon x nlayer)
Conditional mean of stratiform Q1-QR. Unit is [K/h]. Missing value is -9999.9.
dpstrQ2CndMean (4-byte float, size: nlat x nlon x nlayer)
Conditional mean of stratiform Q2. Unit is [K/h]. Missing value is -9999.9.
otherLHCndMean (4-byte float, size: nlat x nlon x nlayer)
Conditional mean of other latent heating. Unit is [K/h]. Missing value is -9999.9.
otherPix (2-byte integer, size: nlat x nlon x nlayer)
Other pixel counts. Missing value is -9999.9.
otherQ1RCndMean (4-byte float, size: nlat x nlon x nlayer)
Conditional mean of other Q1-QR. Unit is [K/h]. Missing value is -9999.9.
otherQ2CndMean (4-byte float, size: nlat x nlon x nlayer)
Conditional mean of other Q2. Unit is [K/h]. Missing value is -9999.9.
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ATBD for TRMM/GPM SLH
precipPix (2-byte integer, size: nlat x nlon x nlayer)
Precipitation pixel counts. Missing value is -9999.9.
shstrLHCndMean (4-byte float, size: nlat x nlon x nlayer)
Conditional mean of shallow-stratiform latent heating. Unit is [K/h]. Missing value is -9999.9.
shstrPix (2-byte integer, size: nlat x nlon x nlayer)
Shallow-stratiform pixel counts. Missing value is -9999.9.
shstrQ1RCndMean (4-byte float, size: nlat x nlon x nlayer)
Conditional mean of shallow-stratiform Q1-QR. Unit is [K/h]. Missing value is -9999.9.
shstrQ2CndMean (4-byte float, size: nlat x nlon x nlayer)
Conditional mean of shallow-stratiform Q2. Unit is [K/h]. Missing value is -9999.9.
2.4.

L3 products (LHM for TRMM and SLM for GPM)

2.4.1. Input variables for LHM/SLM
LHM and SLM (Monthly) products are generated by using the following variables from LHD/SLD(daily) products as input data:
Grid/LHCndMean
Grid/LHCndStdv
Grid/Q1RCndMean
Grid/Q1RCndStdv
Grid/Q2CndMean
Grid/Q2CndStdv
Grid/allPix
Grid/convLHCndMean
Grid/convLHCndStdv
Grid/convPix
Grid/convQ1RCndMean
Grid/convQ1RCndStdv
Grid/convQ2CndMean
Grid/convQ2CndStdv
Grid/dpstrLHCndMean
Grid/dpstrLHCndStdv
Grid/dpstrPix
Grid/dpstrQ1RCndMean
Grid/dpstrQ1RCndStdv
Grid/dpstrQ2CndMean
Grid/dpstrQ2CndStdv
Grid/otherLHCndMean
Grid/otherLHCndStdv
Grid/otherPix
15

ATBD for TRMM/GPM SLH
Grid/otherQ1RCndMean
Grid/otherQ1RCndStdv
Grid/otherQ2CndMean
Grid/otherQ2CndStdv
Grid/precipPix
Grid/shstrLHCndMean
Grid/shstrLHCndStdv
Grid/shstrPix
Grid/shstrQ1RCndMean
Grid/shstrQ1RCndStdv
Grid/shstrQ2CndMean
Grid/shstrQ2CndStdv
2.4.2. Output data for LHM/SLM
2.4.2.1. Dimension Definition
⚫

nlat
➢

⚫

nlon
➢

⚫

268 number of high resolution 0.5° grid intervals of latitude from 67°S to 67°N.
720 number of high resolution 0.5° grid intervals of longitude from 180°W to 180°E.

nlayer➢80 number of layers at the fixed heights of 0.00-0.25 km, 0.25-0.50 km, ..., 19.50-19.75 km,
and 19.75-20.00 km.

2.4.2.2. Output variables
LHCndMean (4-byte float, size: nlat x nlon x nlayer)
Conditional mean of latent heating. Unit is [K/h]. Missing value is -9999.9.
LHCndStdv (4-byte float, size: nlat x nlon x nlayer)
Conditional standard deviation of latent heating. Unit is [K/h]. Missing value is -9999.9.
LHUnCndMean (4-byte float, size: nlat x nlon x nlayer)
Unconditional mean of latent heating. Unit is [K/h]. Missing value is -9999.9.
LHUnCndStdv (4-byte float, size: nlat x nlon x nlayer)
Unconditional standard deviation of latent heating. Unit is [K/h]. Missing value is -9999.9.
Q1RCndMean (4-byte float, size: nlat x nlon x nlayer)
Conditional mean of Q1-QR. Unit is [K/h]. Missing value is -9999.9.
Q1RCndStdv (4-byte float, size: nlat x nlon x nlayer)
Conditional standard deviation of Q1-QR. Unit is [K/h]. Missing value is -9999.9.
Q1RUnCndMean (4-byte float, size: nlat x nlon x nlayer)
Unconditional mean of Q1-QR. Unit is [K/h]. Missing value is -9999.9.
Q1RUnCndStdv (4-byte float, size: nlat x nlon x nlayer)
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ATBD for TRMM/GPM SLH
Unconditional standard deviation of Q1-QR. Unit is [K/h]. Missing value is -9999.9.
Q2CndMean (4-byte float, size: nlat x nlon x nlayer)
Conditional mean of Q2. Unit is [K/h]. Missing value is -9999.9.
Q2CndStdv (4-byte float, size: nlat x nlon x nlayer)
Conditional standard deviation of Q2. Unit is [K/h]. Missing value is -9999.9.
Q2UnCndMean (4-byte float, size: nlat x nlon x nlayer)
Unconditional mean of Q2. Unit is [K/h]. Missing value is -9999.9.
Q2UnCndStdv (4-byte float, size: nlat x nlon x nlayer)
Unconditional standard deviation of Q2. Unit is [K/h]. Missing value is -9999.9.
allPix (4-byte float, size: nlat x nlon x nlayer)
All pixel counts. Missing value is -9999.9.
convLHCndMean (4-byte float, size: nlat x nlon x nlayer)
Conditional mean of convective latent heating. Unit is [K/h]. Missing value is -9999.9.
convLHCndStdv (4-byte float, size: nlat x nlon x nlayer)
Conditional standard deviation of convective latent heating. Unit is [K/h]. Missing value is 9999.9.
convPix (4-byte float, size: nlat x nlon x nlayer)
Convective pixel counts. Missing value is -9999.9.
convQ1RCndMean (4-byte float, size: nlat x nlon x nlayer)
Conditional mean of convective Q1-QR. Unit is [K/h]. Missing value is -9999.9.
convQ1RCndStdv (4-byte float, size: nlat x nlon x nlayer)
Conditional standard deviation of convective Q1-QR. Unit is [K/h]. Missing value is -9999.9.
convQ2CndMean (4-byte float, size: nlat x nlon x nlayer)
Conditional mean of convective Q2. Unit is [K/h]. Missing value is -9999.9.
convQ2CndStdv (4-byte float, size: nlat x nlon x nlayer)
Conditional standard deviation of convective Q2. Unit is [K/h]. Missing value is -9999.9.
dpstrLHCndMean (4-byte float, size: nlat x nlon x nlayer)
Conditional mean of stratiform latent heating. Unit is [K/h]. Missing value is -9999.9.
dpstrLHCndStdv (4-byte float, size: nlat x nlon x nlayer)
Conditional standard deviation of stratiform latent heating. Unit is [K/h]. Missing value is -9999.9.
dpstrPix (4-byte float, size: nlat x nlon x nlayer)
Stratiform pixel counts. Missing value is -9999.9.
dpstrQ1RCndMean (4-byte float, size: nlat x nlon x nlayer)
Conditional mean of stratiform Q1-QR. Unit is [K/h]. Missing value is -9999.9.
dpstrQ1RCndStdv (4-byte float, size: nlat x nlon x nlayer)
Conditional standard deviation of stratiform Q1-QR. Unit is [K/h]. Missing value is -9999.9.
dpstrQ2CndMean (4-byte float, size: nlat x nlon x nlayer)
Conditional mean of stratiform Q2. Unit is [K/h]. Missing value is -9999.9.
dpstrQ2CndStdv (4-byte float, size: nlat x nlon x nlayer)
Conditional standard deviation of stratiform Q2. Unit is [K/h]. Missing value is -9999.9.
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ATBD for TRMM/GPM SLH
otherLHCndMean (4-byte float, size: nlat x nlon x nlayer)
Conditional mean of other latent heating. Unit is [K/h]. Missing value is -9999.9.
otherLHCndStdv (4-byte float, size: nlat x nlon x nlayer)
Conditional standard deviation of other latent heating. Unit is [K/h]. Missing value is -9999.9.
otherPix (4-byte float, size: nlat x nlon x nlayer)
Other pixel counts. Missing value is -9999.9.
otherQ1RCndMean (4-byte float, size: nlat x nlon x nlayer)
Conditional mean of other Q1-QR. Unit is [K/h]. Missing value is -9999.9.
otherQ1RCndStdv (4-byte float, size: nlat x nlon x nlayer)
Conditional standard deviation of other Q1-QR. Unit is [K/h]. Missing value is -9999.9.
otherQ2CndMean (4-byte float, size: nlat x nlon x nlayer)
Conditional mean of other Q2. Unit is [K/h]. Missing value is -9999.9.
otherQ2CndStdv (4-byte float, size: nlat x nlon x nlayer)
Conditional standard deviation of other Q2. Unit is [K/h]. Missing value is -9999.9.
shstrLHCndMean (4-byte float, size: nlat x nlon x nlayer)
Conditional mean of shallow-stratiform latent heating. Unit is [K/h]. Missing value is -9999.9.
shstrLHCndStdv (4-byte float, size: nlat x nlon x nlayer)
Conditional standard deviation of shallow-stratiform latent heating. Unit is [K/h]. Missing value
is -9999.9.
shstrPix (4-byte float, size: nlat x nlon x nlayer)
Shallow-stratiform pixel counts. Missing value is -9999.9.
shstrQ1RCndMean (4-byte float, size: nlat x nlon x nlayer)
Conditional mean of shallow-stratiform Q1-QR. Unit is [K/h]. Missing value is -9999.9.
shstrQ1RCndStdv (4-byte float, size: nlat x nlon x nlayer)
Conditional standard deviation of Q1-QR. Unit is [K/h]. Missing value is -9999.9.
shstrQ2CndMean (4-byte float, size: nlat x nlon x nlayer)
Conditional mean of shallow-stratiform Q2. Unit is [K/h]. Missing value is -9999.9.
shstrQ2CndStdv (4-byte float, size: nlat x nlon x nlayer)
Conditional standard deviation of shallow-stratiform Q2. Unit is [K/h]. Missing value is -9999.9.
3.

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J. Atmos. Sci., 30, 611-627.